System for Public Access to Research Knowledge (SPARK)
The SPARK tool is designed to increase access to and visibility of research projects funded by the Weather Program Office (WPO). By providing public access to project-level information, including topics, hazards, and related research outputs, SPARK supports WPO’s open science initiative, helping users discover, understand, and apply research knowledge across the Weather Enterprise.
Explore WPO‑funded projects now. Filter by hazard, keyword, or other criteria—and then click any project row in the table below to view full details.
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Project Title
PI Last Name
Managing WPO Program
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Year Funded
REACH URLs
Level
Project Goal
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Project Status
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Project End Date
Research Keywords
Primary
Associated Forecast Systems & Models
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Truncated Project Goal
REACH Output #
Improving probabilistic forecasts of extreme rainfall through intelligent processing of high-resolution ensemble predictions
Schumacher
JTTI
check_circle
2016
Level 3
This project aimed to use machine-learning techniques to optimize the skill and reliability of probabilistic forecasts of extreme rainfall. The output is used as a first-guess for the Weather Prediction Center (WPC) Day 2-3 Extreme Rainfall Outlook.
Russ Schumacher
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting, Post-processing, Probabilistic Weather Forecasting
JTTI
A framework for evaluating extreme QPFs in models
more robust and definitive conclusions about overall model performance at forecasting extreme precipitation
WPC
Jim Nelson
Colorado State University
NA16OAR4590238
Water Extremes
Colorado
Joint Technology Transfer Initiative (JTTI)
October 2016 - September 2019
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting, Post-processing, Probabilistic Weather Forecasting
improving probabilistic forecasts of extreme rainfall through intelligent processing of high resolution ensemble predictions, this project aimed to use machine learning techniques to optimize the skill and reliability of probabilistic forecasts of extreme rainfall the output is used as a first guess for the weather prediction center wpc day 2 3 extreme rainfall outlook, a framework for evaluating extreme qpfs in models
Weather Prediction Center (WPC)
Colorado State University
This project aimed to use machine-learning techniques to optimize the skill and reliability of probabilistic forecasts of extreme rainfall. The output is used as a first-guess for the Weather Prediction Center (WPC) Day 2-3 Extreme
6
Seo
JTTI
check_circle
2016
Level 3
The objective of this project is to implement new algorithms for multisensor quantitative precipitation estimation (QPE) and multi-QPE fusion on the Multi-Radar Multi-Sensor (MRMS) Testbed for eventual operation at the National Centers for Environmental Prediction (NCEP) Central Operations (NCO). The new algorithms are expected to improve the accuracy of high-resolution QPE and water prediction especially for extreme precipitation and flooding events.
Dong-Jun Seo, Lin Tang
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
This project aims at addressing the problems in the current high-resolution QPE by implementing new algorithms for multisensor QPE and multi-QPE fusion on the Multi-Radar Multi-Sensor (MRMS) Testbed
Improved accuracy in water prediction, especially for high-impact hydrologic events such as severe-to-extreme flooding
NCO
David Kitzmiller (OWP/NWC)
University of Texas at Arlington
CIMMS/University of Oklahoma
NOAA/OAR/NSSL
NOAA/NWS/NWC
NA16OAR4590232
NA16OAR4590235
Water Extremes
Oklahoma, Texas
Joint Technology Transfer Initiative (JTTI)
October 2016 - September 2019
Dynamics and Nesting, Radar
the objective of this project is to implement new algorithms for multisensor quantitative precipitation estimation qpe and multi qpe fusion on the multi radar multi sensor mrms testbed for eventual operation at the national centers for environmental prediction ncep central operations nco the new algorithms are expected to improve the accuracy of high resolution qpe and water prediction especially for extreme precipitation and flooding events, this project aims at addressing the problems in the current high resolution qpe by implementing new algorithms for multisensor qpe and multi qpe fusion on the multi radar multi sensor mrms testbed
NCEP Central Operations (NCO)
University of Texas at Arlington, CIMMS/University of Oklahoma, NOAA/OAR/NSSL, NOAA/NWS/NWC
The objective of this project is to implement new algorithms for multisensor quantitative precipitation estimation (QPE) and multi-QPE fusion on the Multi-Radar Multi-Sensor (MRMS) Testbed for eventual operation at the National Centers for Environmental Prediction
7
Assessing the impact of assimilating ground-based infrared radiometer data into convective-scale numerical weather prediction models
Wagner
JTTI
check_circle
2016
Level 3
The goal for this project was to transition the network of four Atmospheric Emitted Radiance Interferometers (AERIs) operating at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in north-central Oklahoma into operational NWP ensembles for the improvement in prediction of localized convection.
Timothy Wagner
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Mar 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Satellite & Remote Sensing Technologies
JTTI
Unified Forecast System (UFS)
A project to use increased information about boundary layer structure and its evolution to create improved representation of convective modes in the model and more skillful quantitative precipitation forecasts (QPF).
A real-time assimilation scheme for the AERIs in north central Oklahoma that results in more skillful quantitative precipitation forecasts (QPF)
SPC
Russell Schneider
CIMSS/University of Wisconsin
NA16OAR4590241
Water Extremes
Wisconsin
Joint Technology Transfer Initiative (JTTI)
October 2016 - March 2020
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Satellite & Remote Sensing Technologies
assessing the impact of assimilating ground based infrared radiometer data into convective scale numerical weather prediction models, the goal for this project was to transition the network of four atmospheric emitted radiance interferometers aeris operating at the department of energy atmospheric radiation measurement arm southern great plains sgp facility in north central oklahoma into operational nwp ensembles for the improvement in prediction of localized convection, a project to use increased information about boundary layer structure and its evolution to create improved representation of convective modes in the model and more skillful quantitative precipitation forecasts qpf
Storm Prediction Center (SPC)
CIMSS/University of Wisconsin
The goal for this project was to transition the network of four Atmospheric Emitted Radiance Interferometers (AERIs) operating at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in north-central Oklahoma
1
Development and Implementation of Probabilistic Hail Forecast Products using Multi-Moment Microphysics and Machine Learning Algorithms
Snook
JTTI
check_circle
2016
Level 3
This project aimed to develop machine learning hail prediction algorithms and direct hail prediction products suitable for severe weather forecast operations.
Nathan Snook
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
development and implementation of probabilistic hail forecast products using multi moment microphysics and machine learning algorithms, this project aimed to develop machine learning hail prediction algorithms and direct hail prediction products suitable for severe weather forecast operations, to refine hail forecast products pioneered during sharp and prepare them for operational use
Environmental Modeling Center (EMC)
University of Oklahoma
This project aimed to develop machine learning hail prediction algorithms and direct hail prediction products suitable for severe weather forecast operations.
2
Improving Hydrologic Observing Capabilities with Stream Radars
Wasielewski
JTTI
check_circle
2016
Level 3
This project proposed to advance the state of observations of the water cycle across the U.S. with the siting and installation of 14 Stream Radars and the transfer of their real-time data into the National Water Model (NWM).
Daniel Wasielewski, Humberto Vergera-Arrieta
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
This proposal will install 14 stream radars on cables or bridges across rivers at the pre-determined, high-priority locations
Advance the state of observations of the water cycle across the U.S. in order to improve hydrologic forecasting with the new National Water Model (NWM).
OWP/NWC
Edward Clark
CIMMS/University of Oklahoma
NA16OAR4590234
Water Extremes
Oklahoma
Joint Technology Transfer Initiative (JTTI)
October 2016 - September 2019
Radar
improving hydrologic observing capabilities with stream radars, this project proposed to advance the state of observations of the water cycle across the u s with the siting and installation of 14 stream radars and the transfer of their real time data into the national water model nwm, this proposal will install 14 stream radars on cables or bridges across rivers at the pre determined high priority locations
CIMMS/University of Oklahoma
This project proposed to advance the state of observations of the water cycle across the U.S. with the siting and installation of 14 Stream Radars and the transfer of their real-time data into the National
2
Quantifying Stochastic Forcing at Convective Scales
Randall
JTTI
check_circle
2016
Level 3
This project aims to provide NCEP's Environmental Modeling Center (EMC) with a modeling tool called MP-NGGPS, which can generate initial conditions for ensemble members that differ partially or even exclusively through stochastic perturbations of the parameterized cloud systems.
David Randall
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles
JTTI
Next Generation Global Prediction System (NGGPS), Unified Forecast System (UFS)
This research is aimed at improving modeling capabilities at the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP), with emphasis on the prediction of convective weather systems and the effects of such systems on broader-scale forecasts
Provide EMC with a modeling tool that can be used to generate initial conditions for ensemble members that differ partially or exclusively through stochastic perturbations of the parameterized cloud systems
EMC
Vijay Tallapragada
Colorado State University
NA16OAR4590230
Severe Weather
Colorado
Joint Technology Transfer Initiative (JTTI)
October 2016 - September 2019
Atmospheric Physics, Data Assimilation and Ensembles
quantifying stochastic forcing at convective scales, this project aims to provide ncep's environmental modeling center emc with a modeling tool called mp nggps which can generate initial conditions for ensemble members that differ partially or even exclusively through stochastic perturbations of the parameterized cloud systems, this research is aimed at improving modeling capabilities at the environmental modeling center emc of the national centers for environmental prediction ncep with emphasis on the prediction of convective weather systems and the effects of such systems on broader scale forecasts
Environmental Modeling Center (EMC)
Colorado State University
This project aims to provide NCEP's Environmental Modeling Center (EMC) with a modeling tool called MP-NGGPS, which can generate initial conditions for ensemble members that differ partially or even exclusively through stochastic perturbations of the
2
Development of NWS convective scale ensemble forecasting capability through improving GSI-based hybrid ensemble-variational data assimilation and evaluating the multi-dynamic core approach
Wang
JTTI
check_circle
2016
Level 3
This proposal aimed to extend the GSI-based EnKF/hybrid DA system within the HRRR to directly assimilate multi-scale observations including the radar reflectivity. The impact of direct assimilation of reflectivity observations and the impact of using various error covariance combinations in hybrid DA for the convection allowing forecasts was also evaluated.
Xuguang Wang
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
The GSI-based EnKF/hybrid DA system will be extended with HRRR to directly assimilate multiscale observations including the radar reflectivity.
The capability to assimilate radar reflectivity into the HRRR
EMC
Jacob Carley
University of Oklahoma
NA16OAR4590236
Relevant to All Hazards
Oklahoma
Joint Technology Transfer Initiative (JTTI)
October 2016 - September 2020
Data Assimilation and Ensembles, Dynamics and Nesting, Radar
development of nws convective scale ensemble forecasting capability through improving gsi based hybrid ensemble variational data assimilation and evaluating the multi dynamic core approach, this proposal aimed to extend the gsi based enkf hybrid da system within the hrrr to directly assimilate multi scale observations including the radar reflectivity the impact of direct assimilation of reflectivity observations and the impact of using various error covariance combinations in hybrid da for the convection allowing forecasts was also evaluated, the gsi based enkf hybrid da system will be extended with hrrr to directly assimilate multiscale observations including the radar reflectivity
Environmental Modeling Center (EMC)
University of Oklahoma
This proposal aimed to extend the GSI-based EnKF/hybrid DA system within the HRRR to directly assimilate multi-scale observations including the radar reflectivity. The impact of direct assimilation of reflectivity observations and the impact of using
6
Accounting for non-Gaussianity in the background error distributions associated with cloud-related variables (microwave radiances and hydrometeors) in hybrid data assimilation for convective-scale prediction
Apodaca
JTTI
check_circle
2016
Level 3
This project sought to further develop the operationally used Gridpoint Statistical Interpolation (GSI) data assimilation (DA) software by incorporating a new non-Gaussian solver in the static (3DVar) component of the hybrid data assimilation system within the RAP/HRRR model.
Karina Apodaca
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Mar 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting
To augment the capabilities of the GSI-hybrid system for the RAP+HRRR models to account for the non-Gaussianity in the background error distributions of microwave radiances and cloud hydrometeors to improve the prediction of severe weather events that occur at the convective scale.
Improved prediction of severe weather events that occur at the convective scale.
EMC
Emily Liu
Colorado State University
NA16OAR4590233
Severe Weather
Colorado
Joint Technology Transfer Initiative (JTTI)
October 2016 - March 2020
Data Assimilation and Ensembles, Dynamics and Nesting
accounting for non gaussianity in the background error distributions associated with cloud related variables microwave radiances and hydrometeors in hybrid data assimilation for convective scale prediction, this project sought to further develop the operationally used gridpoint statistical interpolation gsi data assimilation da software by incorporating a new non gaussian solver in the static 3dvar component of the hybrid data assimilation system within the rap hrrr model, to augment the capabilities of the gsi hybrid system for the rap+hrrr models to account for the non gaussianity in the background error distributions of microwave radiances and cloud hydrometeors to improve the prediction of severe weather events that occur at the convective scale
Environmental Modeling Center (EMC)
Colorado State University
This project sought to further develop the operationally used Gridpoint Statistical Interpolation (GSI) data assimilation (DA) software by incorporating a new non-Gaussian solver in the static (3DVar) component of the hybrid data assimilation system within
0
Demonstration of an Airborne Hyperspectral and Thermal Imaging System to Assess Convective Lifted Index
Brooks
JTTI
check_circle
2016
Level 3
This project aimed to adapt hyperspectral and thermal imaging instruments on a University of Tennessee airplane to fly on a drone. The drone would use these instruments to assess the Lifted Index (LI) of near surface air as a precursor to deep convection.
Steven Brooks
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will utilize the existing hyperspectral and thermal imaging instruments on a University of Tennessee aircraft, to assess the Lifted Index (LI) of near surface air as a precursor to deep convection.
Hyperspectral and thermal imaging datasets to complement the micromet tower net-radiation and, sensible and latent heat fluxes, a trained PhD graduate prepared for a NOAA post-doc, an operational drone with hyperspectral, thermal and met. sensors delivered to NOAA, and datasets to supplement severe storms models.
demonstration of an airborne hyperspectral and thermal imaging system to assess convective lifted index, this project aimed to adapt hyperspectral and thermal imaging instruments on a university of tennessee airplane to fly on a drone the drone would use these instruments to assess the lifted index li of near surface air as a precursor to deep convection, this project will utilize the existing hyperspectral and thermal imaging instruments on a university of tennessee aircraft to assess the lifted index li of near surface air as a precursor to deep convection
University of Tennessee
This project aimed to adapt hyperspectral and thermal imaging instruments on a University of Tennessee airplane to fly on a drone. The drone would use these instruments to assess the Lifted Index (LI) of near
0
Assimilation of Remote Sensing Observations into Convective-scale NWP to Improve 0-6 h Probabilistic Forecasts of High Impact Weather
Yussouf
JTTI
check_circle
2016
Level 3
The goal of this project was to improve short-term (0-6 h) probabilistic forecasts of extreme convective rainfall and other hazardous severe weather threats by enhancing the capability of a data assimilation system to incorporate high-temporal resolution thermodynamic and wind observations from ground-based remote sensing boundary layer instruments in addition to NWS conventional and WSR-88D radar observations.
Nusrat Yussouf
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Numerical Weather Prediction (NWP), Probabilistic Weather Forecasting, Radar, Satellite & Remote Sensing Technologies
JTTI
The proposed work aims to improve 0-6 h convective-scale (3 km or less) Quantitative Precipitation Forecasts (QPF and probabilistic QPF) and other high impact weather events (e.g., flash floods, tornadoes, large hail, damaging winds).
Increase the forecast warning lead times of flood producing heavy rainfall events from improved representations of the near storm environment and storm characteristics in the convective-scale model
EMC
WPC
Daryl Kleist (EMC)
Gregory Carbin (POC)
Jim Nelson (WPC)
CIMMS/University of Oklahoma
NA16OAR4590242
"Severe Weather, Water Extremes"
Oklahoma
Joint Technology Transfer Initiative (JTTI)
October 2016 - September 2019
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Numerical Weather Prediction (NWP), Probabilistic Weather Forecasting, Radar, Satellite & Remote Sensing Technologies
assimilation of remote sensing observations into convective scale nwp to improve 0 6 h probabilistic forecasts of high impact weather, the goal of this project was to improve short term 0 6 h probabilistic forecasts of extreme convective rainfall and other hazardous severe weather threats by enhancing the capability of a data assimilation system to incorporate high temporal resolution thermodynamic and wind observations from ground based remote sensing boundary layer instruments in addition to nws conventional and wsr 88d radar observations, the proposed work aims to improve 0 6 h convective scale 3 km or less quantitative precipitation forecasts qpf and probabilistic qpf and other high impact weather events e g flash floods tornadoes large hail damaging winds
CIMMS/University of Oklahoma
The goal of this project was to improve short-term (0-6 h) probabilistic forecasts of extreme convective rainfall and other hazardous severe weather threats by enhancing the capability of a data assimilation system to incorporate high-temporal
3
Assimilation of Lake and Reservoir Levels into the WRF-Hydro National Water Model to Improve Operational Hydrologic Predictions
Gochis
JTTI
check_circle
2016
Level 3
This project aimed to incorporate additional lake and reservoir attribute information and physical process representation to improve reservoir simulation within the National Water Model (NWM). A reservoir level assimilation scheme was also incorporated into the NWM.
David Gochis, Allen Burton, Lynn Johnson
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles
JTTI
National Water Model (NWM), Weather Research and Forecasting Model (WRF), Weather Research and Forecasting Model - Hydro (WRF-Hydro)
This project will improve the representation of reservoir mass balance and storage in the operational NOAA National Water Model (NWM) through the implementation and evaluation of a new reservoir level data assimilation scheme and new reservoir process representations.
Contribute future system upgrades as model representations are improved through the incorporation of additional real-time observations and improved process descriptions
NWC
Edward Clark
University Corporation for Atmospheric Research
Great Lakes Environmental Research Laboratory (GLERL)
CIRA/Colorado State University
NA16OAR4590229
NA16OAR4590240
NA16OAR4590237
Water Extremes
Colorado, Michigan
Joint Technology Transfer Initiative (JTTI)
October 2016 - September 2019
Atmospheric Physics, Data Assimilation and Ensembles
assimilation of lake and reservoir levels into the wrf hydro national water model to improve operational hydrologic predictions, this project aimed to incorporate additional lake and reservoir attribute information and physical process representation to improve reservoir simulation within the national water model nwm a reservoir level assimilation scheme was also incorporated into the nwm, this project will improve the representation of reservoir mass balance and storage in the operational noaa national water model nwm through the implementation and evaluation of a new reservoir level data assimilation scheme and new reservoir process representations
National Water Center (NWC)
University Corporation for Atmospheric Research, Great Lakes Environmental Research Laboratory (GLERL), CIRA/Colorado State University
This project aimed to incorporate additional lake and reservoir attribute information and physical process representation to improve reservoir simulation within the National Water Model (NWM). A reservoir level assimilation scheme was also incorporated into the
1
Operationalizing an Evaporative Demand Drought Index (EDDI) service for drought monitoring and early warning across CONUS
Webb
JTTI
check_circle
2016
Level 3
This project used a fuller representation of evaporative demand to provide an improved decision support tool to end-users for extreme heat and drought conditions. This tool went beyond the standard precipitation and temperature-based evporative demand to account for soil drying induced by solar radiation, humidity, or wind.
Sun Jun 30 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Decision Support
JTTI
Transition the production and dissemination of the Evaporative Demand Drought Index from the originating unit (ESRL-PSD) to the National Weather Service (NWS, the receiving unit) at the National Water Center (NWC) for operational dissemination.
Develop an automated system at ESRL-PSD to meet the needs of users affected by agricultural and hydrologic droughts and fire weather
CPC
UNK
NOAA/ESRL/PSL
OWAQ16-JTTI-I-1
Water Extremes
Colorado
Joint Technology Transfer Initiative (JTTI)
July 2016 - June 2019
Decision Support
operationalizing an evaporative demand drought index eddi service for drought monitoring and early warning across conus, this project used a fuller representation of evaporative demand to provide an improved decision support tool to end users for extreme heat and drought conditions this tool went beyond the standard precipitation and temperature based evporative demand to account for soil drying induced by solar radiation humidity or wind, transition the production and dissemination of the evaporative demand drought index from the originating unit esrl psd to the national weather service nws the receiving unit at the national water center nwc for operational dissemination
Climate Prediction Center (CPC)
NOAA/ESRL/PSL
This project used a fuller representation of evaporative demand to provide an improved decision support tool to end-users for extreme heat and drought conditions. This tool went beyond the standard precipitation and temperature-based evporative demand
0
Upgrades and Improvements to MRMS
Howard
JTTI
check_circle
2016
Level 3
The primary intent of this project was to expand and enhance MRMS product generation, increase the spatial resolution of select MRMS products, incorporate additional observational systems, and use dual polarization radar moments and international gap filling radars. Some of these capabilties were not installed as part of the original MRMS operational implementation ini 2014.
Kenneth Howard, Jennifer Guillot
Complete
Mon Aug 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Radar
JTTI
Multi-Radar/Multi-Sensor System (MRMS)
Expand and enhance product generation, increase the spatial resolution of select MRMS products, incorporate additional observational sensing systems, and infuse the state of science applications which utilize dual polarization radar moments and international gap filling radars.
An accelerated expansion of MRMS operational capabilities and product generation across all NWS operational areas of responsibility.
NCO
UNK
NOAA/OAR/NSSL
OWAQ16-JTTI-I-2
Relevant to All Hazards
Oklahoma
Joint Technology Transfer Initiative (JTTI)
August 2016 - July 2019
Data Assimilation and Ensembles, Dynamics and Nesting, Radar
upgrades and improvements to mrms, the primary intent of this project was to expand and enhance mrms product generation increase the spatial resolution of select mrms products incorporate additional observational systems and use dual polarization radar moments and international gap filling radars some of these capabilties were not installed as part of the original mrms operational implementation ini 2014, expand and enhance product generation increase the spatial resolution of select mrms products incorporate additional observational sensing systems and infuse the state of science applications which utilize dual polarization radar moments and international gap filling radars
NCEP Central Operations (NCO)
NOAA/OAR/NSSL
The primary intent of this project was to expand and enhance MRMS product generation, increase the spatial resolution of select MRMS products, incorporate additional observational systems, and use dual polarization radar moments and international gap
0
Probabilistic precipitation rate estimates from ground-radar for hydrology
Kirstetter
JTTI
check_circle
2017
Level 3
The goal of this project is the evaluation and refinements of a real-time, high-resolution radar probabilistic precipitation algorithm within MRMS for operations.
Pierre-Emmanuel Kirstetter
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Probabilistic Weather Forecasting, Radar
JTTI
Multi-Radar/Multi-Sensor System (MRMS)
This project focuses on the evaluation and refinements of a real-time, high-resolution radar probabilistic precipitation algorithm within MRMS for operations. The MRMS inputs for the algorithm are operational. The project will refine the model by testing additional factors that influence the probabilistic QPE.
Improved quantitative precipitation estimation though PQPE
NCO
Yuqiong Liu
University of Oklahoma
NA17OAR4590170
Water Extremes
Oklahoma
Joint Technology Transfer Initiative (JTTI)
August 2017 - July 2020
Dynamics and Nesting, Probabilistic Weather Forecasting, Radar
probabilistic precipitation rate estimates from ground radar for hydrology, the goal of this project is the evaluation and refinements of a real time high resolution radar probabilistic precipitation algorithm within mrms for operations, this project focuses on the evaluation and refinements of a real time high resolution radar probabilistic precipitation algorithm within mrms for operations the mrms inputs for the algorithm are operational the project will refine the model by testing additional factors that influence the probabilistic qpe
NCEP Central Operations (NCO)
University of Oklahoma
The goal of this project is the evaluation and refinements of a real-time, high-resolution radar probabilistic precipitation algorithm within MRMS for operations.
2
Assessing the Impact of Stochastic Cloud Microphysics in Convection-Resolving Models Using GOES-R Satellite Observations
Otkin
JTTI
check_circle
2017
Level 3
The goal of this project is to implement a stochastic parameter perturbation (SPP) technique in the Thompson-Eidhammer cloud microphysics parameterization scheme that will add perturbations to key microphysical process rates in a statistically and physically consistent manner.
Jason Otkin, Gregory Thompson, Fanyou Kong
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Common Community Physics Package (CCPP), Unified Forecast System (UFS)
To implement a stochastic parameter perturbation (SPP) technique in the Thompson-Eidhammer cloud microphysics parameterization scheme that will add perturbations to key microphysical process rates in a statistically and physically consistent manner. In particular, this proposal will perform sensitivity experiments in which perturbations are added separately to the shape parameter of the gamma distribution describing the cloud droplet number spectra and to the gridresolved vertical velocities that control the activation of cloud and ice condensation nuclei.
The high spatial and temporal resolution of the ABI will promote a detailed analysis of the forecast accuracy while the availability of multiple spectral channels will allow for better discrimination of cloud phase and an evaluation of the water vapor distribution in different atmospheric layers.
EMC
Jason Levit
CIMSS/University of Wisconsin
National Center for Atmospheric Research
University of Oklahoma
assessing the impact of stochastic cloud microphysics in convection resolving models using goes r satellite observations, the goal of this project is to implement a stochastic parameter perturbation spp technique in the thompson eidhammer cloud microphysics parameterization scheme that will add perturbations to key microphysical process rates in a statistically and physically consistent manner, to implement a stochastic parameter perturbation spp technique in the thompson eidhammer cloud microphysics parameterization scheme that will add perturbations to key microphysical process rates in a statistically and physically consistent manner in particular this proposal will perform sensitivity experiments in which perturbations are added separately to the shape parameter of the gamma distribution describing the cloud droplet number spectra and to the gridresolved vertical velocities that control the activation of cloud and ice condensation nuclei
Environmental Modeling Center (EMC)
CIMSS/University of Wisconsin, National Center for Atmospheric Research, University of Oklahoma
The goal of this project is to implement a stochastic parameter perturbation (SPP) technique in the Thompson-Eidhammer cloud microphysics parameterization scheme that will add perturbations to key microphysical process rates in a statistically and physically
3
Implementation of streamflow data assimilator for the National Water Model to improve water prediction and analysis
Noh
JTTI
check_circle
2017
Level 3
The purpose of this project is to implement a real-time ensemble streamflow data assimilator for the National Water Model (NWM) which will will: 1) assimilate real-time streamflow observations into the channel routing model of the NWM to produce more accurate streamflow analyses and predictions, and 2) provide flow-dependent diagnostics for the fluxes from the land surface, surface and subsurface routing models, and the conceptual ground water model which all feed the channel routing model.
Seongjin Noh, James McCreight
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Verification & Validation
JTTI
National Water Model (NWM)
To implement an ensemble data assimilation (DA) module for the streamflow routing model of the NWM and demonstrate the new capability for real-time operation. An upgrade to the current deterministic DA scheme, the module will bring about step-change improvement in the quality of streamflow analysis and prediction, and provide model diagnostics for biases from the upstream modeling chain in the NWM.
Step-change improvement in the quality of streamflow analysis and prediction, and provide model diagnostics for biases from the upstream modeling chain in the NWM.
NWC
Michael Smith
University of Texas-Arlington
National Center for Atmospheric Research
NA17OAR4590174
NA17OAR4590176
Water Extremes
Colorado, Texas
Joint Technology Transfer Initiative (JTTI)
August 2017 - July 2019
Data Assimilation and Ensembles, Verification & Validation
implementation of streamflow data assimilator for the national water model to improve water prediction and analysis, the purpose of this project is to implement a real time ensemble streamflow data assimilator for the national water model nwm which will will 1 assimilate real time streamflow observations into the channel routing model of the nwm to produce more accurate streamflow analyses and predictions and 2 provide flow dependent diagnostics for the fluxes from the land surface surface and subsurface routing models and the conceptual ground water model which all feed the channel routing model, to implement an ensemble data assimilation da module for the streamflow routing model of the nwm and demonstrate the new capability for real time operation an upgrade to the current deterministic da scheme the module will bring about step change improvement in the quality of streamflow analysis and prediction and provide model diagnostics for biases from the upstream modeling chain in the nwm
National Water Center (NWC)
University of Texas-Arlington, National Center for Atmospheric Research
The purpose of this project is to implement a real-time ensemble streamflow data assimilator for the National Water Model (NWM) which will will: 1) assimilate real-time streamflow observations into the channel routing model of the
4
Use of the Stochastic-dynamic Approach in a Single Dynamic-Core Storm-Scale Ensemble for Improved Spread and Reliability of QPF and Surface Variables
Jankov
JTTI
check_circle
2017
Level 3
The goal of this project is to demonstrate that a single-physics suite, single-dynamic-core ensemble with implemented stochastic methods is a skillful and feasible alternative to multi-models in next-generation high-resolution regional and global ensembles.
Isidora Jankov, Judith Berner, Joseph Olson
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Parameterization
JTTI
Common Community Physics Package (CCPP), High-Resolution Rapid Refresh (HRRR), Rapid Refresh (RAP), Unified Forecast System (UFS)
To confirm the reliability and spread of the ensemble forecast on a stochastic parameter perturbation in convection-allowing (3-km grid spacing) ensemble framework based on the High Resolution Rapid Refresh (HRRR). An optimal set of parameters within the HRRR suite of physical parameterizations will be determined, which (a) have known ranges of uncertainty and (b) impact the evolution of key forecast variables.
Demonstrate that a single-physics suite, single-dynamic-core ensemble with implemented stochastic methods is a skillful and feasible alternative to multi-models in next-generation high-resolution regional and global ensembles
EMC
Jason Levit
CIRA/Colorado State University
National Center for Atmospheric Research
CIRES/University of Colorado Boulder
NA17OAR4590181
NA17OAR4590173
NA17OAR4590172
Severe Weather
Colorado
Joint Technology Transfer Initiative (JTTI)
August 2017 - July 2020
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Parameterization
use of the stochastic dynamic approach in a single dynamic core storm scale ensemble for improved spread and reliability of qpf and surface variables, the goal of this project is to demonstrate that a single physics suite single dynamic core ensemble with implemented stochastic methods is a skillful and feasible alternative to multi models in next generation high resolution regional and global ensembles, to confirm the reliability and spread of the ensemble forecast on a stochastic parameter perturbation in convection allowing 3 km grid spacing ensemble framework based on the high resolution rapid refresh hrrr an optimal set of parameters within the hrrr suite of physical parameterizations will be determined which a have known ranges of uncertainty and b impact the evolution of key forecast variables
Environmental Modeling Center (EMC)
CIRA/Colorado State University, National Center for Atmospheric Research, CIRES/University of Colorado Boulder
The goal of this project is to demonstrate that a single-physics suite, single-dynamic-core ensemble with implemented stochastic methods is a skillful and feasible alternative to multi-models in next-generation high-resolution regional and global ensembles.
0
Implementation of Multi-Radar Multi-Sensor Dual-Polarization Radar Synthetic QPE
Cocks
JTTI
check_circle
2017
Level 3
The goal of this project is to test and refine the new Multi-Radar Multi-Sensor (MRMS) Dual-Polarization (DP) quantitative precipitation estimates (QPE) product for a two-year period within the MRMS testbed operating within a real-time environment across the continental United States and ultimatly end with a transition into operations.
Stephen Cocks
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
This project would test and refine the new MRMS DP QPE product for a two-year period within the MRMS testbed operating within a realtime environment across the continental United States. Using data collected during this period, it will compare the MRMS DP QPE performance with that of the current operational MRMS QPE to determine if there are significant reductions in error and bias associated with the former.
Provide enhanced hydrological content and flow on high impact events for improved service to society and industry.
NCO
Jason Levit
CIMMS/University of Oklahoma
NA17OAR4590177
Water Extremes
Oklahoma
Joint Technology Transfer Initiative (JTTI)
August 2017 - July 2020
Radar
implementation of multi radar multi sensor dual polarization radar synthetic qpe, the goal of this project is to test and refine the new multi radar multi sensor mrms dual polarization dp quantitative precipitation estimates qpe product for a two year period within the mrms testbed operating within a real time environment across the continental united states and ultimatly end with a transition into operations, this project would test and refine the new mrms dp qpe product for a two year period within the mrms testbed operating within a realtime environment across the continental united states using data collected during this period it will compare the mrms dp qpe performance with that of the current operational mrms qpe to determine if there are significant reductions in error and bias associated with the former
NCEP Central Operations (NCO)
CIMMS/University of Oklahoma
The goal of this project is to test and refine the new Multi-Radar Multi-Sensor (MRMS) Dual-Polarization (DP) quantitative precipitation estimates (QPE) product for a two-year period within the MRMS testbed operating within a real-time environment
1
Forecast system development activities toward a convective-scale HRRR ensemble
Romine
JTTI
check_circle
2017
Level 3
The proposed work will contribute toward improved short-term guidance on the predictability of high-impact phenomena by developing specific guidance for advancing the High-Resolution Rapid Refresh (HRRR) analysis and forecast system, operationalized within NOAA in 2012, toward an ensemble analysis and prediction system.
Glen Romine
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Verification & Validation
JTTI
High-Resolution Rapid Refresh (HRRR), Multi-Radar/Multi-Sensor System (MRMS), Rapid Refresh (RAP), Unified Forecast System (UFS)
The proposed work will test several approaches in CPE forecast system design, apply appropriate tools for CPE verification, and aim to ameliorate deficiencies owing to configuration choices. More specifically, the NCAR team will aim to show demonstrable improvement over the GSD implementation
Improving the ensemble mean analysis through hybrid ensemble-variational re-centering, use of a CPE analysis to initialize forecasts, and continuous hourly cycling. Improving the reliability of ensemble predictions through examination of flow-dependent initial ensemble perturbations.
WPC
Jacob Carley
National Center for Atmospheric Research
NA17OAR4590182
Severe Weather
Colorado
Joint Technology Transfer Initiative (JTTI)
August 2017 - July 2020
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Verification & Validation
forecast system development activities toward a convective scale hrrr ensemble, the proposed work will contribute toward improved short term guidance on the predictability of high impact phenomena by developing specific guidance for advancing the high resolution rapid refresh hrrr analysis and forecast system operationalized within noaa in 2012 toward an ensemble analysis and prediction system, the proposed work will test several approaches in cpe forecast system design apply appropriate tools for cpe verification and aim to ameliorate deficiencies owing to configuration choices more specifically the ncar team will aim to show demonstrable improvement over the gsd implementation
Weather Prediction Center (WPC)
National Center for Atmospheric Research
The proposed work will contribute toward improved short-term guidance on the predictability of high-impact phenomena by developing specific guidance for advancing the High-Resolution Rapid Refresh (HRRR) analysis and forecast system, operationalized within NOAA in 2012,
6
Multi-Sensor Merged Quantitative Precipitation Estimations for Improved Precipitation Coverage and Accuracy
Martinaitis
JTTI
check_circle
2017
Level 3
This goal of this project is to provide an accurate, high spatiotemporal resolution quantitative precipitation estimation (QPE) product using a combination of multiple observing platforms and physically-based short-term model quantitative precipitation forecasts (QPFs).
Steven Martinaitis
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
High-Resolution Rapid Refresh (HRRR), Multi-Radar/Multi-Sensor System (MRMS)
This project proposes the advancement and expansion of the current operational Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (QPE) product suite to include new high spatiotemporal resolution observing platforms and physically-based forecasts towards a real-time MRMS Multi-Sensor Merged QPE product.
More accurate precipitation coverage and magnitudes, especially in regions with sparse ground-based observing platforms
NCO
Jacob Carley
CIMMS/University of Oklahoma
NA17OAR4590175
Water Extremes
Oklahoma
Joint Technology Transfer Initiative (JTTI)
August 2017 - July 2020
Atmospheric Physics, Dynamics and Nesting, Radar
multi sensor merged quantitative precipitation estimations for improved precipitation coverage and accuracy, this goal of this project is to provide an accurate high spatiotemporal resolution quantitative precipitation estimation qpe product using a combination of multiple observing platforms and physically based short term model quantitative precipitation forecasts qpfs, this project proposes the advancement and expansion of the current operational multi radar multi sensor mrms quantitative precipitation estimation qpe product suite to include new high spatiotemporal resolution observing platforms and physically based forecasts towards a real time mrms multi sensor merged qpe product
NCEP Central Operations (NCO)
CIMMS/University of Oklahoma
This goal of this project is to provide an accurate, high spatiotemporal resolution quantitative precipitation estimation (QPE) product using a combination of multiple observing platforms and physically-based short-term model quantitative precipitation forecasts (QPFs).
1
Implementation of Nested Hyper-Resolution Modeling with Data Assimilation for the National Water Model
Seo
JTTI
check_circle
2017
Level 3
The goal of this project is to implement in collaboration with the NWS Office of Water Prediction (OWP) a one-way nested modeling and analysis system at a street-resolving resolution as part of the NWM operation.
Dong-Jun Seo
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting
JTTI
National Water Model (NWM), Weather Research and Forecasting Model (WRF), Weather Research and Forecasting Model - Hydro (WRF-Hydro)
To implement the National Water Model (NWM) at a street-resolving resolution over a nested domain as part of the NWM operation. The hyper-resolution NWM, or the Hyper-Resolution Model (HRM) for short, can be nested anywhere within the current NWM domain. As with the NWM, we will use WRF-Hydro as the core model but at significantly higher resolutions with scale-commensurate model parameters, and prescribe the initial and boundary conditions by assimilating the NWM states and all available observations within the nested domain.
The HRM will be operable on local computers to provide the forecasters, whether at the OWP or other offices, with the operational flexibility necessary to produce forecasts on demand when and where the conditions warrant
NWC
Jacob Carley
University of Texas-Arlington
NA17OAR4590184
Water Extremes
Texas
Joint Technology Transfer Initiative (JTTI)
August 2017 - July 2020
Data Assimilation and Ensembles, Dynamics and Nesting
implementation of nested hyper resolution modeling with data assimilation for the national water model, the goal of this project is to implement in collaboration with the nws office of water prediction owp a one way nested modeling and analysis system at a street resolving resolution as part of the nwm operation, to implement the national water model nwm at a street resolving resolution over a nested domain as part of the nwm operation the hyper resolution nwm or the hyper resolution model hrm for short can be nested anywhere within the current nwm domain as with the nwm we will use wrf hydro as the core model but at significantly higher resolutions with scale commensurate model parameters and prescribe the initial and boundary conditions by assimilating the nwm states and all available observations within the nested domain
National Water Center (NWC)
University of Texas-Arlington
The goal of this project is to implement in collaboration with the NWS Office of Water Prediction (OWP) a one-way nested modeling and analysis system at a street-resolving resolution as part of the NWM operation.
7
Improvement of WRF-Hydro National Water Model architecture and calibration methods for semi-arid environments with complex terrain
Castro
JTTI
check_circle
2017
Level 3
The goal of this project is to leverage results from a previous project, by adding an empirical channel loss function to the NWM WRF-Hydro Muskingum-Cunge channel routing scheme and calibrate the parameters for the updated NWM, where parameter values are estimated as a function of land and soil characteristics.
Christopher Castro
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Post-processing, Verification & Validation
JTTI
National Water Model (NWM), Weather Research and Forecasting Model (WRF), Weather Research and Forecasting Model - Hydro (WRF-Hydro)
Hydrologic processes in the southwest contiguous US (CONUS), including streamflow and evapotranspiration, are affected by surface heterogeneity and topography. Many of these surface processes are not well represented in the NOAA National Water Model (NWM), leading to significant biases in the southwest CONUS. To ameliorate these systematic errors in the NWM, this work will update the WRF-Hydro architecture and parameters, to improve NOAA's ability to produce water forecasts in the southwest CONUS, specifically during high impact events that can threaten life and property.
Improve NOAA's ability to produce water forecasts in the southwest CONUS, specifically during high impact events that can threaten life and property.
NWC
Jason Levit
University of Arizona
NA17OAR4590183
Water Extremes
Arizona
Joint Technology Transfer Initiative (JTTI)
August 2017 - July 2020
Atmospheric Physics, Data Assimilation and Ensembles, Post-processing, Verification & Validation
improvement of wrf hydro national water model architecture and calibration methods for semi arid environments with complex terrain, the goal of this project is to leverage results from a previous project by adding an empirical channel loss function to the nwm wrf hydro muskingum cunge channel routing scheme and calibrate the parameters for the updated nwm where parameter values are estimated as a function of land and soil characteristics, hydrologic processes in the southwest contiguous us conus including streamflow and evapotranspiration are affected by surface heterogeneity and topography many of these surface processes are not well represented in the noaa national water model nwm leading to significant biases in the southwest conus to ameliorate these systematic errors in the nwm this work will update the wrf hydro architecture and parameters to improve noaa's ability to produce water forecasts in the southwest conus specifically during high impact events that can threaten life and property
National Water Center (NWC)
University of Arizona
The goal of this project is to leverage results from a previous project, by adding an empirical channel loss function to the NWM WRF-Hydro Muskingum-Cunge channel routing scheme and calibrate the parameters for the updated
3
Implementation of a three-dimensional hydrometeor classification algorithm within the Multi-Radar/Multi-Sensor system
Reeves
JTTI
check_circle
2017
Level 3
This goal of this project is to advance the Spectral Bin Classifier (SBC) by incorporating radar observations (used to determine the cloud top, the degree of riming, and the drop-size distribution) to fine tune its output, to allow for more rapid updates, and to include intensities.
Heather Reeves
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will advance the Spectral Bin Classifier (SBC) by incorporating radar observations to fine tune its output, to allow for more rapid updates, and to include intensities. Specifically, radar observations will be used to determine the cloud top, the degree of riming, and the drop-size distribution. Radar-derived precipitation rates will be used to assign intensities. Last, radar-derived melting layer heights will be used to auto-correct the SBC in cases where SN is errantly diagnosed.
A conus-wide analysis of hydrometeor habit updated every 15 minutes, the approximate length of the shortest-lived precipitation type
NCO
Jacob Carley
CIMMS/University of Oklahoma
NA17OAR4590180
Water Extremes
Oklahoma
Joint Technology Transfer Initiative (JTTI)
August 2017 - July 2020
Radar
implementation of a three dimensional hydrometeor classification algorithm within the multi radar multi sensor system, this goal of this project is to advance the spectral bin classifier sbc by incorporating radar observations used to determine the cloud top the degree of riming and the drop size distribution to fine tune its output to allow for more rapid updates and to include intensities, this project will advance the spectral bin classifier sbc by incorporating radar observations to fine tune its output to allow for more rapid updates and to include intensities specifically radar observations will be used to determine the cloud top the degree of riming and the drop size distribution radar derived precipitation rates will be used to assign intensities last radar derived melting layer heights will be used to auto correct the sbc in cases where sn is errantly diagnosed
NCEP Central Operations (NCO)
CIMMS/University of Oklahoma
This goal of this project is to advance the Spectral Bin Classifier (SBC) by incorporating radar observations (used to determine the cloud top, the degree of riming, and the drop-size distribution) to fine tune its
0
FACETs: Developing operationally-ready Hazard Services-Probabilistic Hazard Information (PHI) for convective hazards
Hansen
JTTI
check_circle
2017
Level 3
The goal of this project is to continue development, evaluation, and testing of the AWIPS Hazard Services Probabilistic Hazard Information tool (HS-PHI) for severe convection.
Tracy Hansen, Kevin Manross
Complete
Wed Nov 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Oct 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Probabilistic Weather Forecasting, "Social, Behavioral, and Economic Sciences (SBES)"
JTTI
Advanced Weather Interactive Processing System (AWIPS)
This proposal seeks to continue development, evaluation, and testing of the AWIPS Hazard Services Probabilistic Hazard Information tool (HS-PHI) for severe convection.
Allows continuous improvements to the HS-PHI software.
facets developing operationally ready hazard services probabilistic hazard information phi for convective hazards, the goal of this project is to continue development evaluation and testing of the awips hazard services probabilistic hazard information tool hs phi for severe convection, this proposal seeks to continue development evaluation and testing of the awips hazard services probabilistic hazard information tool hs phi for severe convection
NOAA/OAR/GSL, NOAA/OAR/NSSL
The goal of this project is to continue development, evaluation, and testing of the AWIPS Hazard Services Probabilistic Hazard Information tool (HS-PHI) for severe convection.
0
Forecast Guidance for Aviation Tactical Operations and Strategic Planning over Alaska
Ghirardelli
JTTI
check_circle
2017
Level 3
The goal of this project is to produce skillful and reliable probabilistic guidance for Ceiling & Visibility and Convection & Total Lightning over the Alaska domain in the (Localized Aviation MOS Program (LAMP) model, as well as deterministic guidance to aid with the interpretation of the statistically-derived probabilities.
Judy Ghirardelli
Complete
Wed Nov 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Oct 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Aviation, Dynamics and Nesting, Model & Forecast Guidance, Numerical Weather Prediction (NWP), Post-processing, Probabilistic Weather Forecasting
JTTI
Global Forecast System (GFS)
Accurate forecasts of cloud ceiling height, visibility, convection, and lightning are needed for the safe operation of aircraft. Forecasts are needed at airports, but also for random points where helicopters are used for search and rescue missions. This is especially true for AK, where the rough terrain, vast distances, and lack of roads in many areas dictate that much of the internal travel is by air. The atmospheric numerical weather prediction (NWP) models covering AK provide some guidance for these critical aviation-related variables, but are less skillful than needed. The extension to AK of gridded products involves the collection of the necessary datasets over AK, the development of the specific post-processing equations for use there, the preparation of scripts for operational implementation, and testing in an operational environment.
The forecasts which currently are made hourly and for all hourly projections out to 25 hours, will be extended to 36 hours for longer-range planning and to support the formulation of the 36-hour TAFs (Terminal Aerodrome Forecasts) for international flights.
EMC
NOAA/NWS/STI/DFSB
OWAQ17-JTTI-I-2
Severe Weather
Maryland, Missouri
Joint Technology Transfer Initiative (JTTI)
November 2017 - October 2020
Aircraft Reconnaissance, Aviation, Dynamics and Nesting, Model & Forecast Guidance, Numerical Weather Prediction (NWP), Post-processing, Probabilistic Weather Forecasting
forecast guidance for aviation tactical operations and strategic planning over alaska, the goal of this project is to produce skillful and reliable probabilistic guidance for ceiling visibility and convection total lightning over the alaska domain in the localized aviation mos program lamp model as well as deterministic guidance to aid with the interpretation of the statistically derived probabilities, accurate forecasts of cloud ceiling height visibility convection and lightning are needed for the safe operation of aircraft forecasts are needed at airports but also for random points where helicopters are used for search and rescue missions this is especially true for ak where the rough terrain vast distances and lack of roads in many areas dictate that much of the internal travel is by air the atmospheric numerical weather prediction nwp models covering ak provide some guidance for these critical aviation related variables but are less skillful than needed the extension to ak of gridded products involves the collection of the necessary datasets over ak the development of the specific post processing equations for use there the preparation of scripts for operational implementation and testing in an operational environment
Environmental Modeling Center (EMC)
NOAA/NWS/STI/DFSB
The goal of this project is to produce skillful and reliable probabilistic guidance for Ceiling & Visibility and Convection & Total Lightning over the Alaska domain in the (Localized Aviation MOS Program (LAMP) model, as
0
INSITE: Integrated Support for Impacted air Traffic Environments
Scheck
JTTI
check_circle
2017
Level 3
The goal of this project is to enable the operational transition of the backend processing capabilities of the prototype tool INSITE to the Weather and Climate Operational Supercomputing System (WCOSS) with dissemination of constraint data to AWC and various CWSUs for display of forecast airspace constraints in the Advanced Weather Interactive Processing System (AWIPS).
Melissa Petty Joshua Scheck
Complete
Wed Nov 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Dec 31 2020 03:00:00 GMT-0500 (Eastern Standard Time)
Post-processing
JTTI
Advanced Weather Interactive Processing System (AWIPS)
INSITE is a prototype tool under development at NOAA's Global Systems Division(GSD) in the Earth System Research Laboratory (ESRL) of the Office of Oceanic and Atmospheric Research (OAR). The tool supports National Weather Service (NWS) operations for the convective weather forecast process by providing forecasters an interactive web-based application for identification of potential impacts or constraints to the National Airspace System (NAS) due to forecast convective weather.
Improved support to the impact-based decision support services NWS forecasters provide to FAA.
NCO
NOAA/NWS/NCEP
OWAQ17-JTTI-I-3
Severe Weather
Colorado, Missouri
Joint Technology Transfer Initiative (JTTI)
November 2017 - December 2020
Post-processing
insite integrated support for impacted air traffic environments, the goal of this project is to enable the operational transition of the backend processing capabilities of the prototype tool insite to the weather and climate operational supercomputing system wcoss with dissemination of constraint data to awc and various cwsus for display of forecast airspace constraints in the advanced weather interactive processing system awips, insite is a prototype tool under development at noaa's global systems division gsd in the earth system research laboratory esrl of the office of oceanic and atmospheric research oar the tool supports national weather service nws operations for the convective weather forecast process by providing forecasters an interactive web based application for identification of potential impacts or constraints to the national airspace system nas due to forecast convective weather
NCEP Central Operations (NCO)
NOAA/NWS/NCEP
The goal of this project is to enable the operational transition of the backend processing capabilities of the prototype tool INSITE to the Weather and Climate Operational Supercomputing System (WCOSS) with dissemination of constraint data
0
Extending the Rapidly-Updating Real-Time Mesoscale Analysis (RTMA) to Three Dimensions for Whole-Atmosphere Situational Awareness and Analysis of Record
Alexander
JTTI
check_circle
2017
Level 3
The goal of this project is to develop and implement a 3-D Real-Time Mesoscale Analysis (3D-RTMA) to unify NOAA nowcasting capabilities to meet needs for situational awareness information and forecast verification.
Curtis Alexander
Complete
Wed Nov 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Oct 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Aviation, Dynamics and Nesting, Model & Forecast Guidance, Numerical Weather Prediction (NWP), Verification & Validation
Temporal consistency between real-time analyses and short-term (1-6 hour) numerical weather prediction grids is important for forecast continuity and enhances the operational forecaster's ability to issue watches and warnings. While it has been challenging to achieve this sort of temporal consistency, the increased horizontal resolution of hourly updated fields at 3 km provided by the High-Resolution Rapid Refresh (HRRR) has narrowed the gap between real-time analyses and hourly numerical guidance and enabled some short-term predictive skill for deep convection. This skill improvement is critical for aviation and short-term severe weather applications.
The creation of physically consistent, no-spatial-gap (not possible with a single observing system), 2-D column-integrated fields such as severe weather indices (using 3-D thermodynamic data), improved ceiling height, and precipitation type (using 3-D hydrometeor data with height dependency) that are beyond the scope of current analysis systems.
EMC
NOAA/OAR/GSL
OWAQ17-JTTI-I-4
Severe Weather
Colorado
Joint Technology Transfer Initiative (JTTI)
November 2017 - October 2020
Aviation, Dynamics and Nesting, Model & Forecast Guidance, Numerical Weather Prediction (NWP), Verification & Validation
extending the rapidly updating real time mesoscale analysis rtma to three dimensions for whole atmosphere situational awareness and analysis of record, the goal of this project is to develop and implement a 3 d real time mesoscale analysis 3d rtma to unify noaa nowcasting capabilities to meet needs for situational awareness information and forecast verification, temporal consistency between real time analyses and short term 1 6 hour numerical weather prediction grids is important for forecast continuity and enhances the operational forecaster's ability to issue watches and warnings while it has been challenging to achieve this sort of temporal consistency the increased horizontal resolution of hourly updated fields at 3 km provided by the high resolution rapid refresh hrrr has narrowed the gap between real time analyses and hourly numerical guidance and enabled some short term predictive skill for deep convection this skill improvement is critical for aviation and short term severe weather applications
Environmental Modeling Center (EMC)
NOAA/OAR/GSL
The goal of this project is to develop and implement a 3-D Real-Time Mesoscale Analysis (3D-RTMA) to unify NOAA nowcasting capabilities to meet needs for situational awareness information and forecast verification.
0
Improving the prediction of subseasonal global rainfall variability through the use of a scale-adaptive stochastic physics suite
Bao
JTTI
check_circle
2017
Level 3
The goal of this project is to implement a newly developed, scale-adaptive stochastic physics parameterization suite in the Global Ensemble Forecast System (GEFS), speciffically by focusing on testing, evaluating and calibrating the improved perturbation methods based physical processes, as well as spatial and temporal scales for the perturbations, in order to provide improved uncertainty estimates of precipitation forecasts across a range of prediction time and space scales.
Jian-Wen Bao
Complete
Wed Nov 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon Aug 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Parameterization
JTTI
Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
The ESRL Physical Sciences Division (PSD) will work with the Environmental Modeling Center (EMC) of the National Centers for Environmental Prediction (NCEP) to implement a newly developed, scale-adaptive stochastic physics parameterization suite in the Global Ensemble Forecast System (GEFS). This suite has been developed to improve upon the stochastic physics schemes that were implemented by Jeffrey Whitaker et al. at PSD in the GEFS-based, operational ensemble Kalman filter (EnKF) data assimilation system, and planned to be included in the next GEFS upgrade for operational short- and medium-range ensemble forecasts.
Improved prediction of extreme weather phenomena and variabilities of the global atmosphere on medium to subseasonal time scales.
EMC
NOAA/OAR/ESRL/PSD
OWAQ17-JTTI-I-5
Relevant to All Hazards
Colorado, Maryland
Joint Technology Transfer Initiative (JTTI)
November 2017 - August 2020
Atmospheric Physics, Data Assimilation and Ensembles, Parameterization
improving the prediction of subseasonal global rainfall variability through the use of a scale adaptive stochastic physics suite, the goal of this project is to implement a newly developed scale adaptive stochastic physics parameterization suite in the global ensemble forecast system gefs speciffically by focusing on testing evaluating and calibrating the improved perturbation methods based physical processes as well as spatial and temporal scales for the perturbations in order to provide improved uncertainty estimates of precipitation forecasts across a range of prediction time and space scales, the esrl physical sciences division psd will work with the environmental modeling center emc of the national centers for environmental prediction ncep to implement a newly developed scale adaptive stochastic physics parameterization suite in the global ensemble forecast system gefs this suite has been developed to improve upon the stochastic physics schemes that were implemented by jeffrey whitaker et al at psd in the gefs based operational ensemble kalman filter enkf data assimilation system and planned to be included in the next gefs upgrade for operational short and medium range ensemble forecasts
Environmental Modeling Center (EMC)
NOAA/OAR/ESRL/PSD
The goal of this project is to implement a newly developed, scale-adaptive stochastic physics parameterization suite in the Global Ensemble Forecast System (GEFS), speciffically by focusing on testing, evaluating and calibrating the improved perturbation methods
0
Improving the use of dropsondes in NOAA operations (HWRF)
Sippel
JTTI
check_circle
2017
Level 3
The goal of this project is to improve operational use of aircraft deployed dropsondes assimilated into the NCEP GFS global model and the HWRF regional model as well as assess the benefit of using full dropsonde position and vertical resolution information in NCEP operational models (especially HWRF).
Jason Sippel
Complete
Wed Nov 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Data Assimilation and Ensembles, Dynamics and Nesting
JTTI
Global Forecast System (GFS), Hurricane Weather Research and Forecasting (HWRF), Unified Forecast System (UFS)
A pathway is proposed for improved operational use of aircraft deployed dropsondes assimilated into the NCEP GFS global model and the HWRF regional model. One key element in this proposed work is to provide observation error statistics for the new NRD-94 minisonde developed by NCAR and used exclusively during the NASA HS3 (2012-2014), NOAA SHOUT (2015-2016), and the 2017 NASA/NOAA EPOCH projects.
Inform NOAA regarding the usefulness of the new minisonde as a possible replacement for the RD-94 on standard surveillance and reconnaissance missions.
EMC
NOAA/OAR/AOML
OWAQ17-JTTI-I-6
Tropical Cyclones
Florida
Joint Technology Transfer Initiative (JTTI)
November 2017 - September 2019
Aircraft Reconnaissance, Data Assimilation and Ensembles, Dynamics and Nesting
improving the use of dropsondes in noaa operations hwrf, the goal of this project is to improve operational use of aircraft deployed dropsondes assimilated into the ncep gfs global model and the hwrf regional model as well as assess the benefit of using full dropsonde position and vertical resolution information in ncep operational models especially hwrf, a pathway is proposed for improved operational use of aircraft deployed dropsondes assimilated into the ncep gfs global model and the hwrf regional model one key element in this proposed work is to provide observation error statistics for the new nrd 94 minisonde developed by ncar and used exclusively during the nasa hs3 2012 2014 noaa shout 2015 2016 and the 2017 nasa noaa epoch projects
Environmental Modeling Center (EMC)
NOAA/OAR/AOML
The goal of this project is to improve operational use of aircraft deployed dropsondes assimilated into the NCEP GFS global model and the HWRF regional model as well as assess the benefit of using full
0
Intelligent post-processing of convection-allowing model output to inform Weather Prediction Center outlooks and forecasts
Schumacher
JTTI
check_circle
2018
Level 3
The goal of this project is to 1) quantify the advantages of applying advanced post-processing methods to convection-allowing model (CAM) guidance for informing WPC outlooks and forecasts, 2) evaluate the developed forecast systems in the Flash Flood and Intense Rainfall (FFaIR) experiment and in an operational environment at the WPC and Hydrometeorology Testbed, and 3) implement an operational version of this forecast system at WPC.
Russ Schumacher
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
intelligent post processing of convection allowing model output to inform weather prediction center outlooks and forecasts, the goal of this project is to 1 quantify the advantages of applying advanced post processing methods to convection allowing model cam guidance for informing wpc outlooks and forecasts 2 evaluate the developed forecast systems in the flash flood and intense rainfall ffair experiment and in an operational environment at the wpc and hydrometeorology testbed and 3 implement an operational version of this forecast system at wpc, develop and evaluate machine learning excessive rainfall products using convection allowing model output
Weather Prediction Center (WPC)
CIRA/Colorado State University
The goal of this project is to 1) quantify the advantages of applying advanced post-processing methods to convection-allowing model (CAM) guidance for informing WPC outlooks and forecasts, 2) evaluate the developed forecast systems in the
3
Advancing ADCIRC U.S. Atlantic and Gulf Coast Grids and Capabilities to Facilitate Coupling to the National Water Model in ESTOFS Operational Forecasting
Westerink
JTTI
check_circle
2018
Level 3
The goal of this projects is to implement recent physics based developments, an automated feature based grid generation toolbox (OceanMesh2D), and a version of ADCIRC that bypasses dry elements in order to produce the next generation ESTOFS model for the U.S. Atlantic, Gulf of Mexico and Caribbean coasts and floodplains.
Joannes Westerink
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Dynamics and Nesting
JTTI
ADvanced CIRCulation (ADCIRC), Global Extratropical Surge and Tide Operational Forecast System (G-ESTOFS), Global Forecast System (GFS), National Water Model (NWM), Weather Research and Forecasting Model (WRF), Weather Research and Forecasting Model - Hydro (WRF-Hydro)
Recent physics based developments and an automated feature based grid generation toolbox (OceanMesh2D) will be implemented in order to produce the next generation ESTOFS model for the U.S. Atlantic, Gulf of Mexico and Caribbean coasts and floodplains. A coupling to the National Water Model (NWM) WRF-Hydro code at both upstream and lateral boundaries of ADCIRC's wet/dry interface is implemented, and direct rainfall volume over ADCIRC's inundated coastal floodplain regions will also be included using GFS rainfall data.
Improved resolution along the coastal floodplain will allow for more accurate representations of water flow, including discharge and tidal/storm surge inundation, along the coast
NOS
Ed Myers
University of Notre Dame
NA18OAR4590377
Water Extremes
Indiana
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2021
Atmospheric Physics, Coupled Models & Techniques, Dynamics and Nesting
advancing adcirc u s atlantic and gulf coast grids and capabilities to facilitate coupling to the national water model in estofs operational forecasting, the goal of this projects is to implement recent physics based developments an automated feature based grid generation toolbox oceanmesh2d and a version of adcirc that bypasses dry elements in order to produce the next generation estofs model for the u s atlantic gulf of mexico and caribbean coasts and floodplains, recent physics based developments and an automated feature based grid generation toolbox oceanmesh2d will be implemented in order to produce the next generation estofs model for the u s atlantic gulf of mexico and caribbean coasts and floodplains a coupling to the national water model nwm wrf hydro code at both upstream and lateral boundaries of adcirc's wet dry interface is implemented and direct rainfall volume over adcirc's inundated coastal floodplain regions will also be included using gfs rainfall data
University of Notre Dame
The goal of this projects is to implement recent physics based developments, an automated feature based grid generation toolbox (OceanMesh2D), and a version of ADCIRC that bypasses dry elements in order to produce the next
3
Accelerated Implementation, Testing and Evaluation of Optimized Radar Data Assimilation Capabilities within Ensemble-Variational Hybrid GSI for the NOAA Convection-allowing rapidly updated Forecasting System
Jung
JTTI
check_circle
2018
Level 3
The goal of this project is to 1) develop and test a GSI-based EnKF-EnVar hybrid DA system that directly assimilates additional full-volume WSR-88D Vr and Z data for FV3, 2) optimize hybrid DA configurations for a convection-allowing rapidly refreshing forecasting system, and (3) evaluate the EnKF-EnVar hybrid DA System at the DTC using a simulated operational environment similar to the HRRR configuration.
Youngsun Jung, Ming Xue, Jeffrey Duda
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
This project aims to accelerate the development and evaluation of an updated data assimilation system that can directly assimilate reflectivity and radial velocity data for FV3. It will help accelerate the adaptation of advanced DA with advanced and optimized capabilities for the FV3-based next generation convection-allowing, rapidly updated forecasting system that is expected to replace the operational HRRR.
The goal of this project is to improve the skill of high-resolution, convection allowing models by assimilating radar data. Improvements in the prediction of severe convective storms and associated hazards are the focus.
EMC
Dave Myrick
University of Oklahoma - Center for Analysis and Prediction of Storms
CIRES/University of Colorado
NOAA/OAR/ESRL/GSD
NA18OAR4590385
NA18OAR4590392
Relevant to All Hazards
Colorado, Oklahoma
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2021
Data Assimilation and Ensembles, Radar
accelerated implementation testing and evaluation of optimized radar data assimilation capabilities within ensemble variational hybrid gsi for the noaa convection allowing rapidly updated forecasting system, the goal of this project is to 1 develop and test a gsi based enkf envar hybrid da system that directly assimilates additional full volume wsr 88d vr and z data for fv3 2 optimize hybrid da configurations for a convection allowing rapidly refreshing forecasting system and 3 evaluate the enkf envar hybrid da system at the dtc using a simulated operational environment similar to the hrrr configuration, this project aims to accelerate the development and evaluation of an updated data assimilation system that can directly assimilate reflectivity and radial velocity data for fv3 it will help accelerate the adaptation of advanced da with advanced and optimized capabilities for the fv3 based next generation convection allowing rapidly updated forecasting system that is expected to replace the operational hrrr
Environmental Modeling Center (EMC)
University of Oklahoma - Center for Analysis and Prediction of Storms, CIRES/University of Colorado, NOAA/OAR/ESRL/GSD
The goal of this project is to 1) develop and test a GSI-based EnKF-EnVar hybrid DA system that directly assimilates additional full-volume WSR-88D Vr and Z data for FV3, 2) optimize hybrid DA configurations for
6
Development and NWS Forecaster Evaluation of a Convective-scale Ensemble System for Probabilistic Heavy Rainfall and Severe Weather Forecasts
Yussouf
JTTI
check_circle
2018
Level 3
The objective of this study is to improve the 0-6 h probabilistic quantitative precipitation forecasts (PQPFs) by integrating novel post-processing, visualization and verification capabilities to increase the NWS forecasters' usability of the probabilistic model guidance, so that they can more rapidly and accurately identify potential heavy rainfall events, and provide guidance for the subsequent issuance of flash flood advisories, watch and warning products.
Nusrat Yussouf, Michael Erickson
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting, Radar, Verification & Validation, Forecast Product Usability & Design, Visual Risk Communication
JTTI
To aid WPC's MetWatch desk and Weather Forecast Office (WFO) forecasters in predicting heavy rainfall, flash floods and issuing watches, warnings and guidance products, this study aims to improve 0-6 hr Quantitative Precipitation Forecasts and other severe weather hazards from a convective-scale ensemble forecast system by using observed and radar-derived quantitative precipitation estimates and the Model Evaluation Toolkit. The overarching goals of this study are to improve the heavy precipitation forecasts and to increase National Weather Service forecasters' usability of the probabilistic model guidance so that they can more rapidly and accurately identify potential heavy rainfall events.
The overarching goal of this study is to improve the heavy precipitation forecasts by integrating post-processing, visualization, and verification capabilities. In addition, it aims to increase National Weather Service forecasters' usability of the probabilistic model guidance so that they can more rapidly and accurately identify potential heavy rainfall events, and provide guidance for the subsequent issuance of flash flood advisories, watch and warning products.
EMC
James Halgren
Cooperative Institute for Mesoscale Meteorological Studies;
Cooperative Institute for Research in Environmental Sciences
NA18OAR4590361
NA18OAR4590384
"Severe Weather, Water Extremes"
Colorado, Oklahoma
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2021
Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting, Radar, Verification & Validation, Forecast Product Usability & Design, Visual Risk Communication
development and nws forecaster evaluation of a convective scale ensemble system for probabilistic heavy rainfall and severe weather forecasts, the objective of this study is to improve the 0-6 h probabilistic quantitative precipitation forecasts pqpfs by integrating novel post processing visualization and verification capabilities to increase the nws forecasters' usability of the probabilistic model guidance so that they can more rapidly and accurately identify potential heavy rainfall events and provide guidance for the subsequent issuance of flash flood advisories watch and warning products, to aid wpc's metwatch desk and weather forecast office wfo forecasters in predicting heavy rainfall flash floods and issuing watches warnings and guidance products this study aims to improve 0-6 hr quantitative precipitation forecasts and other severe weather hazards from a convective scale ensemble forecast system by using observed and radar derived quantitative precipitation estimates and the model evaluation toolkit the overarching goals of this study are to improve the heavy precipitation forecasts and to increase national weather service forecasters' usability of the probabilistic model guidance so that they can more rapidly and accurately identify potential heavy rainfall events
Environmental Modeling Center (EMC)
Cooperative Institute for Mesoscale Meteorological Studies;, Cooperative Institute for Research in Environmental Sciences
The objective of this study is to improve the 0-6 h probabilistic quantitative precipitation forecasts (PQPFs) by integrating novel post-processing, visualization and verification capabilities to increase the NWS forecasters' usability of the probabilistic model guidance,
0
Improving Convection-Permitting Ensemble Based Uncertainty Communication for Decision Support using the Weather Archive and Visualization Environment (WAVE)
Demuth
JTTI
check_circle
2018
Level 3
The goal of this project is to develop an enhanced Weather Archive and Visualization Environment (WAVE) system that will facilitate improved assessment and communication of hazardous weather threats for impact-based decision support services (IDSS) by (a) conducting social science research with forecasters and core partners to understand their needs for convection-permitting ensemble (CPE)-based uncertainty and associated verification information, (b) using this knowledge to guide research on model verification of CPEs, and (c) using knowledge from both research efforts to guide development of WAVE.
Julie Demuth, Melissa Petty, Jennifer Henderson
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Probabilistic Weather Forecasting, Verification & Validation, Communicating Probabilities & Uncertainty, Decision Support, Risk Communication, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
JTTI
There is tremendous potential to improve the communication of forecast uncertainty and probabilistic information for hazardous weather threats by using convection-permitting ensemble (CPE) data to enhance the Weather Archive and Visualization Environment (WAVE), a web-based, agile platform that ingests, displays, and archives model data. The PIs propose an integrated, multi-method, co-produced approach that includes (a) interview- and survey-based data collection with NWS forecasters and EMs to gather information about their CPE-based uncertainty and association verification information uses and needs, (b) development of improved forecaster-oriented CPE verification metrics, and (c) WAVE development to ingest, curate, and visualize CPE output and verification information.
The Weather Research and Forecasting Innovation Act of 2017 requires NOAA to prioritize research to improve weather data, modeling, computing, forecasts, and warnings for the protection of life, property and the enhancement of the national economy. More specifically, the law targets improvements to knowledge transfer and technologies including, but not limited to, numerical weather prediction and model advancements (P. L. 115-25 102.b.3). This research directly addresses this need by understanding forecaster and stakeholder needs related to model-produced uncertainty and subsequently integrating their information needs into existing technology.
NWS
Ed Myers
National Center for Atmospheric Research
CIRA/Colorado State University
CIRES/University of Colorado
NA18OAR4590362
NA18OAR4590374
NA18OAR4590372
Relevant to All Hazards
Colorado
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2022
Data Assimilation and Ensembles, Probabilistic Weather Forecasting, Verification & Validation, Communicating Probabilities & Uncertainty, Decision Support, Risk Communication, "Social Science (SBES)", Visual Risk Communication
improving convection permitting ensemble based uncertainty communication for decision support using the weather archive and visualization environment wave, the goal of this project is to develop an enhanced weather archive and visualization environment wave system that will facilitate improved assessment and communication of hazardous weather threats for impact based decision support services idss by a conducting social science research with forecasters and core partners to understand their needs for convection permitting ensemble cpe based uncertainty and associated verification information b using this knowledge to guide research on model verification of cpes and c using knowledge from both research efforts to guide development of wave, there is tremendous potential to improve the communication of forecast uncertainty and probabilistic information for hazardous weather threats by using convection permitting ensemble cpe data to enhance the weather archive and visualization environment wave a web based agile platform that ingests displays and archives model data the pis propose an integrated multi method co produced approach that includes a interview and survey based data collection with nws forecasters and ems to gather information about their cpe based uncertainty and association verification information uses and needs b development of improved forecaster oriented cpe verification metrics and c wave development to ingest curate and visualize cpe output and verification information
National Weather Service (NWS)
National Center for Atmospheric Research, CIRA/Colorado State University, CIRES/University of Colorado
The goal of this project is to develop an enhanced Weather Archive and Visualization Environment (WAVE) system that will facilitate improved assessment and communication of hazardous weather threats for impact-based decision support services (IDSS) by
3
Development and evaluation of extended range ensemble streamflow and water resources forecast products for the National Water Model
Wood
JTTI
check_circle
2018
Level 3
The goal of this project is to improve the National Water Model's (NWM's) utility for providing S2S predictions to support extended range water management decisions by developing, transitioning, and testing a low-risk, alternative NWM configuration for long range ensemble (LRE) forecasting that is designed to meet the requirements of the extended range forecast use case.
Andrew Wood, Bart Nijssen
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
To improve the NWM's utility for providing subseasonal to seasonal predictions to support extended range water management decisions, this project works toward developing and testing a low-risk, alternative NWM configuration for long range ensemble (LRE) forecasting that is designed to meet the requirements of extended range forecasting. Notably, this configuration would have the ability to apply a full range of community methods that have long been relied upon by existing operational extended range forecasting systems in the US and internationally.
The implementation of a long-range ensemble technique into the National Water Model will allow for seasonal predictions of water supply, which could be applied to water management decisions, particularly in arid regions.
NWC
James Nelson
National Center for Atmospheric Research
University of Washington
NA18OAR4590393
NA18OAR4590364
Water Extremes
Colorado, Washington
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2022
Data Assimilation and Ensembles
development and evaluation of extended range ensemble streamflow and water resources forecast products for the national water model, the goal of this project is to improve the national water model's nwm's utility for providing s2s predictions to support extended range water management decisions by developing transitioning and testing a low risk alternative nwm configuration for long range ensemble lre forecasting that is designed to meet the requirements of the extended range forecast use case, to improve the nwm's utility for providing subseasonal to seasonal predictions to support extended range water management decisions this project works toward developing and testing a low risk alternative nwm configuration for long range ensemble lre forecasting that is designed to meet the requirements of extended range forecasting notably this configuration would have the ability to apply a full range of community methods that have long been relied upon by existing operational extended range forecasting systems in the us and internationally
National Water Center (NWC)
National Center for Atmospheric Research, University of Washington
The goal of this project is to improve the National Water Model's (NWM's) utility for providing S2S predictions to support extended range water management decisions by developing, transitioning, and testing a low-risk, alternative NWM configuration
0
Improving Hail Forecasts Through Operational Implementation of the HAILCAST Hail Model
Adams-Selin
JTTI
check_circle
2018
Level 3
The goal of this project is to improve convective hail forecasting through operational implementation of a physically-based hail model, HAILCAST, and incorporation of new hail forecasting verification techniques into the Model Evaluation Tools (MET+).
Rebecca Adams-Selin, Tara Jensen, Israel Jirak
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
High-Resolution Rapid Refresh (HRRR), Unified Forecast System (UFS)
The goal of this proposal is to improve convective hail forecasting through operational implementation of a physically-based hail model, HAILCAST, and incorporation of new hail forecasting verification techniques into the Model Evaluation Tools (MET+).
Implementation of the HAILCAST model into operational forecast systems can improve the ability to detect and warn on the possiblility of severe, damaging hail
EMC
Mark Fresch
Atmospheric and Environmental Research (AER);
National Center for Atmospheric Research;
NWS
NA18OAR4590388
NA18OAR4590382
Severe Weather
Colorado, Massachusetts
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2021
Atmospheric Physics, Verification & Validation
improving hail forecasts through operational implementation of the hailcast hail model, the goal of this project is to improve convective hail forecasting through operational implementation of a physically based hail model hailcast and incorporation of new hail forecasting verification techniques into the model evaluation tools met+, the goal of this proposal is to improve convective hail forecasting through operational implementation of a physically based hail model hailcast and incorporation of new hail forecasting verification techniques into the model evaluation tools met+
Environmental Modeling Center (EMC)
Atmospheric and Environmental Research (AER);, National Center for Atmospheric Research;, NWS
The goal of this project is to improve convective hail forecasting through operational implementation of a physically-based hail model, HAILCAST, and incorporation of new hail forecasting verification techniques into the Model Evaluation Tools (MET+).
1
Improving the Quality of Water Forecasts via Bayesian Integration of Multiple Sources of Forecast Information
Welles
JTTI
check_circle
2018
Level 3
The goal of this project is to implement an integrated suite of algorithms and diagnostic displays for merging forecasts from multiple models and for characterizing and communicating forecast uncertainty.
Edwin Welles
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
The NWS now generates a substantial collection of river forecast information across the US. The information includes predictions of river stages and the associated uncertainties. Integrating information from multiple forecasts will result in a forecast more skillful than any of the individual forecasts. This project implements an integrating post processing module which can combine an arbitrary set of forecasts with the necessary verification information to derive an improved forecast.
The approach implemented in this project will benefit the NWS not only by improving forecast skill but also by providing a proving ground for new experimental forecasts developed by the research community with an intent toward operationalization in the NWS.
improving the quality of water forecasts via bayesian integration of multiple sources of forecast information, the goal of this project is to implement an integrated suite of algorithms and diagnostic displays for merging forecasts from multiple models and for characterizing and communicating forecast uncertainty, the nws now generates a substantial collection of river forecast information across the us the information includes predictions of river stages and the associated uncertainties integrating information from multiple forecasts will result in a forecast more skillful than any of the individual forecasts this project implements an integrating post processing module which can combine an arbitrary set of forecasts with the necessary verification information to derive an improved forecast
National Water Center (NWC)
Deltares
The goal of this project is to implement an integrated suite of algorithms and diagnostic displays for merging forecasts from multiple models and for characterizing and communicating forecast uncertainty.
2
Implementation of an Accurate, Robust and Computationally Efficient Channel Routing Technique for the National Water Model (NWM)
Meselhe
JTTI
check_circle
2018
Level 3
The main objective of this project is to implement a robust, computationally efficient, and strategically accurate channel routing engine within the operational environment of the National Water Model (NWM).
Ehab Meselhe
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting, Visual Risk Communication
JTTI
National Water Model (NWM)
An existing limitation of the National Water model leads to inaccuracies in predicting flood depth and extent under certain flow conditions and in certain geographic regions. The main objective of this proposal is to implement a robust, computationally efficient, and strategically accurate channel routing engine within the operational environment of the National Water Model (NWM). This project implements an approach designed to maximize the benefits of the computational efficiency and numerical stability of the kinematic and diffusive wave approximations while enhancing the routing accuracy in regions where the flow conditions (e.g. strong backwater effects, low-gradient regions) require the deployment of the full dynamic wave approximation.
The outcomes of this work will improve the overall accuracy of the National Water Model in predicting flood elevations, extent and duration.
NWC
Jen Sprague
Tulane University
NA18OAR4590394
Water Extremes
Louisiana
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2021
Atmospheric Physics, Dynamics and Nesting, Visual Risk Communication
implementation of an accurate robust and computationally efficient channel routing technique for the national water model nwm, the main objective of this project is to implement a robust computationally efficient and strategically accurate channel routing engine within the operational environment of the national water model nwm, an existing limitation of the national water model leads to inaccuracies in predicting flood depth and extent under certain flow conditions and in certain geographic regions the main objective of this proposal is to implement a robust computationally efficient and strategically accurate channel routing engine within the operational environment of the national water model nwm this project implements an approach designed to maximize the benefits of the computational efficiency and numerical stability of the kinematic and diffusive wave approximations while enhancing the routing accuracy in regions where the flow conditions e g strong backwater effects low gradient regions require the deployment of the full dynamic wave approximation
National Water Center (NWC)
Tulane University
The main objective of this project is to implement a robust, computationally efficient, and strategically accurate channel routing engine within the operational environment of the National Water Model (NWM).
2
Implementing convective storm statistics from a large reanalysis of WSR-88D data for model verification and forecasting probabilistic uncertainty
Smith
JTTI
check_circle
2018
Level 3
The goal of this project is to continue the research-to-operations (R2O) transition process for key aspects of the Forecasting a Continuum of Environmental Threats (FACETs) effort for convective hazards by 1) using a large, reanalysis dataset of convective storms using WSR-88D data for model verification, 2) providing storm-based probabilistic trends and historical distributions of convective storm features for use in the probabilistic hazard information (PHI) tool and 3) understanding how different forecasters interpret, process, and utilize probability grids within the warning decision process communicate this information to end-users for decision-support
Travis Smith
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project expands the implementation of a large, reanalysis dataset of convective storms using WSR-88D data for a variety of uses, including model verification, providing storm-based probabilistic trends and historical distributions for use in the probabilistic hazard information (PHI) tool, and understanding how different forecasters interpret, process, and utilize probability grids within the warning decision process communicate this information to end-users for decision-support.
This project builds the framework for producing probabilistic severe weather forecasts based on a large database of past severe weather events. It aims to improve probabilistic tools used to issue severe weather warnings.
implementing convective storm statistics from a large reanalysis of wsr 88d data for model verification and forecasting probabilistic uncertainty, the goal of this project is to continue the research to operations r2o transition process for key aspects of the forecasting a continuum of environmental threats facets effort for convective hazards by 1 using a large reanalysis dataset of convective storms using wsr 88d data for model verification 2 providing storm based probabilistic trends and historical distributions of convective storm features for use in the probabilistic hazard information phi tool and 3 understanding how different forecasters interpret process and utilize probability grids within the warning decision process communicate this information to end users for decision support, this project expands the implementation of a large reanalysis dataset of convective storms using wsr 88d data for a variety of uses including model verification providing storm based probabilistic trends and historical distributions for use in the probabilistic hazard information phi tool and understanding how different forecasters interpret process and utilize probability grids within the warning decision process communicate this information to end users for decision support
NCEP Central Operations (NCO)
CIMMS/University of Oklahoma
The goal of this project is to continue the research-to-operations (R2O) transition process for key aspects of the Forecasting a Continuum of Environmental Threats (FACETs) effort for convective hazards by 1) using a large, reanalysis
3
Inter-Office Collaboration Affecting Severe Weather Warning Services
Stumpf
JTTI
check_circle
2018
Level 3
The overall goal of this project is to develop, test, and evaluate with NWS forecasters new software to facilitate inter-office collaboration and determine the best ways, via software design and warning best practices, to produce seamless hazardous weather information and consistent warning services from WFO to WFO.
Gregory Stumpf, Alyssa Bates, Darrel Kingfield, Chris Golden, Chen Ling
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will study and test the issues related to inter-office collaboration during National Weather Service (NWS) severe weather warning operations. In particular, it evaluates ways to resolve the lack of a continuous transition of warnings as a storm crosses or straddles CWA boundaries, often leading to inconsistencies of messaging between adjacent WFOs. The Probabilistic Hazard Information warning tool will be tested to aid in cross-CWA collaboration, allowing one WFO to "hand-off" a storm object to their neighboring WFO, resulting in probabilistic severe threat plumes and resulting warnings moving seamlessly across CWA boundaries. It also explores the socio-cultural underpinnings of inconsistencies in warning information conveyed across CWA boundaries through interviews and surveys of NWS forecasters.
This project will help result in an improved understanding of inter-Weather Forecast Office (WFO) collaboration issues during severe convective weather warning operations. Additionally, this project will result in the development of a software solution and decision-making best practices for seamless severe convective weather warning services across NWS WPO boundaries.
PRSSO
Brian Cosgrove
CIMMS/University of Oklahoma
CIRES/University of Colorado Boulder
University of Akron
NOAA/NWS/NSSL
NOAA/NWS/Warning Decision Training Division
inter office collaboration affecting severe weather warning services, the overall goal of this project is to develop test and evaluate with nws forecasters new software to facilitate inter office collaboration and determine the best ways via software design and warning best practices to produce seamless hazardous weather information and consistent warning services from wfo to wfo, this project will study and test the issues related to inter office collaboration during national weather service nws severe weather warning operations in particular it evaluates ways to resolve the lack of a continuous transition of warnings as a storm crosses or straddles cwa boundaries often leading to inconsistencies of messaging between adjacent wfos the probabilistic hazard information warning tool will be tested to aid in cross cwa collaboration allowing one wfo to "hand off" a storm object to their neighboring wfo resulting in probabilistic severe threat plumes and resulting warnings moving seamlessly across cwa boundaries it also explores the socio cultural underpinnings of inconsistencies in warning information conveyed across cwa boundaries through interviews and surveys of nws forecasters
CIMMS/University of Oklahoma, CIRES/University of Colorado Boulder, University of Akron, NOAA/NWS/NSSL, NOAA/NWS/Warning Decision Training Division
The overall goal of this project is to develop, test, and evaluate with NWS forecasters new software to facilitate inter-office collaboration and determine the best ways, via software design and warning best practices, to produce
6
Implications of inconsistent visual displays on end user uncertainty, risk perception, and behavioral intentions
Grundstein
JTTI
check_circle
2018
Level 3
This project seeks to address the perceived operational gap of of inconsistent weather risk information by qualitatively and quantitatively evaluating visual design inconsistencies associated with the Storm Prediction Center's (SPC) Convective Outlook visual display.
Andrew Grundstein
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Risk Communication, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
JTTI
This project seeks to qualitatively and quantitatively assess visual design inconsistencies associated with the Storm Prediction Center's (SPC) Convective Outlook visual display. Severe weather hazards (tornadoes, lightning, and wind) have caused more fatalities than any other extreme weather event in the United States between 2007-2016. With a newfound focus on visual risk information, operational meteorologists are now creating and sharing more visual information than ever before, making the Convective Outlook a key means for communicating severe weather risk. Therefore, this visual display will be the focus of the proposed project and act as a means to examine the effects of visual inconsistencies on end user risk perception, uncertainty, and behavioral intentions.
An interdisciplinary team of atmospheric and social scientists supports operational meteorologists within the SPC, NWS, and NOAA by providing the first empirical examination of "message consistency" that investigates the importance of having a "consistent" visual design when communicating risk, uncertainty, and probabilistic information. The outcome of this project will include (1) concrete suggestions for implementation in an operational environment, (2) policy recommendations, and (3) suggestions for operational best practices to ensure that Convective Outlooks are "consistently" communicated within NOAA.
implications of inconsistent visual displays on end user uncertainty risk perception and behavioral intentions, this project seeks to address the perceived operational gap of of inconsistent weather risk information by qualitatively and quantitatively evaluating visual design inconsistencies associated with the storm prediction center's spc convective outlook visual display, this project seeks to qualitatively and quantitatively assess visual design inconsistencies associated with the storm prediction center's spc convective outlook visual display severe weather hazards tornadoes lightning and wind have caused more fatalities than any other extreme weather event in the united states between 2007 2016 with a newfound focus on visual risk information operational meteorologists are now creating and sharing more visual information than ever before making the convective outlook a key means for communicating severe weather risk therefore this visual display will be the focus of the proposed project and act as a means to examine the effects of visual inconsistencies on end user risk perception uncertainty and behavioral intentions
Storm Prediction Center (SPC)
University of Georgia
This project seeks to address the perceived operational gap of of inconsistent weather risk information by qualitatively and quantitatively evaluating visual design inconsistencies associated with the Storm Prediction Center's (SPC) Convective Outlook visual display.
5
Improving Water Cycle Prediction in the National Water Model through Regional Calibration, Meteorological Forcing Improvements, and Coastal Coupling
Cardinale
JTTI
check_circle
2018
Level 3
The goal of this project is to improve water cycle prediction for the Laurentian Great Lakes basin and the Lake Champlain basin through three focused tasks including: i) calibration and verification of NWM parameters across select sub-watersheds, and extrapolation of findings (i.e. regionalization) across the entire land surface of the Laurentian Great Lakes and Champlain basins, ii) assessment of precipitation data and models across the Laurentian Great Lakes and Champlain basins with recommendations for operational implementation, and iii) development of coastal coupling between NWM and the Finite Volume Community Ocean Model (FVCOM) for the entire coastline of the Great Lakes and Lake Champlain.
Brad Cardinale, Thomas Johengen, Laura Read
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
This project improves the water cycle prediction capabilities of the National Water Model (NWM) through a regionally-customized approach to calibration of model parameters, analysis and recommendations for operational meteorological forcings, and implementation of coastal coupling between land surface and coastal hydrodynamics models.
Improvements to the water cycle predication capabilities will lead to advancements in regional hydrological modeling as a whole
improving water cycle prediction in the national water model through regional calibration meteorological forcing improvements and coastal coupling, the goal of this project is to improve water cycle prediction for the laurentian great lakes basin and the lake champlain basin through three focused tasks including i calibration and verification of nwm parameters across select sub watersheds and extrapolation of findings i e regionalization across the entire land surface of the laurentian great lakes and champlain basins ii assessment of precipitation data and models across the laurentian great lakes and champlain basins with recommendations for operational implementation and iii development of coastal coupling between nwm and the finite volume community ocean model fvcom for the entire coastline of the great lakes and lake champlain, this project improves the water cycle prediction capabilities of the national water model nwm through a regionally customized approach to calibration of model parameters analysis and recommendations for operational meteorological forcings and implementation of coastal coupling between land surface and coastal hydrodynamics models
National Water Center (NWC)
Cooperative Institute for Great Lakes Research
The goal of this project is to improve water cycle prediction for the Laurentian Great Lakes basin and the Lake Champlain basin through three focused tasks including: i) calibration and verification of NWM parameters across
2
Making Sense of Uncertainty: Improving the Use of Hydrologic Probabilistic Information in Decision-Making
Carr
JTTI
check_circle
2018
Level 3
The goal of this project is to study how: a) Advanced Hydrologic Prediction System (AHPS) and regional hydrographs (e.g., hydrographs developed by NWS RFCs or WFOs); b) outputs from the Hydrologic Ensemble Forecast Service (HEFS), including seasonal water supply forecast related products; and c) briefings for impact-based decision support services (IDSS), can work together to convey the complexity of certainty and uncertainty in short, medium-term and seasonal hydrologic forecasts.
Rachel Hogan Carr
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
This project was designed to test, develop, and transition a suite a new probabilistic hydrologic products, as well as to provide design and delivery recommendations for how probabilistic information can be presented to residential and professional communities.
Sec 102 of the Weather Research and Forecasting Improvement Act seeks to improve "the understanding of how the public receives, interprets, and responds to warnings and forecasts of high impact weather events that endanger life and property." Using scenario-based focus groups, surveys, and interviews, this project explores how end users interpret and respond to three hydrologic forecast products, with the ultimate goal of developing more easily understood products for the protection of life and property.
making sense of uncertainty improving the use of hydrologic probabilistic information in decision making, the goal of this project is to study how a advanced hydrologic prediction system ahps and regional hydrographs e g hydrographs developed by nws rfcs or wfos b outputs from the hydrologic ensemble forecast service hefs including seasonal water supply forecast related products and c briefings for impact based decision support services idss can work together to convey the complexity of certainty and uncertainty in short medium term and seasonal hydrologic forecasts, this project was designed to test develop and transition a suite a new probabilistic hydrologic products as well as to provide design and delivery recommendations for how probabilistic information can be presented to residential and professional communities
NWS Office of Water Prediction (OWP)
Nurture Nature Center
The goal of this project is to study how: a) Advanced Hydrologic Prediction System (AHPS) and regional hydrographs (e.g., hydrographs developed by NWS RFCs or WFOs); b) outputs from the Hydrologic Ensemble Forecast Service (HEFS),
3
Improving Operational Hail Prediction through Machine Learning from HREF and CAPS Storm-Scale Ensemble FV3 and WRF ARW Forecasts including Advanced Microphysics
Snook
JTTI
check_circle
2018
Level 3
The primary goals of this project are (1) to develop, refine, and improve existing machine learning hail forecast algorithms recently developed by the PIs for convection permitting/resolving NWP model forecasts, (2) to develop and demonstrate new advanced machine learning-based products for hail forecasting, (3) to prepare these new and refined algorithms for implementation into the operational High Resolution Ensemble Forecast (HREF) system, and (4) to evaluate the capabilities of these algorithms for ensembles of forecasts using the Finite Volume Cubed-Sphere Dynamical Core (FV3).
Nathan Snook, David Gagne
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Numerical Weather Prediction (NWP), Probabilistic Weather Forecasting
JTTI
Finite-Volume Cubed-Sphere Dynamical Core (FV3), High Resolution Ensemble Forecast (HREF), Weather Research and Forecasting Model (WRF)
This project aims to develop and evaluate new machine learning algorithms for 0-48-hour probabilistic hail forecasting, with the goal of operational implementation. In addition to statistical machine learning methods such as random forests and gradient boosted regression trees, deep learning methods, including convolutional neural networks, will also be investigated. Machine learning-based hail forecast products will be produced for WRF-ARW and FV3 ensembles from the Center for Analysis and Prediction of Storms (CAPS), as well as the operational HREFv2 ensemble.
The forecast products from this project, which rely upon both statistical machine learning (e.g. random forests, gradient boosted regression trees) and deep learning (e.g. convolutional neural nets) methods, have been proven to produce skillful day-ahead forecasts of hail threat and will provide valuable numerical forecast guidance for operational prediction of severe hail.
SPC
Daryl Kleist
University of Oklahoma
National Center for Atmospheric Research
NA18OAR4590371
NA18OAR4590387
Severe Weather
Colorado, Oklahoma
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2021
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Numerical Weather Prediction (NWP), Probabilistic Weather Forecasting
improving operational hail prediction through machine learning from href and caps storm scale ensemble fv3 and wrf arw forecasts including advanced microphysics, the primary goals of this project are 1 to develop refine and improve existing machine learning hail forecast algorithms recently developed by the pis for convection permitting resolving nwp model forecasts 2 to develop and demonstrate new advanced machine learning based products for hail forecasting 3 to prepare these new and refined algorithms for implementation into the operational high resolution ensemble forecast href system and 4 to evaluate the capabilities of these algorithms for ensembles of forecasts using the finite volume cubed sphere dynamical core fv3, this project aims to develop and evaluate new machine learning algorithms for 0-48 hour probabilistic hail forecasting with the goal of operational implementation in addition to statistical machine learning methods such as random forests and gradient boosted regression trees deep learning methods including convolutional neural networks will also be investigated machine learning based hail forecast products will be produced for wrf arw and fv3 ensembles from the center for analysis and prediction of storms caps as well as the operational hrefv2 ensemble
Storm Prediction Center (SPC)
University of Oklahoma, National Center for Atmospheric Research
The primary goals of this project are (1) to develop, refine, and improve existing machine learning hail forecast algorithms recently developed by the PIs for convection permitting/resolving NWP model forecasts, (2) to develop and demonstrate
1
Representing agricultural management processes in the National Water Model
Chen
JTTI
check_circle
2018
Level 3
The goal of this project is to enhance the prediction capabilities of the National Water Model (NWM) through establishing a framework to represent the hydrologic influence of agriculture management processes in the NWM.
Fei Chen, Kristie Franz
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Croplands cover about 20% of the contiguous U.S. land and a major portion of the Upper Mississippi River Basin where irrigation and agriculture have altered precipitation runoff characteristics at local and regional scales. In response, this work accelerates the transition of recent progress in representing agriculture management in the land modeling community to the operational National Water Model by establishing a modeling framework to represent the hydrologic influence of agriculture management processes.
This research will provide better understanding of how to model the effects of agricultural management techniques in the National Water Model, thereby improving overall flood and soil moisture prediction skills.
NWC
Brian Cosgrove
National Center for Atmospheric Research
Iowa State University
NA18OAR4590381
NA18OAR4590390
Water Extremes
Colorado, Iowa
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2021
Atmospheric Physics, Verification & Validation
representing agricultural management processes in the national water model, the goal of this project is to enhance the prediction capabilities of the national water model nwm through establishing a framework to represent the hydrologic influence of agriculture management processes in the nwm, croplands cover about 20% of the contiguous u s land and a major portion of the upper mississippi river basin where irrigation and agriculture have altered precipitation runoff characteristics at local and regional scales in response this work accelerates the transition of recent progress in representing agriculture management in the land modeling community to the operational national water model by establishing a modeling framework to represent the hydrologic influence of agriculture management processes
National Water Center (NWC)
National Center for Atmospheric Research, Iowa State University
The goal of this project is to enhance the prediction capabilities of the National Water Model (NWM) through establishing a framework to represent the hydrologic influence of agriculture management processes in the NWM.
11
Development and testing of displacement data assimilation
Nehrkorn
JTTI
check_circle
2018
Level 3
The goal of this project is to complete the migration of displacement data assimilation to NOAA's DAS toward operational implementation, with a particular emphasis on convection-allowing scale data assimilation.
Thomas Nehrkorn
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Many complex forecast model errors are the result of incorrect predicted locations of a feature, such as a hurricane, front, mesoscale convective system, or convective storm cell. This project implements a displacement data assimilation (DA) technique that solves for displacement errors. The work aims to complete the migration of displacement data assimilation toward operational implementation, with a particular emphasis on convection-allowing scale data assimilation.
This project operationalizes a displacement data assimilation technique to improve the modeled position of meteorological features, thereby improving the overall forecast skill.
EMC
Nicole Kurkowski
AER
NA18OAR4590383
Relevant to All Hazards
Massachusetts
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2021
Data Assimilation and Ensembles
development and testing of displacement data assimilation, the goal of this project is to complete the migration of displacement data assimilation to noaa's das toward operational implementation with a particular emphasis on convection allowing scale data assimilation, many complex forecast model errors are the result of incorrect predicted locations of a feature such as a hurricane front mesoscale convective system or convective storm cell this project implements a displacement data assimilation da technique that solves for displacement errors the work aims to complete the migration of displacement data assimilation toward operational implementation with a particular emphasis on convection allowing scale data assimilation
Environmental Modeling Center (EMC)
AER
The goal of this project is to complete the migration of displacement data assimilation to NOAA's DAS toward operational implementation, with a particular emphasis on convection-allowing scale data assimilation.
0
Improving subseasonal water supply prediction across the Western United States through assimilation of remotely sensed snow cover, snow albedo, and snow water equivalent in the NOAA National Water Model
Rittger
JTTI
check_circle
2018
Level 3
The goal of this project is to address deficiencies in the NOAA National Water Model (NWM) by assimilating a new, near-real-time (NRT) suite of remote-sensing-based snow products into the NWM.
Karl Rittger, Aubrey Dugger, Edward Bair
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Post-processing, Water in the West
JTTI
National Water Model (NWM)
We propose to address these current deficiencies by assimilating a new, near-real-time (NRT)
suite of remote-sensing-based snow products into the NWM. The foundation of our methodology
is a post-processing algorithm developed by the National Snow and Ice Data Center (NSIDC) at
the University of Colorado to create daily, spatially and temporally consistent estimates of
fractional snow-covered area, clean snow albedo, and dust radiative forcing, products which
perform better than traditional MODIS snow products. University of California at Santa Barbara
(UCSB) will expand this product suite in high-priority target regions with near-real-time snow
water equivalent (SWE) estimates derived by an efficient machine-learning algorithm trained on
the NSIDC remote-sensing suite and a historical SWE reconstruction model. To bring this rich
data suite into the NWM, the National Center for Atmospheric Research (NCAR) has devised an
ensemble particle-filter assimilation method to winnow the long-range forecast ensemble set to
"optimal" combinations of model parameters and forecasts that best replicate the snow
observations, inserting uncertainty consistent with the observations back into the long-range
analysis. To test the new framework, the team will conduct a system-level demonstration of the
NWM long-range forecast configuration for the 2018-2019 period with weekly regional snow
state and parameter updates, including full computational benchmarking and feasibility
assessment for U.S.-wide implementation.
The long-range configuration is ideally suited for our data-model fusion approach. Its simpler
process representation and faster computational speeds allow parameter and forcing ensembles to
become computationally tractable, permitting seamless assimilation of high-quality, high-impact
observations like the NSIDC snow suite. With modest effort, we believe this expanded system
will make substantial improvements in the accuracy and relevance of the NWM seasonal
forecasts to water resource managers.
NWC
Israel Jirak
University of Colorado
National Center for Atmospheric Research
University of California Santa Barbara
NA18OAR4590367
NA18OAR4590379
NA18OAR4590380
Water Extremes
California, Colorado
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2021
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Post-processing, Water in the West
improving subseasonal water supply prediction across the western united states through assimilation of remotely sensed snow cover snow albedo and snow water equivalent in the noaa national water model, the goal of this project is to address deficiencies in the noaa national water model nwm by assimilating a new near real time nrt suite of remote sensing based snow products into the nwm, we propose to address these current deficiencies by assimilating a new near real time nrt suite of remote sensing based snow products into the nwm the foundation of our methodology is a post processing algorithm developed by the national snow and ice data center nsidc at the university of colorado to create daily spatially and temporally consistent estimates of fractional snow covered area clean snow albedo and dust radiative forcing products which perform better than traditional modis snow products university of california at santa barbara ucsb will expand this product suite in high priority target regions with near real time snow water equivalent swe estimates derived by an efficient machine learning algorithm trained on the nsidc remote sensing suite and a historical swe reconstruction model to bring this rich data suite into the nwm the national center for atmospheric research ncar has devised an ensemble particle filter assimilation method to winnow the long range forecast ensemble set to "optimal" combinations of model parameters and forecasts that best replicate the snow observations inserting uncertainty consistent with the observations back into the long range analysis to test the new framework the team will conduct a system level demonstration of the nwm long range forecast configuration for the 2018 2019 period with weekly regional snow state and parameter updates including full computational benchmarking and feasibility assessment for u s wide implementation
National Water Center (NWC)
University of Colorado, National Center for Atmospheric Research, University of California Santa Barbara
The goal of this project is to address deficiencies in the NOAA National Water Model (NWM) by assimilating a new, near-real-time (NRT) suite of remote-sensing-based snow products into the NWM.
6
Assimilating Novel WSR-88D and GOES-16 Observations to Improve Convection-Allowing Model Forecasts of Convection Initiation and Severe Weather
Stensrud
JTTI
check_circle
2018
Level 3
The goal of this project is to assimilate complimentary observations that would improve convection allowing model (CAM) ensemble analysis and forecasts of the three ingredients (low-level moisture, instability, and lift) needed for convection initiation (CI).
David Stensrud
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Radar, Satellite & Remote Sensing Technologies
JTTI
Convection Allowing Models (CAM)
Predicting convective initiation at the right time and location can lead to large improvements in forecast accuracy and directly and positively impact NOAA forecast operations. The three ingredients needed for CI - low-level moisture, instability, and lift - are all influenced by processes on multiple spatial and temporal scales that are currently not well observed. This project assimilate complimentary observations that would improve CAM ensemble analysis and forecasts of the three ingredients needed for CI.
This research tests the ability of assimilation of new observations to improve the ability to predict convective initiation that precedes high-impact hazardous weather.
EMC
Nicole Kurkowski
Pennsylvania State University
NA18OAR4590369
Severe Weather
Pennsylvania
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2022
Data Assimilation and Ensembles, Radar, Satellite & Remote Sensing Technologies
assimilating novel wsr 88d and goes 16 observations to improve convection allowing model forecasts of convection initiation and severe weather, the goal of this project is to assimilate complimentary observations that would improve convection allowing model cam ensemble analysis and forecasts of the three ingredients low level moisture instability and lift needed for convection initiation ci, predicting convective initiation at the right time and location can lead to large improvements in forecast accuracy and directly and positively impact noaa forecast operations the three ingredients needed for ci low level moisture instability and lift are all influenced by processes on multiple spatial and temporal scales that are currently not well observed this project assimilate complimentary observations that would improve cam ensemble analysis and forecasts of the three ingredients needed for ci
Environmental Modeling Center (EMC)
Pennsylvania State University
The goal of this project is to assimilate complimentary observations that would improve convection allowing model (CAM) ensemble analysis and forecasts of the three ingredients (low-level moisture, instability, and lift) needed for convection initiation (CI).
6
Applying IBM Analytics to the Challenge of Broadcast Data
Klockow-McClain
JTTI
check_circle
2018
Level 3
The objective of this project is to develop a software application that leverages Microsoft Azure's AI platform, and use it to conduct a pilot study of the ways broadcasters communicate forecast uncertainty.
Kimberly Klockow-McClain
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Television broadcast coverage is a critical yet understudied link in the extreme weather communication chain. This project sought to address this gap through the development of an algorithm (CAST.Bot) that allows for relatively fast and targeted analysis of broadcast communication.
Since local weather broadcasts remain the most frequently-used means of receiving weather warning information, this project will help NWS, specifically the Communications Division, to better understand how NWS information is disseminated and ultimately received and acted upon. This could help NWS better connect NWS products to societal outcomes, develop communication strategies with broadcast partners, and identify topics that require collaborative action at Integrated Warning Team (IWT) meetings.
applying ibm analytics to the challenge of broadcast data, the objective of this project is to develop a software application that leverages microsoft azure's ai platform and use it to conduct a pilot study of the ways broadcasters communicate forecast uncertainty, television broadcast coverage is a critical yet understudied link in the extreme weather communication chain this project sought to address this gap through the development of an algorithm cast bot that allows for relatively fast and targeted analysis of broadcast communication
CIMMS/University of Oklahoma
The objective of this project is to develop a software application that leverages Microsoft Azure's AI platform, and use it to conduct a pilot study of the ways broadcasters communicate forecast uncertainty.
1
Communicating Forecast Uncertainty and Probabilistic Information: Experimenting with Social Observation Data in the Hazardous Weather Testbed
Silva
JTTI
check_circle
2018
Level 3
The goal of this project is to improve communication of forecast uncertainty and probabilistic information by building upon the Severe Weather and Society Survey (SWSS), a new data collection capacity that provides generalizable, longitudinal, and experimental data on how members of the U.S. public receive, understand, and respond to uncertainty and probabilistic information in severe weather forecasts and warnings.
Carol Silva
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
The Severe Weather and Society Survey (SWSS) is a yearly public survey that is managed by the University of Oklahoma Center for Risk and Crisis Management (CRCM). The survey includes reoccurring (baseline) items that measure forecast and warning reception, comprehension, and response, as well as new (one-time) items and experiments that address multiple topics, such as the impact of probabilistic information on risk judgements and protective action decision making. This proposed project will transition the SWSS to RL-6/RL-7 by creating and testing an interactive dashboard that allows users to access the SWSS data in an operational environment. Overall, this project attempts to demonstrate the value of the survey data in operations by allowing forecasters to identify communication strategies and products that are most effective and needed in the environment in which the weather enterprise is communicating.
By giving users access to data prior to an event, they can have better communication strategies/products, which advance NOAA's mission of protect life and property
communicating forecast uncertainty and probabilistic information experimenting with social observation data in the hazardous weather testbed, the goal of this project is to improve communication of forecast uncertainty and probabilistic information by building upon the severe weather and society survey swss a new data collection capacity that provides generalizable longitudinal and experimental data on how members of the u s public receive understand and respond to uncertainty and probabilistic information in severe weather forecasts and warnings, the severe weather and society survey swss is a yearly public survey that is managed by the university of oklahoma center for risk and crisis management crcm the survey includes reoccurring baseline items that measure forecast and warning reception comprehension and response as well as new one time items and experiments that address multiple topics such as the impact of probabilistic information on risk judgements and protective action decision making this proposed project will transition the swss to rl 6 rl 7 by creating and testing an interactive dashboard that allows users to access the swss data in an operational environment overall this project attempts to demonstrate the value of the survey data in operations by allowing forecasters to identify communication strategies and products that are most effective and needed in the environment in which the weather enterprise is communicating
University of Oklahoma
The goal of this project is to improve communication of forecast uncertainty and probabilistic information by building upon the Severe Weather and Society Survey (SWSS), a new data collection capacity that provides generalizable, longitudinal, and
6
Calibration of Channel Properties to Improve Streamflow Estimates in the National Water Model
Minear
JTTI
check_circle
2018
Level 3
The goal of this project is to improve the static reach database (e.g. parameter set) of ~2.7 million reaches utilized by the NWM for channel computations, particularly the reach values of channel dimensions and hydraulic resistance by exploiting a large unpublished dataset of ~48,000 gages well-distributed across the US with field discharge measurements compiled from USGS and eight states.
Toby Minear
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
ADvanced CIRCulation (ADCIRC), National Water Model (NWM)
One potential source of error in National Water Model predictions of discharge and flood timing is channel property, and the sensitivity of NWM river routing remains unresolved. This project expands the national database of riverbed dimensions and aims to improve reach properties in the NWM, thereby improving the physical realism of the model and supporting more accurate model performance.
The end result of this project will be an improved NWM reach properties parameter set that will improve the physical realism of the NWM, support more accurate performance of the NWM, and additionally may help to resolve errors in other parts of the model framework
NWC
Brian Cosgrove
University of Colorado
NA18OAR4590391
Water Extremes
Colorado
Joint Technology Transfer Initiative (JTTI)
October 2018 - September 2020
Atmospheric Physics
calibration of channel properties to improve streamflow estimates in the national water model, the goal of this project is to improve the static reach database e g parameter set of ~2 7 million reaches utilized by the nwm for channel computations particularly the reach values of channel dimensions and hydraulic resistance by exploiting a large unpublished dataset of ~48 000 gages well distributed across the us with field discharge measurements compiled from usgs and eight states, one potential source of error in national water model predictions of discharge and flood timing is channel property and the sensitivity of nwm river routing remains unresolved this project expands the national database of riverbed dimensions and aims to improve reach properties in the nwm thereby improving the physical realism of the model and supporting more accurate model performance
National Water Center (NWC)
University of Colorado
The goal of this project is to improve the static reach database (e.g. parameter set) of ~2.7 million reaches utilized by the NWM for channel computations, particularly the reach values of channel dimensions and hydraulic
1
Generating Operational Guidelines for Use of Probabilistic Hazard Information (PHI) with End Users
Berry
JTTI
check_circle
2018
Level 3
The goal of this project is to improve communication of forecast uncertainty and probabilistic information by increasing our understanding of how NWS core partners process probabilities, and exploring the ways forecasters and end-users relate objective hazard probabilities to the watch, warning and advisory system.
Initial development of Probabilistic Hazard Information (or PHI) assumed that probability thresholds would automatically issue a product. This often contributed to a lack of coherence between forecasters and end users about probabilistic warning thresholds. This project will continue R2O transition processes for key aspects of FACETs for convective hazards by conducting qualitative work with forecasters and end users to a) situate the role of hazard probabilities in warnings, and b) generate operational recommendations of PHI. The qualitative work outlined in the proposal is essential to reach RL-8 and allow for a continued transition of PHI into operations by incorporating end users (i.e., broadcast meteorologists and EM) in the process.
This project will help improve the communication of forecast uncertainty and probabilistic information by increasing the understanding of how NWS core partners process probabilities and explore the ways forecasters and end users relate objective hazard probabilities to the watch, warning, and advisory system.
NWS OSTI
Jen Sprague
CIMMS/University of Oklahoma
Cooperative Institute for Research in Environmental Sciences
generating operational guidelines for use of probabilistic hazard information phi with end users, the goal of this project is to improve communication of forecast uncertainty and probabilistic information by increasing our understanding of how nws core partners process probabilities and exploring the ways forecasters and end users relate objective hazard probabilities to the watch warning and advisory system, initial development of probabilistic hazard information or phi assumed that probability thresholds would automatically issue a product this often contributed to a lack of coherence between forecasters and end users about probabilistic warning thresholds this project will continue r2o transition processes for key aspects of facets for convective hazards by conducting qualitative work with forecasters and end users to a situate the role of hazard probabilities in warnings and b generate operational recommendations of phi the qualitative work outlined in the proposal is essential to reach rl 8 and allow for a continued transition of phi into operations by incorporating end users i e broadcast meteorologists and em in the process
CIMMS/University of Oklahoma, Cooperative Institute for Research in Environmental Sciences
The goal of this project is to improve communication of forecast uncertainty and probabilistic information by increasing our understanding of how NWS core partners process probabilities, and exploring the ways forecasters and end-users relate objective
4
Advancing Frequently-Updating Storm-Scale Ensemble Data Assimilation and Prediction Towards Operations
Alexander
JTTI
check_circle
2018
Level 3
The goal of this project is development toward a unified hourly-updated storm-scale ensemble data assimilation and forecasting system: the Rapid Refresh Forecast System (RRFS) by adding an ensemble storm-scale data-assimilation capability to HRRRv4, the final WRF-based operational implementation of the HRRR model and developing expertise and the software required for the unified storm-scale ensemble data assimilation and forecasting system.
Curtis Alexander, Pam Heinselman, Jacob Carley, Joshua Scheck
Complete
Sun Jul 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Aviation, Data Assimilation and Ensembles, Post-processing, Verification & Validation
JTTI
High-Resolution Rapid Refresh (HRRR), Rapid Refresh (RAP), Unified Forecast System (UFS), Weather Research and Forecasting Model (WRF)
This project represents a significant step toward a unified hourly-updated storm-scale ensemble data assimilation and forecasting system, improving short-term (0-36 h) prediction of high-impact severe convective storms, heavy rainfall, landfalling tropical cyclones, aviation weather events, and winter weather, and helping advance Warn-On-Forecast capabilities.
The ultimate goal of this project is to improve short-term forecasts of high-impact severe convective storms, heavy rainfall, landfalling tropical cyclones, aviation weather events, and winter weather.
WPC
Jeff McQueen (EMC)
OAR/ESRL/GSD
OWAQ-18-JTTI-I-4
Relevant to All Hazards
Colorado, Maryland, Missouri, Oklahoma
Joint Technology Transfer Initiative (JTTI)
July 2018 - June 2021
Atmospheric Physics, Aviation, Data Assimilation and Ensembles, Post-processing, Verification & Validation
advancing frequently updating storm scale ensemble data assimilation and prediction towards operations, the goal of this project is development toward a unified hourly updated storm scale ensemble data assimilation and forecasting system the rapid refresh forecast system rrfs by adding an ensemble storm scale data assimilation capability to hrrrv4 the final wrf based operational implementation of the hrrr model and developing expertise and the software required for the unified storm scale ensemble data assimilation and forecasting system, this project represents a significant step toward a unified hourly updated storm scale ensemble data assimilation and forecasting system improving short term 0 36 h prediction of high impact severe convective storms heavy rainfall landfalling tropical cyclones aviation weather events and winter weather and helping advance warn on forecast capabilities
Weather Prediction Center (WPC)
OAR/ESRL/GSD
The goal of this project is development toward a unified hourly-updated storm-scale ensemble data assimilation and forecasting system: the Rapid Refresh Forecast System (RRFS) by adding an ensemble storm-scale data-assimilation capability to HRRRv4, the final
0
VSAFE: Verification Services for Aviation Forecast Evaluation
Stone
JTTI
check_circle
2018
Level 3
The goal of this project is to enable continued support and availability of the VSAFE prototype verification capabilities running in the GSD operating environment, operational hardening of CWVS, and adaptations to EVENT to use the constraint-based outputs from the Integrated Support for Impacted Air-traffic Environments (INSITE) Data Services capabilities that are planned to be implemented on the Weather and Climate Operational Supercomputing System (WCOSS).
Melissa Petty Michael Krause Kevin Stone
Complete
Sun Jul 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Aviation, Verification & Validation
JTTI
Verification Services for Aviation Forecast Evaluation (VSAFE) is a prototype tool integrating access to verification capabilities. These web-based tools provide verification and analysis capabilities to support performance monitoring of a variety of the NWS aviation services and products provided to the Federal Aviation Administration (FAA). VSAFE incorporates new techniques for evaluating product skill with respect to lead time to event onset and cessation, aligned with performance requirements established by the FAA for terminal thunderstorm, wind, and ceiling and visibility events. This work enables continued support and availability of the VSAFE prototype verification capabilities
This work allows for continued verification of prototype aviation forecast tools, which are designed to improve aviation forecasts of significant weather events.
NCO
Brian Gockel
NOAA/NWS/NCEP
OWAQ-18-JTTI-I-5
Relevant to All Hazards
Maryland
Joint Technology Transfer Initiative (JTTI)
July 2018 - June 2021
Aviation, Verification & Validation
vsafe verification services for aviation forecast evaluation, the goal of this project is to enable continued support and availability of the vsafe prototype verification capabilities running in the gsd operating environment operational hardening of cwvs and adaptations to event to use the constraint based outputs from the integrated support for impacted air traffic environments insite data services capabilities that are planned to be implemented on the weather and climate operational supercomputing system wcoss, verification services for aviation forecast evaluation vsafe is a prototype tool integrating access to verification capabilities these web based tools provide verification and analysis capabilities to support performance monitoring of a variety of the nws aviation services and products provided to the federal aviation administration faa vsafe incorporates new techniques for evaluating product skill with respect to lead time to event onset and cessation aligned with performance requirements established by the faa for terminal thunderstorm wind and ceiling and visibility events this work enables continued support and availability of the vsafe prototype verification capabilities
NCEP Central Operations (NCO)
NOAA/NWS/NCEP
The goal of this project is to enable continued support and availability of the VSAFE prototype verification capabilities running in the GSD operating environment, operational hardening of CWVS, and adaptations to EVENT to use the
0
Optimizing Geostationary Lightning Mapper Use in AWIPS
Rudlosky
JTTI
check_circle
2018
Level 3
The goal of this project is to implement a new Geostationary Lightning Mapper (GLM) plugin tool thatprovides forecasters with full access to the GLM data suite from both GOES16 (current) and GOESS/17 (future).
Scott Rudlosky, Joseph Patton, Anita Leroy, Kristin Calhoun, Jason Burks
Complete
Sun Jul 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Verification & Validation
JTTI
This project works toward the implementation of a new Geostationary Lightning Mapper (GLM) plugin tool that provides forecasters with full access to the GLM data suite from both GOES16 and GOESS/17. A new GLM product will be added to AWIPS to replace the initial gridded GLM display. In addition to lightning grids, this project will develop tools to monitor trends in lightning over time. The identification and tracking of storms will provide crucial new capabilities, including tables with basic storm statistics and time series of various GLM variables.
The proposed GLM tool moves beyond individual lightning products to introduce a new technique with a broad array of operational applications. This new tool more clearly demonstrates the utility of the GLM observations and cements their role in the NWS concept of operations (CONOPS) and standard operating procedure (SOP).
NCO
Jacob Carley
NOAA/NESDIS/STAR
OWAQ-18-JTTI-I-6
Severe Weather
Alabama, Maryland, Missouri, Oklahoma
Joint Technology Transfer Initiative (JTTI)
July 2018 - June 2021
Dynamics and Nesting, Verification & Validation
optimizing geostationary lightning mapper use in awips, the goal of this project is to implement a new geostationary lightning mapper glm plugin tool thatprovides forecasters with full access to the glm data suite from both goes16 current and goess 17 future, this project works toward the implementation of a new geostationary lightning mapper glm plugin tool that provides forecasters with full access to the glm data suite from both goes16 and goess 17 a new glm product will be added to awips to replace the initial gridded glm display in addition to lightning grids this project will develop tools to monitor trends in lightning over time the identification and tracking of storms will provide crucial new capabilities including tables with basic storm statistics and time series of various glm variables
NCEP Central Operations (NCO)
NOAA/NESDIS/STAR
The goal of this project is to implement a new Geostationary Lightning Mapper (GLM) plugin tool thatprovides forecasters with full access to the GLM data suite from both GOES16 (current) and GOESS/17 (future).
0
Using HYSPLIT ensemble dispersion modeling for forecasting applications
Stunder
JTTI
check_circle
2018
Level 3
The main objective of this project is to assess and communicate the uncertainty in the forecasting of the transport, dispersion, and deposition of such materials by using ensemble dispersion simulations generated by HYSPLIT driven by operational National Weather Service (NWS) National Centers for Environmental Prediction (NCEP) meteorological ensemble forecasts.
Barbara Stunder, Alice Crawford
Complete
Sun Jul 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Jun 30 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, High-Performance Computing (HPC), Communicating Probabilities & Uncertainty
Currently, operational products produced for the NWS by the HYSPLIT atmospheric trajectory and dispersion model are deterministic. Depending on the situation, the products may contain significant uncertainties which are not estimated or properly conveyed. This work develops products that provide NWS with HYSPLIT products which assess and communicate uncertainty in the modeled transport, dispersion, and deposition of hazardous materials, using HYSPLIT ensemble dispersion simulations.
Additional uncertainty information for forecasters will allow decision makers additional tools to make informed decisions during hazardous material release events.
EMC
Nicole Kurkowski
NOAA/OAR/ARL
OWAQ-18-JTTI-I-7
Relevant to All Hazards
Maryland
Joint Technology Transfer Initiative (JTTI)
July 2018 - June 2022
Data Assimilation and Ensembles, High-Performance Computing (HPC), Communicating Probabilities & Uncertainty
using hysplit ensemble dispersion modeling for forecasting applications, the main objective of this project is to assess and communicate the uncertainty in the forecasting of the transport dispersion and deposition of such materials by using ensemble dispersion simulations generated by hysplit driven by operational national weather service nws national centers for environmental prediction ncep meteorological ensemble forecasts, currently operational products produced for the nws by the hysplit atmospheric trajectory and dispersion model are deterministic depending on the situation the products may contain significant uncertainties which are not estimated or properly conveyed this work develops products that provide nws with hysplit products which assess and communicate uncertainty in the modeled transport dispersion and deposition of hazardous materials using hysplit ensemble dispersion simulations
Environmental Modeling Center (EMC)
NOAA/OAR/ARL
The main objective of this project is to assess and communicate the uncertainty in the forecasting of the transport, dispersion, and deposition of such materials by using ensemble dispersion simulations generated by HYSPLIT driven by
0
FACETs: Advancing physical and social science concepts toward operational
implementation of Probabilistic Hazard Information
Gerard
JTTI
check_circle
2018
Level 3
The goal of this project is to continue the research-to-operations (R2O) transition process for key aspects of the Forecasting a Continuum of Environmental Threats (FACETs) effort for convective hazards.
Alan Gerard
Complete
Sun Jul 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Jun 30 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
This project leverages years of re-analyzed radar-based storm information to further the development of probabilistic hazard information products. This includes expanding the nowcast model to include multiple scales and feature types for different weather hazards and providing initial storm-motion estimates based on previous storms with similar characteristics. The project will also examine the effects of different representations of forecast uncertainty on risk perception.
This project continues work focused on building the framework for producing probabilistic severe weather forecasts. It aims to improve existing and develop new probabilistic tools used to issue more timely and descriptive severe weather warnings.
facets advancing physical and social science concepts toward operational implementation of probabilistic hazard information, the goal of this project is to continue the research to operations r2o transition process for key aspects of the forecasting a continuum of environmental threats facets effort for convective hazards, this project leverages years of re analyzed radar based storm information to further the development of probabilistic hazard information products this includes expanding the nowcast model to include multiple scales and feature types for different weather hazards and providing initial storm motion estimates based on previous storms with similar characteristics the project will also examine the effects of different representations of forecast uncertainty on risk perception
Storm Prediction Center (SPC)
NOAA/OAR/NSSL
The goal of this project is to continue the research-to-operations (R2O) transition process for key aspects of the Forecasting a Continuum of Environmental Threats (FACETs) effort for convective hazards.
0
Adding TC Genesis Verification Capabilities to the Model Evaluation Tools - TC Software
Newman
JTTI
check_circle
2018
Level 3
The goal of this project is to add a new tool to the Model Evaluation Tools software package to verify deterministic and probabilistic TC genesis forecasts.
Daniel Halperin Kathryn Newman
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Probabilistic Weather Forecasting, Verification & Validation
JTTI-STI
Unified Forecast System (UFS)
The National Hurricane Center uses several tropical cyclone genesis guidance products for their operational Tropical Weather Outlook (TWO). Currently there is no unified verification system that allows NHC to directly compare its probabilistic TWO forecasts against the various guidance tools. Furthermore, global model output is used in many of the TC genesis guidance products and is considered an important source of raw guidance for TC genesis forecasts. The proposed work seeks to add a new tool to the Model Evaluation Tools software package to verify deterministic and probabilistic TC genesis forecasts.
This work can be applied to advancing forecasts of tropical cyclone development in the priority time periods of 0-3 and/or 6-10 days.
NHC
Mark DeMaria
Embry-Riddle Aeronautical University
NA18NWS4680066
Tropical Cyclones
Colorado, Florida
Joint Technology Transfer Initiative (JTTI)
September 2018 - August 2022
Model & Forecast Guidance, Probabilistic Weather Forecasting, Verification & Validation
adding tc genesis verification capabilities to the model evaluation tools tc software, the goal of this project is to add a new tool to the model evaluation tools software package to verify deterministic and probabilistic tc genesis forecasts, the national hurricane center uses several tropical cyclone genesis guidance products for their operational tropical weather outlook two currently there is no unified verification system that allows nhc to directly compare its probabilistic two forecasts against the various guidance tools furthermore global model output is used in many of the tc genesis guidance products and is considered an important source of raw guidance for tc genesis forecasts the proposed work seeks to add a new tool to the model evaluation tools software package to verify deterministic and probabilistic tc genesis forecasts
National Hurricane Center (NHC)
Embry-Riddle Aeronautical University
The goal of this project is to add a new tool to the Model Evaluation Tools software package to verify deterministic and probabilistic TC genesis forecasts.
4
Development and Evaluation of new Statistical Calibration Methods for Multi-Model Ensemble Weeks 3-4 Probabilistic Forecasts
Vigaud
JTTI
check_circle
2018
Level 3
The goal of this project is to extend previous work developing a multi-model ensembling (MME) prediction system for submonthly forecasts to improve CPC week 3-4 probabilistic precipitation and temperature outlooks by adapting established calibration and MME approaches and evaluating the improvements to the existing methodology based on gridpoint Extended Logistic Regressions (ELR) and equal weighting of individual forecasts validated by the PIs over the US, where it was found to achieve probabilistically reliable winter tercile precipitation week 3-4 forecasts.
Nicolas Vigaud, Andrew Robertson, Michael Tippett, Nachiteka Acharya
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Post-processing, Probabilistic Weather Forecasting
JTTI-STI
Unified Forecast System (UFS)
The goal of this project is to extend previous work developing a prediction system for submonthly forecasts to improve CPC week 3-4 probabilistic precipitation and temperature outlooks. Ultimately, this project will evaluate the methodologies for a multi-model ensemble based on the operational models that CPC utilizes and transition an improved subseasonal forecast calibration system to CPC operations.
The goal of this project is to extend previous work developing a prediction system for submonthly forecasts to improve CPC week 3-4 probabilistic precipitation and temperature outlooks.
EMC
Jessica Meixner
Columbia University
NA18NWS4680067
Relevant to All Hazards
New York
Joint Technology Transfer Initiative (JTTI)
September 2018 - August 2022
Data Assimilation and Ensembles, Dynamics and Nesting, Post-processing, Probabilistic Weather Forecasting
development and evaluation of new statistical calibration methods for multi model ensemble weeks 3 4 probabilistic forecasts, the goal of this project is to extend previous work developing a multi model ensembling mme prediction system for submonthly forecasts to improve cpc week 3 4 probabilistic precipitation and temperature outlooks by adapting established calibration and mme approaches and evaluating the improvements to the existing methodology based on gridpoint extended logistic regressions elr and equal weighting of individual forecasts validated by the pis over the us where it was found to achieve probabilistically reliable winter tercile precipitation week 3 4 forecasts, the goal of this project is to extend previous work developing a prediction system for submonthly forecasts to improve cpc week 3 4 probabilistic precipitation and temperature outlooks ultimately this project will evaluate the methodologies for a multi model ensemble based on the operational models that cpc utilizes and transition an improved subseasonal forecast calibration system to cpc operations
Environmental Modeling Center (EMC)
Columbia University
The goal of this project is to extend previous work developing a multi-model ensembling (MME) prediction system for submonthly forecasts to improve CPC week 3-4 probabilistic precipitation and temperature outlooks by adapting established calibration and
2
The Impact of Ocean Resolution in the Unified Forecast System (UFS) on the subseasonal forecast of extreme hydrological events
Meixner
JTTI
check_circle
2018
Level 3
The objective of the project is to deliver 1) A scientifically validated version of the UFS with capabilities to run computationally efficient the ocean model MOM6 at 0.25 degrees global resolution and 2) a set of 20-year reforecasts compliant with the SubX developed forecast protocol that can be used for calibration of real-time subseasonal forecasts.
Cristiana Stan Jessica Meixner
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Skillful subseasonal forecasts (lead times ranging from 15 to 45 days) will allow stakeholders to better prepare for extremes in the hydrological cycle such as the onset of drought conditions and heavy precipitation events. The influence of SST anomalies on the atmospheric variability is at the core of weather-climate nexus, and how this influence affects the subseasonal forecast skill is still an open question. This project works toward a scientifically validated version of the Unified Forecast System that contributes to the understanding of the role of small SST anomalies on atmospheric predictability.
A better understanding of how ocean resolution and small SST anomalies impact subseasonal forecasts can be leveraged to produce improved long-range forecasts of drought, heav precipitation events, and other extremes in the hydrological cycle.
the impact of ocean resolution in the unified forecast system ufs on the subseasonal forecast of extreme hydrological events, the objective of the project is to deliver 1 a scientifically validated version of the ufs with capabilities to run computationally efficient the ocean model mom6 at 0 25 degrees global resolution and 2 a set of 20 year reforecasts compliant with the subx developed forecast protocol that can be used for calibration of real time subseasonal forecasts, skillful subseasonal forecasts lead times ranging from 15 to 45 days will allow stakeholders to better prepare for extremes in the hydrological cycle such as the onset of drought conditions and heavy precipitation events the influence of sst anomalies on the atmospheric variability is at the core of weather climate nexus and how this influence affects the subseasonal forecast skill is still an open question this project works toward a scientifically validated version of the unified forecast system that contributes to the understanding of the role of small sst anomalies on atmospheric predictability
Environmental Modeling Center (EMC)
George Mason University
The objective of the project is to deliver 1) A scientifically validated version of the UFS with capabilities to run computationally efficient the ocean model MOM6 at 0.25 degrees global resolution and 2) a set
The goal of this project is to improve real-time quantitative precipitation estimates (QPE) in operational Multi-Radar Multi-Sensor (MRMS) data by mitigating a dry (wet) bias observed in stratiform (convective) rainfall.
Stephen Cocks
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Radar
JTTI
Multi-Radar/Multi-Sensor System (MRMS)
In a recent JTTI project (NOAA-OAR-OWAQ-2017-2005122), a new Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) algorithm (Q3DP; Zhang et al., 2017) utilizing specific attenuation (Ryzhkov et al. 2014; Wang et al., 2014) below, bright band corrected (Zhang and Qi, 2010) reflectivity (Z) above and within the melting layer (ML) and specific differential phase (Kdp) where hail was likely (Z > 50 dBZ) to estimate rain rates was implemented, evaluated and refined within a real-time (24-hrs/7 days a week) environment using the MRMS Research Testbed.
Improved MRMS system in NWS Operations
NCO
Patrick Marsh
CIMMS/University of Oklahoma
NA19OAR4590237
Water Extremes
Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2022
Radar
enhanced multi radar multi sensor dual polarization radar synthetic qpe, the goal of this project is to improve real time quantitative precipitation estimates qpe in operational multi radar multi sensor mrms data by mitigating a dry wet bias observed in stratiform convective rainfall, in a recent jtti project noaa oar owaq 2017 2005122 a new multi radar multi sensor mrms quantitative precipitation estimate qpe algorithm q3dp zhang et al 2017 utilizing specific attenuation ryzhkov et al 2014 wang et al 2014 below bright band corrected zhang and qi 2010 reflectivity z above and within the melting layer ml and specific differential phase kdp where hail was likely z > 50 dbz to estimate rain rates was implemented evaluated and refined within a real time 24 hrs 7 days a week environment using the mrms research testbed
NCEP Central Operations (NCO)
CIMMS/University of Oklahoma
The goal of this project is to improve real-time quantitative precipitation estimates (QPE) in operational Multi-Radar Multi-Sensor (MRMS) data by mitigating a dry (wet) bias observed in stratiform (convective) rainfall.
1
Hazard Services: National Center Evolve
Hardin
JTTI
check_circle
2019
Level 3
The goal of this project is to develop workflows that allow the Weather Prediction Center (WPC) to produce the Mesoscale Precipitation Discussion (MPD) and Excessive Rainfall Outlook (ERO), and the Ocean Prediction Center (OPC) to produce their High Seas Product (HSP) on the Advanced Weather Interactive Processing System (AWIPS) as well as developing a collaborative framework for National Centers of Environmental Prediction (NCEP) offices to share hazardous weather information among themselves and with Weather Forecast Offices (WFOs).
Nathan Hardin , Taylor Trogdon
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance
JTTI
Advanced Weather Interactive Processing System (AWIPS)
This work seeks to establish Hazard Services (HS) workflows for the Weather Prediction Center (WPC) and Ocean Prediction Center (OPC) to produce the Mesoscale Precipitation Discussion (MPD), the Excessive Rainfall Outlook (ERO), and the High Seas Warning (HSW) products on the Advanced Weather Interactive Processing System (AWIPS). This work also entails building a framework for sharing hazardous weather information between National Centers (NCs) and Weather Forecast Offices (WFOs).
Project outcomes will help improve warning decision making, and weather forecasts.
WPC
Daryl Kleist
CIRA/Colorado State University
NOAA/ESRL/GSD
NA19OAR4590228
Water Extremes
Colorado
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2023
Model & Forecast Guidance
hazard services national center evolve, the goal of this project is to develop workflows that allow the weather prediction center wpc to produce the mesoscale precipitation discussion mpd and excessive rainfall outlook ero and the ocean prediction center opc to produce their high seas product hsp on the advanced weather interactive processing system awips as well as developing a collaborative framework for national centers of environmental prediction ncep offices to share hazardous weather information among themselves and with weather forecast offices wfos, this work seeks to establish hazard services hs workflows for the weather prediction center wpc and ocean prediction center opc to produce the mesoscale precipitation discussion mpd the excessive rainfall outlook ero and the high seas warning hsw products on the advanced weather interactive processing system awips this work also entails building a framework for sharing hazardous weather information between national centers ncs and weather forecast offices wfos
Weather Prediction Center (WPC)
CIRA/Colorado State University, NOAA/ESRL/GSD
The goal of this project is to develop workflows that allow the Weather Prediction Center (WPC) to produce the Mesoscale Precipitation Discussion (MPD) and Excessive Rainfall Outlook (ERO), and the Ocean Prediction Center (OPC) to
0
Development and Testing of a GSI-based Multi-Scale EnKF System for Convection-Allowing Stand-Alone Regional FV3
Jung
JTTI
check_circle
2019
Level 3
The goal of this project is to develop and test a multi-scale GSI-based EnKF system that is suitable for regional models and capable of optimally combining observations representing large-scale background flow to convective-scale phenomena for the stand-alone (SAR) FV3-based Rapid Refresh Forecast System (RRFS).
Youngsun Jung, Ming Xue
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting
This research involves building and testing multi-scale data assimilation capabilities within the Gridpoint Statistical Interpolation (GSI) framework for use with the new SAR version of the FV3 model. Adding a self-consistent multi-scale EnKF system, will make it possible to use the SAR FV3 for convection-allowing model (CAM) forecasts over large territories.
This project will establish multi-scale data assimilation capabilities will advance SAR-FV3 and improve weather forecasts.
EMC
Jason Levit
University of Oklahoma
NA19OAR4590236
Relevant to All Hazards
Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2022
Data Assimilation and Ensembles, Dynamics and Nesting
development and testing of a gsi based multi scale enkf system for convection allowing stand alone regional fv3, the goal of this project is to develop and test a multi scale gsi based enkf system that is suitable for regional models and capable of optimally combining observations representing large scale background flow to convective scale phenomena for the stand alone sar fv3 based rapid refresh forecast system rrfs, this research involves building and testing multi scale data assimilation capabilities within the gridpoint statistical interpolation gsi framework for use with the new sar version of the fv3 model adding a self consistent multi scale enkf system will make it possible to use the sar fv3 for convection allowing model cam forecasts over large territories
Environmental Modeling Center (EMC)
University of Oklahoma
The goal of this project is to develop and test a multi-scale GSI-based EnKF system that is suitable for regional models and capable of optimally combining observations representing large-scale background flow to convective-scale phenomena for
3
End User Understanding of Uncertainty in Higher Spatial and Temporal Hazardous Weather Forecasts
LaDue
JTTI
check_circle
2019
Level 3
The goal of this project is to examine the influence of messaging in new forecasting products from NOAA's Storm Prediction Center (SPC) on the decision-making of emergency managers, school superintendents, fire captains, public works supervisors, and other end-users in the hours before a storm.
Daphne LaDue
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
This project extends the knowledge gained through research in VORTEX-Southeast to the broader United States that are at risk for severe thunderstorms and tornadoes. Many critical decisions are made in local jurisdictions in advance of severe weather. These decisions are generally considered as being made "by emergency managers," but that is an oversimplification of the complexity of decisions and actions taking
place. For example, schools, fire, and public works departments all make high-impact, costly decisions on whether to prepare for a forecasted event. Schools decide whether to delay start, end the school day early, or cancel after-school activities to keep students safe. These decisions impact the parents of young
children who must arrange for childcare. Fire and public works departments' decisions may mean that extra staff is required to retrieve and prepare equipment. Any preparation decision has a financial cost and some of these are borne widely throughout a community (e.g., school closures). These decisions are best
made 4-6 hours in advance - if not the day or night before - to allow for adequate preparation, but that is a time when forecast uncertainty is generally still quite high, particularly in terms of which specific cities or counties will be affected. Many of these decisions, whether made with forecast information from public or private sources, are anchored in the Storm Prediction Center's (SPC) Day 1 Outlook and
Mesoscale Convective Discussion products. The SPC is currently testing enhanced tools (SPC temporal guidance) that may assist both local forecasters and schools, fire, and public works in local decision-making. These enhanced, experimental tools help define the most likely times when severe weather might start in a particular city or county, when it is most likely to impact that city or county, and when it is most
likely to end.
The SPC is currently testing enhanced tools (SPC temporal guidance) that may assist both local forecasters and schools, fire, and public works in local decision-making. These enhanced, experimental tools help define the most likely times when severe weather might start in a particular city or county, when it is most likely to impact that city or county, and when it is most likely to end.This project will discover whether and how these new products from SPC affect the decisions made my these decision-makers when at risk for severe thunderstorms and tornadoes.
end user understanding of uncertainty in higher spatial and temporal hazardous weather forecasts, the goal of this project is to examine the influence of messaging in new forecasting products from noaa's storm prediction center spc on the decision making of emergency managers school superintendents fire captains public works supervisors and other end users in the hours before a storm, this project extends the knowledge gained through research in vortex southeast to the broader united states that are at risk for severe thunderstorms and tornadoes many critical decisions are made in local jurisdictions in advance of severe weather these decisions are generally considered as being made "by emergency managers " but that is an oversimplification of the complexity of decisions and actions taking place for example schools fire and public works departments all make high impact costly decisions on whether to prepare for a forecasted event schools decide whether to delay start end the school day early or cancel after school activities to keep students safe these decisions impact the parents of young children who must arrange for childcare fire and public works departments' decisions may mean that extra staff is required to retrieve and prepare equipment any preparation decision has a financial cost and some of these are borne widely throughout a community e g school closures these decisions are best made 4-6 hours in advance - if not the day or night before - to allow for adequate preparation but that is a time when forecast uncertainty is generally still quite high particularly in terms of which specific cities or counties will be affected many of these decisions whether made with forecast information from public or private sources are anchored in the storm prediction center's spc day 1 outlook and mesoscale convective discussion products the spc is currently testing enhanced tools spc temporal guidance that may assist both local forecasters and schools fire and public works in local decision making these enhanced experimental tools help define the most likely times when severe weather might start in a particular city or county when it is most likely to impact that city or county and when it is most likely to end
Storm Prediction Center (SPC)
University of Oklahoma
The goal of this project is to examine the influence of messaging in new forecasting products from NOAA's Storm Prediction Center (SPC) on the decision-making of emergency managers, school superintendents, fire captains, public works supervisors,
0
Enhancing the variational bias correction method to support the assimilation of satellite all-sky infrared brightness temperatures
Otkin
JTTI
check_circle
2019
Level 3
The goal of this project is to enhance short-range forecasts for severe weather features that are sensitive to errors in the cloud and water vapor initial conditions and support the development of the Rapid Refresh Forecast System
Jason Otkin
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Satellite & Remote Sensing Technologies, Verification & Validation
Researchers intend to advance the all-sky data assimilation capabilities of the operational Gridpoint Statistical Interpolation (GSI); assimilate all-sky infrared brightness temperatures from the Advanced Baseline Imager (ABI) sensor in the SAR-FV3; and evaluate the ability of higher-order nonlinear predictors to eliminate the bias from all-sky ABI brightness temperatures.
Assimilation of all-sky infrared brightness temperatures will improve forecast accuracy.
EMC
Li Bi (contractor), Archival Mehra (signatory)
CIMSS/University of Wisconsin
NA19OAR4590240
Relevant to All Hazards
Wisconsin
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2022
Data Assimilation and Ensembles, Dynamics and Nesting, Satellite & Remote Sensing Technologies, Verification & Validation
enhancing the variational bias correction method to support the assimilation of satellite all sky infrared brightness temperatures, the goal of this project is to enhance short range forecasts for severe weather features that are sensitive to errors in the cloud and water vapor initial conditions and support the development of the rapid refresh forecast system, researchers intend to advance the all sky data assimilation capabilities of the operational gridpoint statistical interpolation gsi assimilate all sky infrared brightness temperatures from the advanced baseline imager abi sensor in the sar fv3 and evaluate the ability of higher order nonlinear predictors to eliminate the bias from all sky abi brightness temperatures
Environmental Modeling Center (EMC)
CIMSS/University of Wisconsin
The goal of this project is to enhance short-range forecasts for severe weather features that are sensitive to errors in the cloud and water vapor initial conditions and support the development of the Rapid Refresh
2
Advancing Forecast Verification Efforts for Unified Forecast System Advanced Physics Testing using Spatial Verification Methods
Otkin
JTTI
check_circle
2019
Level 3
The goal of this project is to enhance the METplus verification system through development of new use-cases that employ spatial verification methods to assess the ability of different physics suites in CAMs to accurately simulate clouds.
Jason Otkin, Tara Jensen, Patrick Skinner
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Verification & Validation
JTTI
Convection Allowing Models (CAM), Model Evaluation Tools Plus (METPlus), Unified Forecast System (UFS)
To advance the METplus verification system, researchers are developing new use cases for evaluating the ability of various physics suites in current and future versions of operational convection-allowing models (CAMs) to produce accurate convection simulations. This will be completed using output from a suite of model simulations produced during the 2019 and 2020 Hazardous Weather Testbed Spring Experiments.
This project will improve weather forecasting by accurately simulating convection.
EMC
Nicole Kurkowski (Jason Levit signed TP)
CIMSS/University of Wisconsin
National Center for Atmospheric Research
CIMMS/University of Oklahoma
NA19OAR4590233
NA19OAR4590234
NA19OAR4590235
Severe Weather
Colorado, Oklahoma, Wisconsin
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2022
Atmospheric Physics, Verification & Validation
advancing forecast verification efforts for unified forecast system advanced physics testing using spatial verification methods, the goal of this project is to enhance the metplus verification system through development of new use cases that employ spatial verification methods to assess the ability of different physics suites in cams to accurately simulate clouds, to advance the metplus verification system researchers are developing new use cases for evaluating the ability of various physics suites in current and future versions of operational convection allowing models cams to produce accurate convection simulations this will be completed using output from a suite of model simulations produced during the 2019 and 2020 hazardous weather testbed spring experiments
Environmental Modeling Center (EMC)
CIMSS/University of Wisconsin, National Center for Atmospheric Research, CIMMS/University of Oklahoma
The goal of this project is to enhance the METplus verification system through development of new use-cases that employ spatial verification methods to assess the ability of different physics suites in CAMs to accurately simulate
0
Estimating Inundation Extent and Depth from National Water Model Outputs and High Resolution Topographic Data
Passalacqua
JTTI
check_circle
2019
Level 3
The goal of this project is to enhance the current forecast system by enabling the replacement of 10m terrain data with High Resolution
Topographic Data (HRT), where available, and thus alter the Geospatial Processing accordingly to generate lidar based products.
Paola Passalacqua, paolapassalacqua@gmail.com
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Visual Risk Communication
JTTI
National Water Model (NWM)
The existing flood inundation mapping system will be enhanced with a workflow GeoFlood product that will estimate flood inundation (extent and depth) from NWM and HRT data.
The need for rapid estimation of flood inundation extent and depth resulting from hurricanes will be met to save lives and property
NWC
Jacob Carley
University of Texas-Austin
NA19OAR4590229
Water Extremes
Texas
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2022
Dynamics and Nesting, Visual Risk Communication
estimating inundation extent and depth from national water model outputs and high resolution topographic data, the goal of this project is to enhance the current forecast system by enabling the replacement of 10m terrain data with high resolution topographic data hrt where available and thus alter the geospatial processing accordingly to generate lidar based products, the existing flood inundation mapping system will be enhanced with a workflow geoflood product that will estimate flood inundation extent and depth from nwm and hrt data
National Water Center (NWC)
University of Texas-Austin
The goal of this project is to enhance the current forecast system by enabling the replacement of 10m terrain data with High Resolution Topographic Data (HRT), where available, and thus alter the Geospatial Processing accordingly
4
Enhancing the prediction of landfalling hurricanes through improved data assimilation with the GSI-based ensemble-variational hybrid system and JEDI
Pu
JTTI
check_circle
2019
Level 3
The goal of this project is to enhance the capability of NCEP operational models in predicting landfalling hurricanes including their landfall time, location, inland evolution, intensity, and structure changes.
Zhaoxia Pu
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Radar, Satellite & Remote Sensing Technologies, Surface Observing Networks
JTTI
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Forecast System (GFS), Gridpoint Statistical Interpolation (GSI), Unified Forecast System (UFS)
The project aims to advance the science (1) by enhancing the assimilation of satellite-measured ocean surface wind and surface Mesonet observations on land and of ground-based Doppler radar (NEXRAD) observations; and (2) from incorporating new observations from the hurricane inner-core region, thereby strengthening predictions for intensity and track of landfalling hurricanes for current operational hurricane forecast models, such as the GFS-FV3.
Will improve the capability of models to predict the intensity and movement of hurricanes accurately and save lives and propoerties
EMC
Jacob Carley
University of Utah
NA19OAR4590239
Tropical Cyclones
Utah
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2022
Data Assimilation and Ensembles, Radar, Satellite & Remote Sensing Technologies, Surface Observing Networks
enhancing the prediction of landfalling hurricanes through improved data assimilation with the gsi based ensemble variational hybrid system and jedi, the goal of this project is to enhance the capability of ncep operational models in predicting landfalling hurricanes including their landfall time location inland evolution intensity and structure changes, the project aims to advance the science 1 by enhancing the assimilation of satellite measured ocean surface wind and surface mesonet observations on land and of ground based doppler radar nexrad observations and 2 from incorporating new observations from the hurricane inner core region thereby strengthening predictions for intensity and track of landfalling hurricanes for current operational hurricane forecast models such as the gfs fv3
Environmental Modeling Center (EMC)
University of Utah
The goal of this project is to enhance the capability of NCEP operational models in predicting landfalling hurricanes including their landfall time, location, inland evolution, intensity, and structure changes.
11
Laying the foundation for ensemble prediction and probabilistic hazard tool development for winter weather
Reeves
JTTI
check_circle
2019
Level 3
The goal of this research is to extend both the Warn-on-Forecast (WoF) and Forecasting A Continuum of Environmental Threats (FACETs) efforts into the winter season and provide a series of recommendations for how to proceed with a formal line of research for winter FACETs tool development and for applications of a WoF-type frequently-cycled system for winter weather.
Heather Reeves
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Probabilistic Weather Forecasting
JTTI
The project will extend existing work with convection-allowing ensemble prediction and Probabilistic Hazards Information (PHI) tool development from the warm season into the cool season, examining the winter hazards capabilities of the Warn-on-Forecast (WoF) initiative
Availability of PHI tools to predict a range of winter weather hazards for the safety of life and property.
WPC
Tabitha Huntemann
CIMMS/University of Oklahoma
NA19OAR4590230
Winter Weather
Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2022
Data Assimilation and Ensembles, Probabilistic Weather Forecasting
laying the foundation for ensemble prediction and probabilistic hazard tool development for winter weather, the goal of this research is to extend both the warn on forecast wof and forecasting a continuum of environmental threats facets efforts into the winter season and provide a series of recommendations for how to proceed with a formal line of research for winter facets tool development and for applications of a wof type frequently cycled system for winter weather, the project will extend existing work with convection allowing ensemble prediction and probabilistic hazards information phi tool development from the warm season into the cool season examining the winter hazards capabilities of the warn on forecast wof initiative
Weather Prediction Center (WPC)
CIMMS/University of Oklahoma
The goal of this research is to extend both the Warn-on-Forecast (WoF) and Forecasting A Continuum of Environmental Threats (FACETs) efforts into the winter season and provide a series of recommendations for how to proceed
2
Enhanced tools for high-resolution ensemble development and verification
Romine
JTTI
check_circle
2019
Level 3
The goal of this project is to mature convection-permitting (CP) ensembles and transition appropriate components into the FV3 model and METplus framework to advance NOAA's forecast model development approach and verification capabilities, and specifically toward accelerated development of NOAA's Rapid Refresh Forecast System (RRFS).
Glen Romine
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Verification & Validation
JTTI
Finite-Volume Cubed-Sphere Dynamical Core (FV3), High-Resolution Rapid Refresh (HRRR), Model Evaluation Tools Plus (METPlus), Rapid Refresh (RAP), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
This research will advance tools in support of NOAA's development of the Rapid Refresh Forecast System (RRFS). In particular, to transition components into the FV3 model and the METplus framework, the project will test previously developed tools of convection-permitting ensembles combined with current MET verification capabilities.
This transition will benefit operational and research stakeholders by contributing diagnostic capabilities for future cumulus parameterization ensemble assessment and for evidence-based decision making.
EMC
James Nelson (WPC)
National Center for Atmospheric Research
NA19OAR4590232
Relevant to All Hazards
Colorado
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2022
Data Assimilation and Ensembles, Dynamics and Nesting, Verification & Validation
enhanced tools for high resolution ensemble development and verification, the goal of this project is to mature convection permitting cp ensembles and transition appropriate components into the fv3 model and metplus framework to advance noaa's forecast model development approach and verification capabilities and specifically toward accelerated development of noaa's rapid refresh forecast system rrfs, this research will advance tools in support of noaa's development of the rapid refresh forecast system rrfs in particular to transition components into the fv3 model and the metplus framework the project will test previously developed tools of convection permitting ensembles combined with current met verification capabilities
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
The goal of this project is to mature convection-permitting (CP) ensembles and transition appropriate components into the FV3 model and METplus framework to advance NOAA's forecast model development approach and verification capabilities, and specifically toward
2
Advancing the Direct Assimilation of Radar Observations to Improve Convective Scale Numerical Weather Prediction through Optimizing the Combined Use of Static and Ensemble Covariances, the Additive Perturbations, and the Assimilation Frequency in the Hybrid EnVar and through Integration with JEDI
Wang
JTTI
check_circle
2019
Level 3
The goal of this project is to extend the Convective Scale Hybrid EnVar for rapidly updated DA throughout the Continental United States, experiment and test to optimize the radar DA, document the ability of the extended DA, and transition all of this within the FV3-SAR model for seamless integration with the Unified Forecast System.
Xuguang Wang
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Radar
JTTI
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Unified Forecast System (UFS)
To advance and extend the next-generation operational convective scale data assimilation (DA) system, researchers will extend the Convective Scale Hybrid EnVar for rapidly updated DA throughout the Continental United States; experiment and test to optimize the radar DA; document the ability of the extended DA; and transition all of this within the FV3-SAR model for seamless integration with the Unified Forecast System.
The project will benefit both the NOAA operational centers and the research community to become fully engaged under a unified modeling framework and significantly improve forecasts for the safety of life and property.
EMC
Daryl Kleist
University of Oklahoma
NA19OAR4590231
Relevant to All Hazards
Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2019 - August 2023
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Radar
advancing the direct assimilation of radar observations to improve convective scale numerical weather prediction through optimizing the combined use of static and ensemble covariances the additive perturbations and the assimilation frequency in the hybrid envar and through integration with jedi, the goal of this project is to extend the convective scale hybrid envar for rapidly updated da throughout the continental united states experiment and test to optimize the radar da document the ability of the extended da and transition all of this within the fv3 sar model for seamless integration with the unified forecast system, to advance and extend the next generation operational convective scale data assimilation da system researchers will extend the convective scale hybrid envar for rapidly updated da throughout the continental united states experiment and test to optimize the radar da document the ability of the extended da and transition all of this within the fv3 sar model for seamless integration with the unified forecast system
Environmental Modeling Center (EMC)
University of Oklahoma
The goal of this project is to extend the Convective Scale Hybrid EnVar for rapidly updated DA throughout the Continental United States, experiment and test to optimize the radar DA, document the ability of the
6
Use of Ocean Stability Data and Machine Learning to Improve Tropical Cyclone Situational Awareness and NHC Statistical-Dynamical Intensity Guidance
Chirokova
JTTI
check_circle
2020
Level 3
The goal of this project is to develop an improved version of Tdy (ITdy) and test ITdy as an alternative to OHC for qualitative use by forecasters, generalize NHC statistical TC-intensity forecast models to better represent TC-ocean interaction and use ITdy and salinity as input and test advanced ML techniques to improve the NHC Intensity Guidance Suite.
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Dynamics and Nesting, Model & Forecast Guidance, Satellite & Remote Sensing Technologies
JTTI
Tropical cyclone (TC) intensity changes are influenced by the atmospheric environment surrounding the storm, energy exchanges with the underlying ocean or land surface, and internal processes such as eyewall replacement cycles. The National Hurricane Center (NHC) uses dynamical, statistical-dynamical, and consensus models as guidance for their intensity forecasts in the Atlantic and East Pacific basins. NHC also uses satellite imagery, sea surface temperature (SST), and ocean heat content (OHC) analyses for situational awareness. Although the Hurricane Weather Research and Forecast (HWRF) model has generally provided more accurate intensity forecasts than the statistical-dynamical intensity forecast models, statistical models increase the skill of consensus models, and the statistical classification models are more skillful in predicting TC rapid intensification (RI, a significant increase in TC intensity in a short period of time), a particularly challenging problem and one of the NHC's highest priorities.
Reduce property losses and loss of life
NHC
Brian Zachry (NHC)
CIRA/Colorado State University
NOAA/AOML
NA20OAR4590353
Tropical Cyclones
Colorado, Florida
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2024
Artificial Intelligence (AI) / Machine Learning (ML), Dynamics and Nesting, Model & Forecast Guidance, Satellite & Remote Sensing Technologies
use of ocean stability data and machine learning to improve tropical cyclone situational awareness and nhc statistical dynamical intensity guidance, the goal of this project is to develop an improved version of tdy itdy and test itdy as an alternative to ohc for qualitative use by forecasters generalize nhc statistical tc intensity forecast models to better represent tc ocean interaction and use itdy and salinity as input and test advanced ml techniques to improve the nhc intensity guidance suite, tropical cyclone tc intensity changes are influenced by the atmospheric environment surrounding the storm energy exchanges with the underlying ocean or land surface and internal processes such as eyewall replacement cycles the national hurricane center nhc uses dynamical statistical dynamical and consensus models as guidance for their intensity forecasts in the atlantic and east pacific basins nhc also uses satellite imagery sea surface temperature sst and ocean heat content ohc analyses for situational awareness although the hurricane weather research and forecast hwrf model has generally provided more accurate intensity forecasts than the statistical dynamical intensity forecast models statistical models increase the skill of consensus models and the statistical classification models are more skillful in predicting tc rapid intensification ri a significant increase in tc intensity in a short period of time a particularly challenging problem and one of the nhc's highest priorities
National Hurricane Center (NHC)
CIRA/Colorado State University, NOAA/AOML
The goal of this project is to develop an improved version of Tdy (ITdy) and test ITdy as an alternative to OHC for qualitative use by forecasters, generalize NHC statistical TC-intensity forecast models to better
0
Generating Storm Surge Hazards using Hazard Services
Trogdon
JTTI
check_circle
2020
Level 3
The goal of this project is to develop a workflow that allows the National Hurricane Center (NHC) to produce the Storm Surge Watch/Warning (SSWW) on AWIPS2.
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Visualizations
JTTI
Advanced Weather Interactive Processing System (AWIPS)
Over the past decade the National Hurricane Center (NHC) has prioritized improving communication of the storm surge hazard, evidenced by the operational implementation of the Potential Storm Surge Flooding Graphic (PSSFG) and Storm Surge Watch/Warning (SSWW). While this targeted strategy has undoubtedly mitigated loss of life and property, recent storms have elucidated limitations to the current SSWW workflow using the Advanced Weather Interactive Processing System's (AWIPS) Graphical Forecast Editor (GFE).
Reduce loss of life due to storm surge
NHC
Brian Zachry (NHC)
CIRA/Colorado State University
NA20OAR4590344
Tropical Cyclones
Colorado
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2024
Model & Forecast Guidance, Visualizations
generating storm surge hazards using hazard services, the goal of this project is to develop a workflow that allows the national hurricane center nhc to produce the storm surge watch warning ssww on awips2, over the past decade the national hurricane center nhc has prioritized improving communication of the storm surge hazard evidenced by the operational implementation of the potential storm surge flooding graphic pssfg and storm surge watch warning ssww while this targeted strategy has undoubtedly mitigated loss of life and property recent storms have elucidated limitations to the current ssww workflow using the advanced weather interactive processing system's awips graphical forecast editor gfe
National Hurricane Center (NHC)
CIRA/Colorado State University
The goal of this project is to develop a workflow that allows the National Hurricane Center (NHC) to produce the Storm Surge Watch/Warning (SSWW) on AWIPS2.
0
Communicating Forecast Uncertainty and Probabilistic Information: Expanding and Embedding Social and Behavioral Data in National Weather Service Operations
Ripberger
JTTI
check_circle
2020
Level 3
The goal of this project is to improve forecast communication and decision support during extreme weather and high impact weather events by building upon the Severe Weather and Society Survey (Wx Survey) and the Severe Weather and Society Dashboard (Wx Dash), which provide generalizable, longitudinal, and experimental data on how members of the Continental U.S. (CONUS) public receive, understand, and respond to uncertainty and probabilistic information in severe weather forecasts and warnings.
This project will improve forecast communication and decision support during extreme weather and high impact weather events by building upon the Severe Weather and Society Survey (Wx Survey) and the Severe Weather and Society Dashboard (Wx Dash), which provide generalizable, longitudinal, and experimental data on how members of the Continental U.S. (CONUS) public receive, understand, and respond to uncertainty and probabilistic information in severe weather forecasts and warnings.
This database of Weather and Society data is meant to help members of the weather enterprise answer basic questions about the people in the communities they serve, which is a necessary step toward customizing and improving risk communication, education, and decision support to meet the characteristics of the community. It is also meant to help administrators and researchers identify differences and best practices between communities and/or monitor changes within communities as enterprise members experiment with new education and communication strategies. The Weather and Society Dashboard (WxDash) assists in achieving these goals by providing enterprise members, administrators, and researchers an opportunity to use and interact with the database of community statistics.
communicating forecast uncertainty and probabilistic information expanding and embedding social and behavioral data in national weather service operations, the goal of this project is to improve forecast communication and decision support during extreme weather and high impact weather events by building upon the severe weather and society survey wx survey and the severe weather and society dashboard wx dash which provide generalizable longitudinal and experimental data on how members of the continental u s conus public receive understand and respond to uncertainty and probabilistic information in severe weather forecasts and warnings, this project will improve forecast communication and decision support during extreme weather and high impact weather events by building upon the severe weather and society survey wx survey and the severe weather and society dashboard wx dash which provide generalizable longitudinal and experimental data on how members of the continental u s conus public receive understand and respond to uncertainty and probabilistic information in severe weather forecasts and warnings
University of Oklahoma
The goal of this project is to improve forecast communication and decision support during extreme weather and high impact weather events by building upon the Severe Weather and Society Survey (Wx Survey) and the Severe
11
Applications of METplus to Subseasonal Climate Outlooks, Multi-Model Ensembles, Process Studies, and Extremes
Jensen
JTTI
check_circle
2020
Level 3
The goal of this project is to adapt and implement the extended Model Evaluation Tools (METplus) verification and diagnostic framework to address the specialized needs of CPC's subseasonal and seasonal forecasting.
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance, Verification & Validation
JTTI
Model Evaluation Tools Plus (METPlus), Unified Forecast System (UFS)
The goal of this project is to use both traditional and object-based verification methods available in METplus to evaluate seasonal and subseasonal guidance, to assess 1) how well models replicate climatological characteristics; 2) how well models forecasts of spatial features match observed features; and 3) how well models and post-processed tools forecast hazards and extremes using various types of thresholds.
Improve long range planning
CPC
Dan Collins (CPC)
National Center for Atmospheric Research
NOAA/NWS/CPC
NA20OAR4590346
Relevant to All Hazards
Colorado, Maryland
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2022
Data Assimilation and Ensembles, Model & Forecast Guidance, Verification & Validation
applications of metplus to subseasonal climate outlooks multi model ensembles process studies and extremes, the goal of this project is to adapt and implement the extended model evaluation tools metplus verification and diagnostic framework to address the specialized needs of cpc's subseasonal and seasonal forecasting, the goal of this project is to use both traditional and object based verification methods available in metplus to evaluate seasonal and subseasonal guidance to assess 1 how well models replicate climatological characteristics 2 how well models forecasts of spatial features match observed features and 3 how well models and post processed tools forecast hazards and extremes using various types of thresholds
Climate Prediction Center (CPC)
National Center for Atmospheric Research, NOAA/NWS/CPC
The goal of this project is to adapt and implement the extended Model Evaluation Tools (METplus) verification and diagnostic framework to address the specialized needs of CPC's subseasonal and seasonal forecasting.
0
Deep Learning for Operational Identification and Prediction of Synoptic-Scale Fronts
McGovern
JTTI
check_circle
2020
Level 3
The goal of this project is to transition to operations an automated machine learning (ML) approach for enhancing frontal identification and analysis that will be used in a human-in-the-loop manner by forecasters.
We will develop an automated machine learning (ML) approach for frontal analysis that will be used in a human-in-the-loop manner by forecasters. ML will provide a first-guess map for warm, cold, occluded, and stationary fronts, as well as drylines over the region covered by the Unified Surface Analysis, which is the Northern Hemisphere from 130E eastward to 10E from the Equator to 80N. The human forecasters will make use of the first-guess map to significantly reduce the forecaster time needed to produce the final map.
Improved forecasts of synoptic-scale weather phenomena across the Northern Hemisphere.
WPC
Jim Nelson (WPC)
University of Oklahoma;
Central Michigan University
deep learning for operational identification and prediction of synoptic scale fronts, the goal of this project is to transition to operations an automated machine learning ml approach for enhancing frontal identification and analysis that will be used in a human in the loop manner by forecasters, we will develop an automated machine learning ml approach for frontal analysis that will be used in a human in the loop manner by forecasters ml will provide a first guess map for warm cold occluded and stationary fronts as well as drylines over the region covered by the unified surface analysis which is the northern hemisphere from 130e eastward to 10e from the equator to 80n the human forecasters will make use of the first guess map to significantly reduce the forecaster time needed to produce the final map
Weather Prediction Center (WPC)
University of Oklahoma;, Central Michigan University
The goal of this project is to transition to operations an automated machine learning (ML) approach for enhancing frontal identification and analysis that will be used in a human-in-the-loop manner by forecasters.
3
Integration of a road temperature analysis and forecast into NWS operations
Reeves
JTTI
check_circle
2020
Level 3
The goal of this project is to transition a new algorithm that provides a 0-100% probability estimate that untreated roads are subfreezing (TRprob) into a development version of the WPC's Winter Storm Severity Index (WSSI), to advance TRprob scientifically by adapting it to work with ensemble output, and to include specific threats (i.e. accumulating snow, accumulating ice, black ice, etc).
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Probabilistic Weather Forecasting
JTTI
The aim of this project is to fold the TRprob algorithm into the Winter Storm Severity Index (WSSI) product supported by the NCEP/Weather Prediction Center (WPC) as a new travel hazards component of the WSSI. The WSSI advances the warning/advisory system from its current binary form that is based primarily on total snow accumulation, to warnings/advisories that are graded according to the expected impact for a range of hazards (not just those dependent on high snow accumulations). This allows for a more impacts-based probabilistic approach to watches/advisories.
Saving lives
WPC
Jim Nelson (WPC)
CIMMS/University of Oklahoma;
CIRES/University of Colorado
NA20OAR4590348
NA20OAR4590349
Winter Weather
Colorado, Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2023
Data Assimilation and Ensembles, Probabilistic Weather Forecasting
integration of a road temperature analysis and forecast into nws operations, the goal of this project is to transition a new algorithm that provides a 0 100% probability estimate that untreated roads are subfreezing trprob into a development version of the wpc's winter storm severity index wssi to advance trprob scientifically by adapting it to work with ensemble output and to include specific threats i e accumulating snow accumulating ice black ice etc, the aim of this project is to fold the trprob algorithm into the winter storm severity index wssi product supported by the ncep weather prediction center wpc as a new travel hazards component of the wssi the wssi advances the warning advisory system from its current binary form that is based primarily on total snow accumulation to warnings advisories that are graded according to the expected impact for a range of hazards not just those dependent on high snow accumulations this allows for a more impacts based probabilistic approach to watches advisories
Weather Prediction Center (WPC)
CIMMS/University of Oklahoma;, CIRES/University of Colorado
The goal of this project is to transition a new algorithm that provides a 0-100% probability estimate that untreated roads are subfreezing (TRprob) into a development version of the WPC's Winter Storm Severity Index (WSSI),
4
Generating calibrated forecast guidance for severe weather beyond day 1
Schumacher
JTTI
check_circle
2020
Level 3
The goal of this project is to use a newly developed ensemble reforecast dataset, historical observations of severe weather, and machine learning algorithms to develop a guidance tool for Storm Prediction Center (SPC) forecasters to use in the preparation of their Convective Outlooks on forecast days 2-8.
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Reanalysis & Reforecasting, Model & Forecast Guidance, Probabilistic Weather Forecasting
JTTI
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
In this project, we will extend our severe weather ML model to post-process global- model output and produce CONUS-wide forecasts of severe weather to aid SPC forecasters at days 2-8 (36-204 hours). We will leverage the new Unified Forecast System FV3-based Global Ensemble Forecast System, set to become operational in 2020, as input to our ML model and generate probabilistic severe weather forecasts. We will objectively and subjectively evaluate the probabilistic forecasts at the Hazardous Weather Testbed Spring Forecast Experiment and with SPC forecasters.
The most likely use of the probabilistic forecast products generated from this project will be as guidance to SPC forecasters when generating convective outlooks on days 3-8, when calibrated. Reliable guidance is currently lacking for this forecast period.
SPC
Israel Jirak (SPC)
CIRA/Colorado State University
NA20OAR4590350
Severe Weather
Colorado
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2024
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Reanalysis & Reforecasting, Model & Forecast Guidance, Probabilistic Weather Forecasting
generating calibrated forecast guidance for severe weather beyond day 1, the goal of this project is to use a newly developed ensemble reforecast dataset historical observations of severe weather and machine learning algorithms to develop a guidance tool for storm prediction center spc forecasters to use in the preparation of their convective outlooks on forecast days 2 8, in this project we will extend our severe weather ml model to post process global model output and produce conus wide forecasts of severe weather to aid spc forecasters at days 2 8 36 204 hours we will leverage the new unified forecast system fv3 based global ensemble forecast system set to become operational in 2020 as input to our ml model and generate probabilistic severe weather forecasts we will objectively and subjectively evaluate the probabilistic forecasts at the hazardous weather testbed spring forecast experiment and with spc forecasters
Storm Prediction Center (SPC)
CIRA/Colorado State University
The goal of this project is to use a newly developed ensemble reforecast dataset, historical observations of severe weather, and machine learning algorithms to develop a guidance tool for Storm Prediction Center (SPC) forecasters to
2
Expanding Chemical Data Assimilation System to Support NOAA Unified Forecast System
Kim
JTTI
check_circle
2020
Level 3
The goal of this project is to start from the next generation data assimilation system to be used by NOAA/NWS/NCEP, JEDI, and implement the full-chemistry aerosol assimilation algorithm (Tang et al., 2017), which includes the exact aerosol species used in NAQFC and future full- chemistry CAM-CMAQ.
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Reanalysis & Reforecasting
JTTI
Community Atmosphere Model (CAM), Community Multiscale Air Quality (CMAQ) Model, NOAA's National Air Quality Forecasting Capability (NAQFC), Unified Forecast System (UFS)
In this project, we plan to start from the next generation data assimilation system to be used by NOAA/NWS/NCEP, JEDI, and implement the full-chemistry aerosol assimilation algorithm (Tang et al., 2017), which includes the exact aerosol species used in NAQFC and future full- chemistry CAM-CMAQ. It is expected that this work will significantly improve the first-day aerosol forecast of NCEP chemical/aerosol models by adjusting their initial conditions. We plan to distribute the work among OAR (ARL and ESRL) and NCEP teams.
provide more accurate air quality information to users
EMC
Daryl Kleist (EMC)
CISESS/University of Maryland;
CIRES/University of Colorado
NA20OAR4590352
NA20OAR4590351
Relevant to All Hazards
Colorado, Maryland
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2023
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Reanalysis & Reforecasting
expanding chemical data assimilation system to support noaa unified forecast system, the goal of this project is to start from the next generation data assimilation system to be used by noaa nws ncep jedi and implement the full chemistry aerosol assimilation algorithm tang et al 2017 which includes the exact aerosol species used in naqfc and future full chemistry cam cmaq, in this project we plan to start from the next generation data assimilation system to be used by noaa nws ncep jedi and implement the full chemistry aerosol assimilation algorithm tang et al 2017 which includes the exact aerosol species used in naqfc and future full chemistry cam cmaq it is expected that this work will significantly improve the first day aerosol forecast of ncep chemical aerosol models by adjusting their initial conditions we plan to distribute the work among oar arl and esrl and ncep teams
Environmental Modeling Center (EMC)
CISESS/University of Maryland;, CIRES/University of Colorado
The goal of this project is to start from the next generation data assimilation system to be used by NOAA/NWS/NCEP, JEDI, and implement the full-chemistry aerosol assimilation algorithm (Tang et al., 2017), which includes the
0
Products to Guide Impact-Based Flash Flood Warnings in the National Weather Service
Vergara
JTTI
check_circle
2020
Level 3
The goal of this project is to build upon flash flood probabilistic products that were developed and tested during the Hydrometeorological Testbed Hydro Experiment (HMT-Hydro) during the summers of 2018 and 2019.
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Probabilistic Weather Forecasting, Visualizations
JTTI
The project will build upon flash flood probabilistic products that were developed and tested during the Hydrometeorological Testbed Hydro Experiment (HMT-Hydro) during the summers of 2018 and 2019. Project scientists worked with NWS forecasters to evaluate newly developed probability of receiving a local storm report (LSR), minor flooding, moderate flooding, and major flooding products. The methodology of summarizing FLASH outputs for prior events to yield probabilistic products will be repeated here, but the LSRs will be further segregated into base level, considerable, and catastrophic classes, in accordance with the new flash flood damage threat tags
This project advances the Flooded Locations and Simulated Hydrographs (FLASH) system used
operationally by NWS forecasters for issuing flash flood impact-based warnings (IBWs). Specifically, it develops probabilistic products specific to geographic regimes and seasons that succinctly communicate the likelihood of experiencing base-level, considerable, and catastrophic flash-flooding events. The project also provides map layers to be ingested in their operational AWIPS2 software to highlight the specificity of the impacts as they relate to road flooding, debris flows and flash floods emanating from burn scars, and
flooding in campgrounds.
WPC
Kate Abshire (AFS)
CIMMS/University of Oklahoma;
NOAA/OAR/NSSL
NA20OAR4590354
Water Extremes
Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2024
Model & Forecast Guidance, Probabilistic Weather Forecasting, Visualizations
products to guide impact based flash flood warnings in the national weather service, the goal of this project is to build upon flash flood probabilistic products that were developed and tested during the hydrometeorological testbed hydro experiment hmt hydro during the summers of 2018 and 2019, the project will build upon flash flood probabilistic products that were developed and tested during the hydrometeorological testbed hydro experiment hmt hydro during the summers of 2018 and 2019 project scientists worked with nws forecasters to evaluate newly developed probability of receiving a local storm report lsr minor flooding moderate flooding and major flooding products the methodology of summarizing flash outputs for prior events to yield probabilistic products will be repeated here but the lsrs will be further segregated into base level considerable and catastrophic classes in accordance with the new flash flood damage threat tags
Weather Prediction Center (WPC)
CIMMS/University of Oklahoma;, NOAA/OAR/NSSL
The goal of this project is to build upon flash flood probabilistic products that were developed and tested during the Hydrometeorological Testbed Hydro Experiment (HMT-Hydro) during the summers of 2018 and 2019.
5
Winter Storm Severity Index: Improving Storm Readiness through Severity and Social Impact Forecasting
Hogan-Carr
JTTI
check_circle
2020
Level 3
The goal of this project is to gather feedback from a number of focus groups (public and private) to assess the needed improvements to the design (colors, wording, presentation) of WSSI enabling partners to quickly ingest winter weather impact information.
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Forecast Product Usability & Design, "Risk Categories, Indices, or Levels", "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
JTTI
The WSSI experimental product in development by WPC has emerged in response to user needs for easily consumable forecast information that identifies the multiple impacts and relative severity of an impending storm. The project will commence with a series of focus groups: two rounds of focus groups (in-person and virtual) in each of the partnering WFO regions to test the ways in which different professional users understand, respond to and use the WSSI in different geographic locations. At each location, the project team will meet with and interview forecasters from the WFO area to get their input and feedback on the product as well. The project will also host several web-based focus groups with industry-specific users (i.e., aviation, transportation) and a focus group with key national center forecasters at WPC's office in College Park. The project will deliver recommendations for the design and delivery of WSSI to improve its utility for a range of users.
The recommendations and information obtained through this project will result in an improved, user-tested Winter Storm Severity Index product that can help consolidate forecast information and drive communication about the severity of threat and areas of highest focus for responding to winter weather events. In particular, the emergency managers and other partners will be able to use this new WSSI product to better communicate anticipated impacts from winter weather hazards and trigger emergency processes and plans.
WPC
Jim Nelson (WPC)
Nurture Nature Center
NA20OAR4590355
Winter Weather
Pennsylvania
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2023
Forecast Product Usability & Design, "Risk Categories, Indices, or Levels", "Social Science (SBES)", Visual Risk Communication
winter storm severity index improving storm readiness through severity and social impact forecasting, the goal of this project is to gather feedback from a number of focus groups public and private to assess the needed improvements to the design colors wording presentation of wssi enabling partners to quickly ingest winter weather impact information, the wssi experimental product in development by wpc has emerged in response to user needs for easily consumable forecast information that identifies the multiple impacts and relative severity of an impending storm the project will commence with a series of focus groups two rounds of focus groups in person and virtual in each of the partnering wfo regions to test the ways in which different professional users understand respond to and use the wssi in different geographic locations at each location the project team will meet with and interview forecasters from the wfo area to get their input and feedback on the product as well the project will also host several web based focus groups with industry specific users i e aviation transportation and a focus group with key national center forecasters at wpc's office in college park the project will deliver recommendations for the design and delivery of wssi to improve its utility for a range of users
Weather Prediction Center (WPC)
Nurture Nature Center
The goal of this project is to gather feedback from a number of focus groups (public and private) to assess the needed improvements to the design (colors, wording, presentation) of WSSI enabling partners to quickly
2
0-3 hour tornado prediction using the Warn on Forecast System and machine learning
Snook
JTTI
check_circle
2020
Level 3
The goal of this project is to develop a machine learning (ML) prediction system that produces skillful automated predictions of 0-3 h probability of tornado occurrence using the Warn on Forecast System (WoFS).
We will develop a machine learning (ML) prediction system that produces skillful automated predictions of 0-3 h probability of tornado occurrence using the Warn on Forecast System (WoFS). To maximize the potential operational impact of the project, this system will be based upon convolutional neural networks (CNN), which have been demonstrated to produce highly skillful predictions of 0-1 h tornado occurrence using observed radar and model sounding data (Lagerquist et al. 2019b).
Improved short-term tornado prediction capabilities as a result of this project will afford individuals and businesses more opportunities to protect life and property, mitigating the impact of tornadoes. The use of radar observations in the training model-based machine learning (ML) systems pioneered during this project will also inform future meteorological ML applications.
SPC
Israel Jirak (SPC)
University of Oklahoma;
National Center for Atmospheric Research
0 3 hour tornado prediction using the warn on forecast system and machine learning, the goal of this project is to develop a machine learning ml prediction system that produces skillful automated predictions of 0 3 h probability of tornado occurrence using the warn on forecast system wofs, we will develop a machine learning ml prediction system that produces skillful automated predictions of 0 3 h probability of tornado occurrence using the warn on forecast system wofs to maximize the potential operational impact of the project this system will be based upon convolutional neural networks cnn which have been demonstrated to produce highly skillful predictions of 0 1 h tornado occurrence using observed radar and model sounding data lagerquist et al 2019b
Storm Prediction Center (SPC)
University of Oklahoma;, National Center for Atmospheric Research
The goal of this project is to develop a machine learning (ML) prediction system that produces skillful automated predictions of 0-3 h probability of tornado occurrence using the Warn on Forecast System (WoFS).
2
Flow-dependent machine learning based post-processing of convection allowing ensembles to provide convective outlooks of severe weather hazards
Johnson
JTTI
check_circle
2020
Level 3
The goal of this project is to develop a suite of flow-dependent Random Forest (RF) models that are applicable to post-processing FV3-based convection allowing ensemble (CAE) forecast products into 4-hourly convective outlook guidance for individual severe and significant-severe weather hazards at 12-36 forecast hour lead times.
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing
JTTI
Finite-Volume Cubed-Sphere Dynamical Core (FV3)
Convection Allowing Ensemble (CAE) forecasts are one of the primary tools used by op- erational forecasters at the Storm Prediction Center (SPC) to provide the public and NWS offices with convective outlooks that concisely communicate and quantify the threat of severe convective weather. However, significant systematic biases and variation in forecast accuracy in different weather patterns exist in the CAE forecasts. These biases and flow-dependent errors need to be corrected for with statistical post-processing and/or the intuition of the forecaster before using them in the operational forecast process.
improved situational awareness of longer range convective weather
SPC
Israel Jirak (SPC)
University of Oklahoma
NA20OAR4590358
Severe Weather
Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2024
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing
flow dependent machine learning based post processing of convection allowing ensembles to provide convective outlooks of severe weather hazards, the goal of this project is to develop a suite of flow dependent random forest rf models that are applicable to post processing fv3 based convection allowing ensemble cae forecast products into 4 hourly convective outlook guidance for individual severe and significant severe weather hazards at 12 36 forecast hour lead times, convection allowing ensemble cae forecasts are one of the primary tools used by op erational forecasters at the storm prediction center spc to provide the public and nws offices with convective outlooks that concisely communicate and quantify the threat of severe convective weather however significant systematic biases and variation in forecast accuracy in different weather patterns exist in the cae forecasts these biases and flow dependent errors need to be corrected for with statistical post processing and or the intuition of the forecaster before using them in the operational forecast process
Storm Prediction Center (SPC)
University of Oklahoma
The goal of this project is to develop a suite of flow-dependent Random Forest (RF) models that are applicable to post-processing FV3-based convection allowing ensemble (CAE) forecast products into 4-hourly convective outlook guidance for individual
2
Improving land-surface flux partitioning in operational short range forecasts through integration of NOAA weather and water models
Cabell
JTTI
check_circle
2020
Level 3
The goal of this project is to develop general, foundational model coupling infrastructure within the UFS framework that will support the utilization of hydrologically-enhanced land surface representations in coupled land-atmosphere data assimilation and forecast systems.
David Gochis/Ryan Cabell, Terra Ladwig/Jason English
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles
JTTI
High-Resolution Rapid Refresh (HRRR), National Water Model (NWM), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
Under this project the new fully-coupled RRFS/NWM system will be developed and rigorously evaluated. The model will be deployed for several limited area, limited duration case studies to explore the role of the hydrologically-enhanced physics afforded by the NWM on joint precipitation, streamflow and flood inundation prediction skill. Then the new RRFS/NWM system will be deployed over the full CONUS in a real-time, continuously-cycling demonstration mode. Forecasts from the real-time demonstration will be evaluated and contrasted with the currently operational HRRR and NWM systems.
improved hydrological prediction
EMC
Michael Barlage (EMC)
National Center for Atmospheric Research;
University of Colorado
NA20OAR4590359
NA20OAR4590360
Water Extremes
Colorado
Joint Technology Transfer Initiative (JTTI)
September 2020 - September 2024
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles
improving land surface flux partitioning in operational short range forecasts through integration of noaa weather and water models, the goal of this project is to develop general foundational model coupling infrastructure within the ufs framework that will support the utilization of hydrologically enhanced land surface representations in coupled land atmosphere data assimilation and forecast systems, under this project the new fully coupled rrfs nwm system will be developed and rigorously evaluated the model will be deployed for several limited area limited duration case studies to explore the role of the hydrologically enhanced physics afforded by the nwm on joint precipitation streamflow and flood inundation prediction skill then the new rrfs nwm system will be deployed over the full conus in a real time continuously cycling demonstration mode forecasts from the real time demonstration will be evaluated and contrasted with the currently operational hrrr and nwm systems
Environmental Modeling Center (EMC)
National Center for Atmospheric Research;, University of Colorado
The goal of this project is to develop general, foundational model coupling infrastructure within the UFS framework that will support the utilization of hydrologically-enhanced land surface representations in coupled land-atmosphere data assimilation and forecast systems.
0
Combining A Fuzzy Clustering Approach With the National Blend of Models (NBM) to Facilitate Scenario-Driven Products for Extreme Weather
Colle
JTTI
check_circle
2020
Level 3
The goal of this project is to combine operational ensemble clustering approaches developed at Stony Brook University with the National Blend of Models (NBM).
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Verification & Validation, Visualizations
JTTI
National Blend of Models (NBM)
This project will combine operational ensemble clustering approaches developed at Stony Brook University with the National Blend of Models (NBM) in collaboration with partners at the Environmental Modeling Center (EMC), Weather Prediction Center (WPC), Ocean Prediction Center (OPC), and Meteorological Development Lab (MDL).
improve forecast and communication of impacts
MDL
Jim Nelson (WPC)
Stony Brook University
NA20OAR4590361
Relevant to All Hazards
New York
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2023
Data Assimilation and Ensembles, Verification & Validation, Visualizations
combining a fuzzy clustering approach with the national blend of models nbm to facilitate scenario driven products for extreme weather, the goal of this project is to combine operational ensemble clustering approaches developed at stony brook university with the national blend of models nbm, this project will combine operational ensemble clustering approaches developed at stony brook university with the national blend of models nbm in collaboration with partners at the environmental modeling center emc weather prediction center wpc ocean prediction center opc and meteorological development lab mdl
NWS Meteorological Development Laboratory (MDL)
Stony Brook University
The goal of this project is to combine operational ensemble clustering approaches developed at Stony Brook University with the National Blend of Models (NBM).
1
Enhancement of Radiation Coupling to Cloud Microphysics and Land Surface Properties for Global Weather Predictions
Iacono
JTTI
check_circle
2020
Level 3
The goal of this project is to transition several improvements related to radiative processes in the Unified Forecast System (UFS) including enhancements to a) the coupling between the microphysics parameterization and the radiation code, b) the coupling between the land parameterization and the radiation code, and c) the representation of the vertical correlation of the sub-grid variability of liquid and ice cloud condensate in the radiation code.
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Dynamics and Nesting, Parameterization
JTTI
Common Community Physics Package (CCPP), Unified Forecast System (UFS)
Effectively representing the sub-grid scale properties of clouds in dynamical models remains a significant source of uncertainty in weather and climate predictions. This uncertainty relates to the small-scale horizontal inhomogeneity of cloud microphysical properties and the vertical correlation or overlap of clouds and their impacts on cloud radiative processes and radiative heating rates that in turn influence the atmospheric state through changes to the temperature lapse rate, atmospheric stability and surface properties. The primary objectives of the proposed research are to improve the coupling of microphysics (MP) to radiative transfer in the NOAA global prediction model through the consistent specification of cloud physical properties and to evaluate a method for representing the sub-grid variability and vertical correlation of cloud condensate.
The primary benefit to the public and the science community of the
proposed work will be through advancing the effectiveness of global and regional weather
predictions, specifically in regard to improving the coupling of radiative transfer to cloud
microphysics and land processes, in support of the NOAA goals of providing effective public
forecasts especially in regard to extreme weather and high impact weather events.
EMC
Fanglin Yang (EMC)
AER;
National Center for Atmospheric Research
NA20OAR4590362
NA20OAR4590363
Relevant to All Hazards
Colorado, Massachusetts
Joint Technology Transfer Initiative (JTTI)
September 2020 - August 2022
Atmospheric Physics, Coupled Models & Techniques, Dynamics and Nesting, Parameterization
enhancement of radiation coupling to cloud microphysics and land surface properties for global weather predictions, the goal of this project is to transition several improvements related to radiative processes in the unified forecast system ufs including enhancements to a the coupling between the microphysics parameterization and the radiation code b the coupling between the land parameterization and the radiation code and c the representation of the vertical correlation of the sub grid variability of liquid and ice cloud condensate in the radiation code, effectively representing the sub grid scale properties of clouds in dynamical models remains a significant source of uncertainty in weather and climate predictions this uncertainty relates to the small scale horizontal inhomogeneity of cloud microphysical properties and the vertical correlation or overlap of clouds and their impacts on cloud radiative processes and radiative heating rates that in turn influence the atmospheric state through changes to the temperature lapse rate atmospheric stability and surface properties the primary objectives of the proposed research are to improve the coupling of microphysics mp to radiative transfer in the noaa global prediction model through the consistent specification of cloud physical properties and to evaluate a method for representing the sub grid variability and vertical correlation of cloud condensate
Environmental Modeling Center (EMC)
AER;, National Center for Atmospheric Research
The goal of this project is to transition several improvements related to radiative processes in the Unified Forecast System (UFS) including enhancements to a) the coupling between the microphysics parameterization and the radiation code, b)
0
UFS-R2O: MRW/S2S Coupled Model Development
Mehra
search_activity
2020
Level 3
This initiative creates a fully coupled global Earth system model that integrates atmosphere, ocean, land, sea-ice, and aerosols under the Unified Forecast System (UFS). It unifies the scientific infrastructure used for both medium-range weather forecasting and sub-seasonal to seasonal climate predictions.
Avichal Mehra, Christiane Jablonowski, Cory Martin, Cristiana Stan, Daryl Kleist, Georg Grell, Jeff McQueen , Jeff Whitaker, Joseph Olson, Kevin Viner, Lisa Bengtsson, Philip Pegion, Vijay Tallapragada, Yuejian Zhu, Ping Zhu
Tue Jun 30 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, High-Performance Computing (HPC), Reanalysis & Reforecasting, Numerical Weather Prediction (NWP), Post-processing
JTTI
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Ensemble Forecast System (GEFS), Global Forecast System (GFS), Modular Ocean Model (MOM), UFS-Medium Range Weather Application (MRWA), UFS-Season to Subseasonal (UFS-S2S)
The primary goal is to extend predictive skill for weeks 3 and 4 (and beyond) by capturing the complex feedbacks between different Earth system components, ultimately replacing legacy standalone global models.
Improved long-range outlooks allow sectors like agriculture, energy, and water management to prepare for heatwaves, droughts, and cold snaps weeks in advance. The public benefits from more reliable extended forecasts that help with long-term travel and safety planning.
EMC
Vijay Tallapragada
NOAA/NWS/EMC
NOAA/OAR/GSL
NOAA/OAR/PSL
George Mason University
University of Michigan
WPO20-JTTI-I-1
Relevant to All Hazards
Colorado, Maryland, Michigan, Virginia
July 2020 - June 2026
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, High-Performance Computing (HPC), Reanalysis & Reforecasting, Numerical Weather Prediction (NWP), Post-processing
ufs r2o mrw s2s coupled model development, this initiative creates a fully coupled global earth system model that integrates atmosphere ocean land sea ice and aerosols under the unified forecast system ufs it unifies the scientific infrastructure used for both medium range weather forecasting and sub seasonal to seasonal climate predictions, the primary goal is to extend predictive skill for weeks 3 and 4 and beyond by capturing the complex feedbacks between different earth system components ultimately replacing legacy standalone global models
Environmental Modeling Center (EMC)
NOAA/NWS/EMC, NOAA/OAR/GSL, NOAA/OAR/PSL, George Mason University, University of Michigan
This initiative creates a fully coupled global Earth system model that integrates atmosphere, ocean, land, sea-ice, and aerosols under the Unified Forecast System (UFS). It unifies the scientific infrastructure used for both medium-range weather forecasting
0
UFS-R2O: RRFS and Retirement of Legacy Models
Blake
search_activity
2020
Level 3
This project establishes the Rapid Refresh Forecast System (RRFS), a high-resolution, hourly-updating ensemble model designed to predict severe convection and local weather events. It simultaneously works to consolidate and retire a fragmented suite of aging regional models (such as the NAM and RAP).
Ben Blake, Curtis Alexander, Jacob Carley, Joseph Olson, Ligia Bernardet, Ming Xue, Steven Weygandt, Tara Jensen, Xuguang Wang
Tue Jun 30 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, High-Performance Computing (HPC), Reanalysis & Reforecasting, Numerical Weather Prediction (NWP), Post-processing
JTTI
Rapid Refresh Forecast System (RRFS), UFS-Short-Range Weather Application (SRWA)
The objective is to provide forecasters with accurate, storm-scale guidance that precisely predicts the timing, location, and intensity of hazardous weather like tornadoes, flash floods, and blizzards.
Forecasters can issue more precise warnings for severe weather, giving the public critical extra minutes to seek shelter and protecting life and property. It simplifies the complex array of models forecasters must monitor, reducing confusion during high-impact events.
EMC
Jacob Carley
NOAA/NWS/EMC
NOAA/OAR/GSL
NOAA/NWS/MDL
National Center for Atmospheric Research
University of Oklahoma
WPO20-JTTI-I-2
Relevant to All Hazards
Colorado, Maryland, Oklahoma
July 2020 - June 2026
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, High-Performance Computing (HPC), Reanalysis & Reforecasting, Numerical Weather Prediction (NWP), Post-processing
ufs r2o rrfs and retirement of legacy models, this project establishes the rapid refresh forecast system rrfs a high resolution hourly updating ensemble model designed to predict severe convection and local weather events it simultaneously works to consolidate and retire a fragmented suite of aging regional models such as the nam and rap, the objective is to provide forecasters with accurate storm scale guidance that precisely predicts the timing location and intensity of hazardous weather like tornadoes flash floods and blizzards
Environmental Modeling Center (EMC)
NOAA/NWS/EMC, NOAA/OAR/GSL, NOAA/NWS/MDL, National Center for Atmospheric Research, University of Oklahoma
This project establishes the Rapid Refresh Forecast System (RRFS), a high-resolution, hourly-updating ensemble model designed to predict severe convection and local weather events. It simultaneously works to consolidate and retire a fragmented suite of aging
0
UFS-R2O: 3DRTMA
Mehra
search_activity
2020
Level 3
3DRTMA develops the official high-resolution "Analysis of Record" by fusing millions of observations from satellites, radar, and ground stations into a detailed, three-dimensional picture of the atmosphere. It represents the best possible estimate of the current state of the weather at any given moment.
Avichal Mehra, Curtis Alexander, Jacob Carley, Manuel Pondeca, Steven Weygandt, Vijay Tallapragada
The aim is to resolve complex near-surface weather details in 3D-especially in difficult terrain and coastal zones-to serve as the "ground truth" for situational awareness and model verification.
The aviation and energy sectors rely on this precise data for safe flight routing and efficient grid management. For the public, it ensures that the short-term forecasts they check on their phones start from the most accurate baseline possible.
EMC
Vijay Tallapragada
NOAA/NWS/EMC
NOAA/OAR/GSL
WPO20-JTTI-I-3
Relevant to All Hazards
Colorado, Maryland
July 2020 - June 2026
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, High-Performance Computing (HPC), Reanalysis & Reforecasting, Numerical Weather Prediction (NWP), Post-processing
ufs r2o 3drtma, 3drtma develops the official high resolution "analysis of record" by fusing millions of observations from satellites radar and ground stations into a detailed three dimensional picture of the atmosphere it represents the best possible estimate of the current state of the weather at any given moment, the aim is to resolve complex near surface weather details in 3d-especially in difficult terrain and coastal zones-to serve as the "ground truth" for situational awareness and model verification
Environmental Modeling Center (EMC)
NOAA/NWS/EMC, NOAA/OAR/GSL
3DRTMA develops the official high-resolution "Analysis of Record" by fusing millions of observations from satellites, radar, and ground stations into a detailed, three-dimensional picture of the atmosphere. It represents the best possible estimate of the
0
Enhancements to the RTE+RRTMGP Radiation Code in Support of NOAA Operational Forecasts
Mlawer
JTTI
check_circle
2021
Level 3
The goal of this project is to implement enhancements to the capabilities, utility, and accuracy of a high-performance broadband radiation code (called RTE+RRTMGP), which is important for NOAA Unified Forecast System (UFS) goals and applications.
Eli Mlawer
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Common Community Physics Package (CCPP), Global Forecast System (GFS), Unified Forecast System (UFS)
This project will add enhancements to the current RTE+RRTMGP radiation parameterization within the UFS model. These enhancements include: extending the vertical range of the code from 65 km to 90-95 km, developing additional specifications for gas optics to improve optimization for NOAA forecast needs, and increasing the coupling between the GFS aerosol parameterization and radiation code.
Due to the ubiquity of radiative processes in weather forecasts, all NOAA operations and forecast centers will benefit from this proposed work
EMC
Qingfu Lu (EMC)
Atmospheric and Environmental Research (AER)
Columbia University
enhancements to the rte+rrtmgp radiation code in support of noaa operational forecasts, the goal of this project is to implement enhancements to the capabilities utility and accuracy of a high performance broadband radiation code called rte+rrtmgp which is important for noaa unified forecast system ufs goals and applications, this project will add enhancements to the current rte+rrtmgp radiation parameterization within the ufs model these enhancements include extending the vertical range of the code from 65 km to 90 95 km developing additional specifications for gas optics to improve optimization for noaa forecast needs and increasing the coupling between the gfs aerosol parameterization and radiation code
Environmental Modeling Center (EMC)
Atmospheric and Environmental Research (AER), Columbia University
The goal of this project is to implement enhancements to the capabilities, utility, and accuracy of a high-performance broadband radiation code (called RTE+RRTMGP), which is important for NOAA Unified Forecast System (UFS) goals and applications.
1
Creating a probabilistic, ensemble-based approach to winter precipitation type for the National Blend of Models with the Spectral Bin Classifier
Rosenow
JTTI
check_circle
2021
Level 3
The goal of this project is to advance winter precipitation type (e.g., snow, sleet, freezing rain) guidance by adapting the hydrometeor-phase algorithm used by the Multi-Fadar/Multi-Sensor (MRMS) system, the Spectral Bin Classifer (SBC), to work with the National Blend of Models (NBM) ensemble.
Andrew Rosenow
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Model & Forecast Guidance, Probabilistic Weather Forecasting, Radar, Visualizations, Decision Support
JTTI
Multi-Radar/Multi-Sensor System (MRMS), National Blend of Models (NBM), Unified Forecast System (UFS)
The goal of this project is to advance winter precipitation type (snow, sleet, freezing rain, etc.) guidance by adapting the hydrometeor-phase algorithm used by the Multi-Radar/Multi-Sensor (MRMS) system, the Spectral Bin Classifier (SBC), to work with the National Blend of Models (NBM) ensemble. The SBC offers enhanced utility/decision support as well as a more accurate representation of the physical processes leading to hydrometeor-phase change. This project addresses creation of probabilistic hazard tools for high-end and extreme winter weather in an NBM-based ensemble of precipitation type forecasts, and creates visualization tools for forecasters to interpret ensemble-based precipitation type when making short- to medium-term forecasts. The result of this work will be a series of recommendations on implementing this technology into the NBM.
This project aims to improve winter weather guidance available from the National Blend of Models (NBM). This research also supports the NOAA Weather-Ready Nation initiative by providing impacts-based decision support for hazards that pose a significant threat to human health and safety.
WPC
Jim Nelson (WPC)
CIMMS/University of Oklahoma
NA21OAR4590162
Winter Weather
Oklahoma
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Atmospheric Physics, Data Assimilation and Ensembles, Model & Forecast Guidance, Probabilistic Weather Forecasting, Radar, Visualizations, Decision Support
creating a probabilistic ensemble based approach to winter precipitation type for the national blend of models with the spectral bin classifier, the goal of this project is to advance winter precipitation type e g snow sleet freezing rain guidance by adapting the hydrometeor phase algorithm used by the multi fadar multi sensor mrms system the spectral bin classifer sbc to work with the national blend of models nbm ensemble, the goal of this project is to advance winter precipitation type snow sleet freezing rain etc guidance by adapting the hydrometeor phase algorithm used by the multi radar multi sensor mrms system the spectral bin classifier sbc to work with the national blend of models nbm ensemble the sbc offers enhanced utility decision support as well as a more accurate representation of the physical processes leading to hydrometeor phase change this project addresses creation of probabilistic hazard tools for high end and extreme winter weather in an nbm based ensemble of precipitation type forecasts and creates visualization tools for forecasters to interpret ensemble based precipitation type when making short to medium term forecasts the result of this work will be a series of recommendations on implementing this technology into the nbm
Weather Prediction Center (WPC)
CIMMS/University of Oklahoma
The goal of this project is to advance winter precipitation type (e.g., snow, sleet, freezing rain) guidance by adapting the hydrometeor-phase algorithm used by the Multi-Fadar/Multi-Sensor (MRMS) system, the Spectral Bin Classifer (SBC), to work
2
Adapting machine learning-based convective hazard guidance to use the HRRR/RRFS
Sobash
JTTI
check_circle
2021
Level 3
The goal of this project is to adapt and evaluate an existing experimental machine learning-based hazard prediction system to use operational convective allowing model (CAM) forecasts, such as those from the High Resolution Rapid Refresh (HRRR) Model and the High Resolution Ensemble Forecast System (HREF).
Ryan Sobash
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project addresses these issues by adapting an existing experimental machine learning-based hazard prediction system to use operational CAM forecasts, such as those from the HRRR and HREF. The system will produce hourly probabilistic convective hazard guidance for a suite of convective hazards, such as tornadoes, large hail, and lightning, for lead-times from 0-48 hours. A full range of sensitivity experiments will be conducted to inform system design choices, including identifying the most effective ML model architectures, the optimal amount of training data, and the spatial and temporal scales most appropriate for hazard guidance as a function of lead-time and hazard type.
This project will lead to improved predictions of convective hazards, increasing the accuracy of forecasts issued by NOAA agencies, such as the Storm Prediction Center and NWS WFOs.
SPC
Israel Jirak (SPC)
National Center for Atmospheric Research
NA21OAR4590163
Severe Weather
Colorado
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting
adapting machine learning based convective hazard guidance to use the hrrr rrfs, the goal of this project is to adapt and evaluate an existing experimental machine learning based hazard prediction system to use operational convective allowing model cam forecasts such as those from the high resolution rapid refresh hrrr model and the high resolution ensemble forecast system href, this project addresses these issues by adapting an existing experimental machine learning based hazard prediction system to use operational cam forecasts such as those from the hrrr and href the system will produce hourly probabilistic convective hazard guidance for a suite of convective hazards such as tornadoes large hail and lightning for lead times from 0-48 hours a full range of sensitivity experiments will be conducted to inform system design choices including identifying the most effective ml model architectures the optimal amount of training data and the spatial and temporal scales most appropriate for hazard guidance as a function of lead time and hazard type
Storm Prediction Center (SPC)
National Center for Atmospheric Research
The goal of this project is to adapt and evaluate an existing experimental machine learning-based hazard prediction system to use operational convective allowing model (CAM) forecasts, such as those from the High Resolution Rapid Refresh
2
Unification and Improvements to Guidance for National Weather Service Tropical Cyclone Wind and Storm Surge Hazard Products
Musgrave
JTTI
check_circle
2021
Level 3
The goal of this project is to unify and improve the post-processing applications for all NWS tropical cyclone wind and surge hazard products. These improvements will leverage a new deterministic surface model called the WTCM and the observational wind database collected for its calibration.
Kate Musgrave
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Model & Forecast Guidance, Post-processing
JTTI
The focus of this proposal is to unify and improve post-processing applications (e.g., WSP, P-Surge, deterministic wind models) for all NWS TC wind and surge hazard products. These improvements will leverage a new deterministic surface wind model called the WTCM, developed in conjunction with NHC and the Miami WFO, and the observational wind database collected for its calibration.
This project will lead to more consistent and accurate NWS tropical cyclone wind and surge hazard products used by the general public and for IDSS for emergency managers.
NHC
Wallace Hogsett (NHC)
CIRA/Colorado State University
NA21OAR4590164
Tropical Cyclones
Colorado
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Atmospheric Physics, Model & Forecast Guidance, Post-processing
unification and improvements to guidance for national weather service tropical cyclone wind and storm surge hazard products, the goal of this project is to unify and improve the post processing applications for all nws tropical cyclone wind and surge hazard products these improvements will leverage a new deterministic surface model called the wtcm and the observational wind database collected for its calibration, the focus of this proposal is to unify and improve post processing applications e g wsp p surge deterministic wind models for all nws tc wind and surge hazard products these improvements will leverage a new deterministic surface wind model called the wtcm developed in conjunction with nhc and the miami wfo and the observational wind database collected for its calibration
National Hurricane Center (NHC)
CIRA/Colorado State University
The goal of this project is to unify and improve the post-processing applications for all NWS tropical cyclone wind and surge hazard products. These improvements will leverage a new deterministic surface model called the WTCM
0
Direct Assimilation of GOES-R Geostationary Lightning Mapper (GLM) Data within JEDI Hybrid System for Operational UFS Convection-Allowing Predictions
Xue
JTTI
check_circle
2021
Level 3
This project looks to add the capability of direct lightning data assimilation using the Joint Effort for Data Assimilation Integration (JEDI) to Unified Forecast System (UFS) convective-scale applications and further refine, optimize, and test them in case studies and quasi-operational settings targeting the operational Rapid Refresh Forecast System (RRFS).
Ming Xue, Amanda Back/Blake Allen
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Convection Allowing Models (CAM), Rapid Refresh (RAP), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
This project looks to add the capability of direct lightning data assimilation using JEDI to UFS convective-scale applications
The benefits include better representation and forecasts of convective storms, particularly over ocean regions that do not have radar coverage
EMC
Jacob Carley
University of Oklahoma
CIRA/Colorado State University
NA21OAR4590165
NA21OAR4590166
Severe Weather
Colorado, Oklahoma
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Data Assimilation and Ensembles
direct assimilation of goes r geostationary lightning mapper glm data within jedi hybrid system for operational ufs convection allowing predictions, this project looks to add the capability of direct lightning data assimilation using the joint effort for data assimilation integration jedi to unified forecast system ufs convective scale applications and further refine optimize and test them in case studies and quasi operational settings targeting the operational rapid refresh forecast system rrfs, this project looks to add the capability of direct lightning data assimilation using jedi to ufs convective scale applications
Environmental Modeling Center (EMC)
University of Oklahoma, CIRA/Colorado State University
This project looks to add the capability of direct lightning data assimilation using the Joint Effort for Data Assimilation Integration (JEDI) to Unified Forecast System (UFS) convective-scale applications and further refine, optimize, and test them
7
Advancing Land Modeling Infrastructure in the UFS for Hierarchical Model Development
Turuncoglu
JTTI
check_circle
2021
Level 3
The goal of this project is to advance land modeling infrastructure within the Unified Forecast System (UFS) to support running the UFS land model as an independent model component with new capabilities for driving the land with atmospheric forcings and flexibly coupling to the UFS atmosphere and other models.
Rocky Dunlap/Ufuk Turuncoglu
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Common Community Physics Package (CCPP), Earth System Modeling Framework (ESMF), Global Forecast System (GFS), National Unified Operational Prediction Capability (NUOPC), Noah Multiparameterization Land Surface Model (Noah-MP LSM), Unified Forecast System (UFS)
This project builds on existing infrastructure development activities in the Unified Forecast System (UFS). A new component interface based on the Earth System Modeling Framework (ESMF) and the National Unified Operation Prediction Capability (NUOPC) layer will be created for the Noah-MP land surface model (LSM). This will allow Noah-MP to flexibly run as a separate model component within the UFS with the ability to turn off land-atmosphere coupling exchanges. The proposed approach is fully compatible with, and builds upon, the current land model interface based on the Common Community Physics Package (CCPP). A set of powerful "data models" called the Community Data Models for Earth Prediction Systems (CDEPS) will be used to infuse forcings into the system under an extensible framework. In two-way coupled mode, the Community Mediator for Earth Prediction Systems (CMEPS) will be used to connect the LSM to other components in the coupled system, in a manner that is architecturally consistent with how the ocean, wave, and sea ice components are coupled.
This project will deliver new capabilities for driving the land with atmospheric forcings, selectively controlling atmosphere-land feedbacks for hierarchical testing, and supporting "land-only" runs of the UFS to support data assimilation and generation of initial conditions.
EMC
Mike Barlage (EMC)
National Center for Atmospheric Research
NA21OAR4590167
Relevant to All Hazards
Colorado
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2025
Atmospheric Physics, Coupled Models & Techniques
advancing land modeling infrastructure in the ufs for hierarchical model development, the goal of this project is to advance land modeling infrastructure within the unified forecast system ufs to support running the ufs land model as an independent model component with new capabilities for driving the land with atmospheric forcings and flexibly coupling to the ufs atmosphere and other models, this project builds on existing infrastructure development activities in the unified forecast system ufs a new component interface based on the earth system modeling framework esmf and the national unified operation prediction capability nuopc layer will be created for the noah mp land surface model lsm this will allow noah mp to flexibly run as a separate model component within the ufs with the ability to turn off land atmosphere coupling exchanges the proposed approach is fully compatible with and builds upon the current land model interface based on the common community physics package ccpp a set of powerful "data models" called the community data models for earth prediction systems cdeps will be used to infuse forcings into the system under an extensible framework in two way coupled mode the community mediator for earth prediction systems cmeps will be used to connect the lsm to other components in the coupled system in a manner that is architecturally consistent with how the ocean wave and sea ice components are coupled
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
The goal of this project is to advance land modeling infrastructure within the Unified Forecast System (UFS) to support running the UFS land model as an independent model component with new capabilities for driving the
0
Advancing the Use of Multivariate Object-Based Verification Methods in Regional-Scale Models
Otkin
JTTI
check_circle
2021
Level 3
The goal of this project is to assess the accuracy of clouds and convection in the Rapid Refresh Forecast System (RRFS) through comparisons of observed and simulated satellite and radar datasets using object-based verification methods.
Jason Otkin
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri Jan 31 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Multi-Radar/Multi-Sensor System (MRMS), Rapid Refresh (RAP), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
This project will assess the accuracy of clouds and convection in the Rapid Refresh Forecast System (RRFS) through comparisons of observed and simulated satellite and radar datasets using object-based verification methods. Lightning observations and infrared brightness temperatures from the Geostationary Lightning Mapper (GLM) and Advanced Baseline Imager (ABI) sensors onboard the GOES-16/17 satellites, as well as radar data from the Multiple Radar Multiple Sensor (MRMS) system will be used to assess the forecast accuracy. The proposed project will develop new model verification methods that use the GLM, ABI, and MRMS datasets in a multivariate framework that supports a more thorough evaluation than any one dataset can provide by itself.
The model improvements will benefit society by leading to more accurate forecasts during high-impact weather events and periods of increased wildfire risk where accurate lightning forecasts are paramount for pre-staging personnel and responding to incidents.
advancing the use of multivariate object based verification methods in regional scale models, the goal of this project is to assess the accuracy of clouds and convection in the rapid refresh forecast system rrfs through comparisons of observed and simulated satellite and radar datasets using object based verification methods, this project will assess the accuracy of clouds and convection in the rapid refresh forecast system rrfs through comparisons of observed and simulated satellite and radar datasets using object based verification methods lightning observations and infrared brightness temperatures from the geostationary lightning mapper glm and advanced baseline imager abi sensors onboard the goes 16 17 satellites as well as radar data from the multiple radar multiple sensor mrms system will be used to assess the forecast accuracy the proposed project will develop new model verification methods that use the glm abi and mrms datasets in a multivariate framework that supports a more thorough evaluation than any one dataset can provide by itself
Environmental Modeling Center (EMC)
CIMSS/University of Wisconsin
The goal of this project is to assess the accuracy of clouds and convection in the Rapid Refresh Forecast System (RRFS) through comparisons of observed and simulated satellite and radar datasets using object-based verification methods.
1
Advancing Ensemble Subseasonal Forecasting with Machine Learning
Cohen
JTTI
check_circle
2021
Level 3
The goal of this project is to increase the skill of subseasonal to seasonal (S2S) predictions by using new machine learning statistical techniques to extract predictable information from the North American Multi-Model Ensemble (NMME), Subseasonal Experiment (SubX), and Global Ensemble Forecast System (GEFSv12) ensemble forecasts.
Judah Cohen, Dara Entekhabi
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles
JTTI
Global Ensemble Forecast System (GEFS), North American Multi-Model Ensemble (NMME), Unified Forecast System (UFS)
This project proposes to increase the skill of subseasonal to seasonal (S2S) predictions by using machine learning (ML) to extract predictable information from the NMME, SubX and GEFSv12 ensemble forecasts.
This project aims to significantly improve S2S real-time probabilistic predictions at NOAA's Climate Prediction Center (CPC).
CPC
David DeWitt (CPC)
Atmospheric and Environmental Research
Massachusetts Institute of Technology
NOAA/NWS/CPC
NA21OAR4590169
NA21OAR4590170
BOP
Relevant to All Hazards
Maryland, Massachusetts
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles
advancing ensemble subseasonal forecasting with machine learning, the goal of this project is to increase the skill of subseasonal to seasonal s2s predictions by using new machine learning statistical techniques to extract predictable information from the north american multi model ensemble nmme subseasonal experiment subx and global ensemble forecast system gefsv12 ensemble forecasts, this project proposes to increase the skill of subseasonal to seasonal s2s predictions by using machine learning ml to extract predictable information from the nmme subx and gefsv12 ensemble forecasts
Climate Prediction Center (CPC)
Atmospheric and Environmental Research, Massachusetts Institute of Technology, NOAA/NWS/CPC
The goal of this project is to increase the skill of subseasonal to seasonal (S2S) predictions by using new machine learning statistical techniques to extract predictable information from the North American Multi-Model Ensemble (NMME), Subseasonal
9
Implementation, Testing, and Evaluation of Radar Data Assimilation Capabilities within the JEDI Hybrid EnVar System for the Rapid Refresh Forecast System
Liu
JTTI
check_circle
2021
Level 3
The goal of this project is to implement direct radar data assimilation (DA) capabilities with the Joint-Effort for DA Integration (JEDI)-based hybrid ensemble-variation (EnVAR) DA system, and test and evaluate it for the target Rapid Refresh Forecast System (RRFS) model configuartions based on the limited area FV3 (FV3-LAM).
Chengsi Liu, Jeffrey Duda
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Rapid Refresh (RAP), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
Aiming at the future RRFS system, the CAPS and GSL teams will collaborate with EMC to implement direct radar DA capabilities within the JEDI-based hybrid EnVar DA system and test and evaluate it for the target RRFS model configurations based on FV3-LAM
The DA capabilities developed should lead to significant improvements to severe weather forecasting from sub-hour to 12-hour time ranges.
EMC
Jacob Carley (EMC)
University of Oklahoma
CIRES/University of Colorado
NA21OAR4590171
NA21OAR4590172
Relevant to All Hazards
Colorado, Oklahoma
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Data Assimilation and Ensembles, Radar
implementation testing and evaluation of radar data assimilation capabilities within the jedi hybrid envar system for the rapid refresh forecast system, the goal of this project is to implement direct radar data assimilation da capabilities with the joint effort for da integration jedi based hybrid ensemble variation envar da system and test and evaluate it for the target rapid refresh forecast system rrfs model configuartions based on the limited area fv3 fv3 lam, aiming at the future rrfs system the caps and gsl teams will collaborate with emc to implement direct radar da capabilities within the jedi based hybrid envar da system and test and evaluate it for the target rrfs model configurations based on fv3 lam
Environmental Modeling Center (EMC)
University of Oklahoma, CIRES/University of Colorado
The goal of this project is to implement direct radar data assimilation (DA) capabilities with the Joint-Effort for DA Integration (JEDI)-based hybrid ensemble-variation (EnVAR) DA system, and test and evaluate it for the target Rapid
3
Enhancing the Storm Prediction Center's Convective Outlook with Continuous Probabilities and Conditional Intensity Forecasts
Ripberger
JTTI
check_circle
2021
Level 3
The goal of this project is to develop and test visualizations for communicating conditional intensity and continuous probability information in SPC's Convective Outlook graphic.
Joseph Ripberger
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Risk Communication, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
JTTI
The goal of this project is to develop and test visualizations for communicating conditional intensity and continuous probability information in SPC's Convective Outlook graphic.
The proposed project will benefit society by developing and evaluating state-of-the-art severe weather communication tools on the convective outlook scale (i.e. one to eight days before the event). These tools will include the most sophisticated severe weather information packaged and communicated in the most effective way based on rigorous testing and input from creators and users. This interdisciplinary approach will primarily result in improved knowledge of communicating forecast uncertainty and probabilistic hazard information in the severe weather domain.
SPC
Patrick Marsh (SPC)
University of Oklahoma
NA21OAR4590173
Severe Weather
Oklahoma
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Risk Communication, "Social Science (SBES)", Visual Risk Communication
enhancing the storm prediction center's convective outlook with continuous probabilities and conditional intensity forecasts, the goal of this project is to develop and test visualizations for communicating conditional intensity and continuous probability information in spc's convective outlook graphic, the goal of this project is to develop and test visualizations for communicating conditional intensity and continuous probability information in spc's convective outlook graphic
Storm Prediction Center (SPC)
University of Oklahoma
The goal of this project is to develop and test visualizations for communicating conditional intensity and continuous probability information in SPC's Convective Outlook graphic.
1
Enhancing Feature Driven Evaluation of High Impact Hydrometeorology Events through METplus
Ek
JTTI
check_circle
2021
Level 3
The goal of this project is to develop and refine several Model Evaluation Tools (MET) enhanced verification and diagnostics framework (METplus) tools to aid the Weather Prediction Center on high priority tasks related to snowfall prediction and high-impact events.
Tara Jensen/Mike Ek, Michael Erickson/Matt Green
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Model Evaluation Tools Plus (METPlus), Unified Forecast System (UFS)
This project will use existing tools and methods contained within METplus to assist with diagnostic investigations of the Unified Forecast System (UFS) in collaboration with hydrometeorologic applications developers at the Weather Prediction Center (WPC) and across the National Oceanic and Atmospheric Administration (NOAA). The project will leverage work performed through previous United States Weather Research Program (USWRP) Research to Operations (R2O) and Joint Technology Transfer Initiative (JTTI) projects focused on establishing a METplus-based system at WPC for the Hydrometerology Testbed (HMT). Now that the system is established, this project will focus on developing and refining several METplus tools to aid WPC on high priority tasks related to snowfall prediction and high-impact events.
The addition of Forecast Consistency and Forecast Difficulty tools in METplus will allow for these tools to be easily implemented and utilized by WPC forecasters in operations. Metrics evaluating run to run consistency, which can lead to the quantification and improvement of ensemble forecast products, are lacking in operations. The integration of Multivariable MODE/MTD and Feature Relative tools will lead to further improvements in currently existing experimental snowband and heavy precipitation tracking websites. In addition, retrospective analysis of Feature Relative snowbands can improve forecaster situational awareness during high-impact weather events.
WPC
Mark Klein (WPC)
National Center for Atmospheric Research
CIRES/University of Colorado
NA21OAR4590174
NA21OAR4590175
Winter Weather
Colorado
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Verification & Validation
enhancing feature driven evaluation of high impact hydrometeorology events through metplus, the goal of this project is to develop and refine several model evaluation tools met enhanced verification and diagnostics framework metplus tools to aid the weather prediction center on high priority tasks related to snowfall prediction and high impact events, this project will use existing tools and methods contained within metplus to assist with diagnostic investigations of the unified forecast system ufs in collaboration with hydrometeorologic applications developers at the weather prediction center wpc and across the national oceanic and atmospheric administration noaa the project will leverage work performed through previous united states weather research program uswrp research to operations r2o and joint technology transfer initiative jtti projects focused on establishing a metplus based system at wpc for the hydrometerology testbed hmt now that the system is established this project will focus on developing and refining several metplus tools to aid wpc on high priority tasks related to snowfall prediction and high impact events
Weather Prediction Center (WPC)
National Center for Atmospheric Research, CIRES/University of Colorado
The goal of this project is to develop and refine several Model Evaluation Tools (MET) enhanced verification and diagnostics framework (METplus) tools to aid the Weather Prediction Center on high priority tasks related to snowfall
6
Medium-range excessive rainfall forecasts with machine learning models
Schumacher
JTTI
check_circle
2021
Level 3
The goal of this project is to redevelop the Colorado State University (CSU) Machine Learning Probabilities system (MLP) with the new Unified Forecast System (UFS) global model while simultaneously developing a seamless, reliable day 1-8 prediction systems as an additional piece of forecast guidance for Weather Prediction Center forecasters.
Russ Schumacher
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project is proposing to redevelop the machine learning-based CSU-MLP model with the new UFS global model while simultaneously developing a seamless, reliable day 1-8 prediction system as an additional piece of forecast guidance for WPC forecasters.
This project will develop new tools that will inform Weather Prediction Center forecasters in their generation of excessive rainfall outlooks and other forecasts of heavy precipitation. In particular, the tools produced in this project can be used as a "first guess" by forecasters, which streamlines their forecast process and will result in improved forecast reliability and skill. Because the WPC ERO is widely used by diverse stakeholders to anticipate the potential for excessive rainfall and flash flooding, improvements in these outlooks may yield substantial societal benefit, by increasing preparedness for flash flooding.
medium range excessive rainfall forecasts with machine learning models, the goal of this project is to redevelop the colorado state university csu machine learning probabilities system mlp with the new unified forecast system ufs global model while simultaneously developing a seamless reliable day 1 8 prediction systems as an additional piece of forecast guidance for weather prediction center forecasters, this project is proposing to redevelop the machine learning based csu mlp model with the new ufs global model while simultaneously developing a seamless reliable day 1 8 prediction system as an additional piece of forecast guidance for wpc forecasters
Weather Prediction Center (WPC)
CIRA/Colorado State University
The goal of this project is to redevelop the Colorado State University (CSU) Machine Learning Probabilities system (MLP) with the new Unified Forecast System (UFS) global model while simultaneously developing a seamless, reliable day 1-8
3
Leveraging artificial intelligence to statistically postprocess GEFS ensemble forecasts for Week 3-4 Outlooks of precipitation probabilities for the CPC
Smith
JTTI
check_circle
2021
Level 3
The goal of this project is to develop and demonstrate ensemble-based post-processing methodologies and algorithms that leverage artificial intelligence to correct inherent systemtic biases in the Global Ensemble Forecast System (GEFS), thereby improving the skill of subseasonal precipitation accumulation forecasts.
Rochelle Worsnop/Cathy Smith
Complete
Wed Dec 01 2021 03:00:00 GMT-0500 (Eastern Standard Time)
Sun Nov 30 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Post-processing
JTTI
Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
This project proposes to develop and demonstrate novel, state-of-the-science, ensemble-based post-processing methodologies for operational use at the Climate Prediction Center (CPC). These methods leverage artificial intelligence, specifically neural networks (NNs), to correct inherent systematic biases in the Global Ensemble Forecast System (GEFS). These algorithms will accelerate the use of AI in operations by calibrating existing GEFS forecasts and subsequently support the production of operational subseasonal forecast products such as CPC's "Week 3-4 Outlook" precipitation probability maps.
The project activities and outcomes are well integrated with NOAA plans and directives to benefit society through advanced weather forecasts that can protect life and property. They are specifically relevant to the CPC mission to "deliver real-time products and information that predict and describe climate variations on timescales from weeks to years thereby promoting effective management of climate risk and a climate-resilient society."
CPC
David DeWitt (CPC)
CIRES/University of Colorado
NA21OAR4590176
Relevant to All Hazards
Colorado
Joint Technology Transfer Initiative (JTTI)
December 2021 - November 2025
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Post-processing
leveraging artificial intelligence to statistically postprocess gefs ensemble forecasts for week 3 4 outlooks of precipitation probabilities for the cpc, the goal of this project is to develop and demonstrate ensemble based post processing methodologies and algorithms that leverage artificial intelligence to correct inherent systemtic biases in the global ensemble forecast system gefs thereby improving the skill of subseasonal precipitation accumulation forecasts, this project proposes to develop and demonstrate novel state of the science ensemble based post processing methodologies for operational use at the climate prediction center cpc these methods leverage artificial intelligence specifically neural networks nns to correct inherent systematic biases in the global ensemble forecast system gefs these algorithms will accelerate the use of ai in operations by calibrating existing gefs forecasts and subsequently support the production of operational subseasonal forecast products such as cpc's "week 3 4 outlook" precipitation probability maps
Climate Prediction Center (CPC)
CIRES/University of Colorado
The goal of this project is to develop and demonstrate ensemble-based post-processing methodologies and algorithms that leverage artificial intelligence to correct inherent systemtic biases in the Global Ensemble Forecast System (GEFS), thereby improving the skill
1
An Optimized Lake-Treatment Strategy for Improved Land-Surface Modeling and Weather Prediction in the Unified Forecast System (UFS)
Gronewold
JTTI
check_circle
2021
Level 3
The goal of this project is to evaluate options for land surface treatment in the Unified Forecast System (UFS) using established lake models, satellite remote sensing, and in-situ observations, and provide an optimized strategy for improving weather and climate prediction in the UFS through better representation of lakes.
Andrew Gronewold, David Yates, Tatiana Smirnova, Eric Anderson
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will provide (i) an improved global lake bathymetric data set for use in UFS global climate modeling (GFSv18) and short-term weather forecasting (RRFSv2); (ii) an evaluation of leading lake physics models (e.g. FLake, CLM Lake Model) and recommended configurations and parameterizations to enhance the Common Community Physics Package (CCPP); and (iii) lake initialization strategies for UFS (e.g. RRFSv2, GFS18)
Improved lake-effect weather forecast guidance will be available to local weather forecast offices (WFO) and the public. As surface weather affects many aspects of everyday life, the improvements to model skill will have direct benefits to the general public, federal/state/local decision makers, commerce, and the research community who rely on NOAA's operational forecast suite.
EMC
Michael Barlage (EMC)
University of Michigan
National Center for Atmospheric Research
CIRES/University of Colorado
NOAA/OAR/GLERL
NOAA/OAR/GSL
an optimized lake treatment strategy for improved land surface modeling and weather prediction in the unified forecast system ufs, the goal of this project is to evaluate options for land surface treatment in the unified forecast system ufs using established lake models satellite remote sensing and in situ observations and provide an optimized strategy for improving weather and climate prediction in the ufs through better representation of lakes, this project will provide i an improved global lake bathymetric data set for use in ufs global climate modeling gfsv18 and short term weather forecasting rrfsv2 ii an evaluation of leading lake physics models e g flake clm lake model and recommended configurations and parameterizations to enhance the common community physics package ccpp and iii lake initialization strategies for ufs e g rrfsv2 gfs18
Environmental Modeling Center (EMC)
University of Michigan, National Center for Atmospheric Research, CIRES/University of Colorado, NOAA/OAR/GLERL, NOAA/OAR/GSL
The goal of this project is to evaluate options for land surface treatment in the Unified Forecast System (UFS) using established lake models, satellite remote sensing, and in-situ observations, and provide an optimized strategy for
20
Adapting and ingesting the operational National Blend of Models (NBM) product suite for use in the operational NOAA National Water Model
Zhang
JTTI
check_circle
2021
Level 3
This project aims to create the capability to process and ingest a suite of National Blend of Model (NBM) deterministic and probabilistic quantitative precipitation forecast (QPF) products into the operational National Water Model (NWM) and document the changes in skill in the NWM forecasts of streamflow and inundated area.
David Gochis/Yongxin Zhang
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
National Blend of Models (NBM), National Water Model (NWM), Unified Forecast System (UFS)
This proposal aims to create the capability to process and ingest a suite of NBM deterministic and probabilistic QPF products into the operational NWM and document the changes in skill in the NWM forecasts of streamflow and inundated area.
The outputs of this proposal, namely the production of improved deterministic and probabilistic hydrologic forecasts, directly addresses the following priority area: Improve forecasts and messaging of extreme weather and high impact weather events, and, specifically, further develop,test and evaluate tools and models that convey probabilistic hazard information to assist forecasters in diagnosing the magnitude and evolution of high impact and extreme weather events.
adapting and ingesting the operational national blend of models nbm product suite for use in the operational noaa national water model, this project aims to create the capability to process and ingest a suite of national blend of model nbm deterministic and probabilistic quantitative precipitation forecast qpf products into the operational national water model nwm and document the changes in skill in the nwm forecasts of streamflow and inundated area, this proposal aims to create the capability to process and ingest a suite of nbm deterministic and probabilistic qpf products into the operational nwm and document the changes in skill in the nwm forecasts of streamflow and inundated area
NWS Office of Water Prediction (OWP)
National Center for Atmospheric Research
This project aims to create the capability to process and ingest a suite of National Blend of Model (NBM) deterministic and probabilistic quantitative precipitation forecast (QPF) products into the operational National Water Model (NWM) and
0
Integration of Model Large-Scale Environmental Diagnostics for Tropical Cyclones into the MET-TC Verification Package
Musgrave
JTTI
check_circle
2021
Level 3
The goal of this project is to integrate diagnostic software previous developed by the Hurricane Forecast Improvement Program (HFIP) into the Model Evaluation Tools-Tropical Cyclone (MET-TC) verification package, to allow model developers to use the large-scale diagnostics as part of their assessment of model improvements in a standardized open format.
Kate Musgrave, Paul Kucera
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
The goal of this project is to integrate the TC environment diagnostic software into the Model Evaluation Tools-Tropical Cyclone (MET-TC) verification package, to allow model developers to use the large-scale diagnostics as part of their assessment of model improvements in a standardized open-source format.
The large-scale diagnostics serve as an important piece of the analysis of hurricanes, to answer the important questions behind the changes in track and intensity. The longer-term benefits also include improvements to existing and the development of new statistical-dynamical and artificial intelligence-based forecast guidance incorporating the large-scale diagnostics.
NHC
Wallace Hogsett (NHC)
CIRA/Colorado State University
National Center for Atmospheric Research
NA21OAR4590181
NA21OAR4590182
Tropical Cyclones
Colorado
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Verification & Validation
integration of model large scale environmental diagnostics for tropical cyclones into the met tc verification package, the goal of this project is to integrate diagnostic software previous developed by the hurricane forecast improvement program hfip into the model evaluation tools tropical cyclone met tc verification package to allow model developers to use the large scale diagnostics as part of their assessment of model improvements in a standardized open format, the goal of this project is to integrate the tc environment diagnostic software into the model evaluation tools tropical cyclone met tc verification package to allow model developers to use the large scale diagnostics as part of their assessment of model improvements in a standardized open source format
National Hurricane Center (NHC)
CIRA/Colorado State University, National Center for Atmospheric Research
The goal of this project is to integrate diagnostic software previous developed by the Hurricane Forecast Improvement Program (HFIP) into the Model Evaluation Tools-Tropical Cyclone (MET-TC) verification package, to allow model developers to use the
0
Winter Storm Severity Index for Alaska
Tobin
JTTI
check_circle
2021
Level 3
The goal of this project is to expand the operational CONUS WSSI into the Alaska Region as a user-informed and tested product that will meet the unique climate considerations of the various regions in the state.
Kirstin Harnos/Dana Tobin, Rachel Hogan Carr
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
"Risk Categories, Indices, or Levels", "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
JTTI
This study is proposing a mixed social science study to support the expansion of the WPC Winter Storm Severity Index (WSSI) operational product into the Alaska region
At the conclusion of this project, Alaska will have its first impact based communication tool that will distill complex, multifaceted numerical weather information into easily consumable graphics.
WPC
Jim Nelson (WPC)
CIRES/University of Colorado
Nature Nurture Center
NA21OAR4590188
NA21OAR4590183
Winter Weather
Colorado, Pennsylvania
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
"Risk Categories, Indices, or Levels", "Social Science (SBES)", Visual Risk Communication
winter storm severity index for alaska, the goal of this project is to expand the operational conus wssi into the alaska region as a user informed and tested product that will meet the unique climate considerations of the various regions in the state, this study is proposing a mixed social science study to support the expansion of the wpc winter storm severity index wssi operational product into the alaska region
Weather Prediction Center (WPC)
CIRES/University of Colorado, Nature Nurture Center
The goal of this project is to expand the operational CONUS WSSI into the Alaska Region as a user-informed and tested product that will meet the unique climate considerations of the various regions in the
3
Improving numerical weather forecasting and S2S prediction with the NCEP Unified Forecast System through strongly coupled land-atmosphere data assimilation using JEDI
Pu
JTTI
check_circle
2021
Level 3
The goal of this project is to implement a strongly coupled land-atmosphere data assimilation capabilitity into the Unified Forecast System (UFS) using the Joint Effort for Data Assimilation Integration (JEDI).
Zhaoxia Pu
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Data Assimilation and Ensembles
JTTI
Global Forecast System (GFS), Noah Multiparameterization Land Surface Model (Noah-MP LSM), Unified Forecast System (UFS)
This project addresses a gap in current UFS data assimilation capabilities. It will lead to a benchmark strongly coupled land-atmosphere data assimilation system for NCEP UFS using JEDI
It is anticipated that this project will help improve the ability of NCEP operational systems in both weather and S2S prediction.
EMC
Travis Elless (EMC)
University of Utah
NA21OAR4590160
Relevant to All Hazards
Utah
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2025
Coupled Models & Techniques, Data Assimilation and Ensembles
improving numerical weather forecasting and s2s prediction with the ncep unified forecast system through strongly coupled land atmosphere data assimilation using jedi, the goal of this project is to implement a strongly coupled land atmosphere data assimilation capabilitity into the unified forecast system ufs using the joint effort for data assimilation integration jedi, this project addresses a gap in current ufs data assimilation capabilities it will lead to a benchmark strongly coupled land atmosphere data assimilation system for ncep ufs using jedi
Environmental Modeling Center (EMC)
University of Utah
The goal of this project is to implement a strongly coupled land-atmosphere data assimilation capabilitity into the Unified Forecast System (UFS) using the Joint Effort for Data Assimilation Integration (JEDI).
0
Diagnosing synoptic progressiveness forecast errors within the UFS MRWA
Romine
JTTI
check_circle
2021
Level 3
The goal of this project is to develop and apply tools to isolate the cause of synoptic progressiveness within the Unified Forecast System (UFS) Medium-Range Weather Applications (MRWA) and will develop recommendations for NOAA to improve performance of the operational forecast system.
Glen Romine
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Unified Forecast System (UFS), UFS-Medium Range Weather Application (MRWA)
This project will develop and apply tools to isolate the cause of synoptic progressiveness within the UFS MRWA and will develop recommendations for NOAA to improve performance of the operational forecast system.
Improved forecasts of these events will aid operational forecasters in providing more reliable medium-range guidance and enabling better decisions in advance of societally disruptive weather.
EMC
Alicia Bently (EMC)
Geoff Manikin (EMC)
National Center for Atmospheric Research
NA21OAR4590184
Relevant to All Hazards
Colorado
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Model & Forecast Guidance
diagnosing synoptic progressiveness forecast errors within the ufs mrwa, the goal of this project is to develop and apply tools to isolate the cause of synoptic progressiveness within the unified forecast system ufs medium range weather applications mrwa and will develop recommendations for noaa to improve performance of the operational forecast system, this project will develop and apply tools to isolate the cause of synoptic progressiveness within the ufs mrwa and will develop recommendations for noaa to improve performance of the operational forecast system
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
The goal of this project is to develop and apply tools to isolate the cause of synoptic progressiveness within the Unified Forecast System (UFS) Medium-Range Weather Applications (MRWA) and will develop recommendations for NOAA to
2
Post-Processing of CMAQ forecast for Improving Air Quality Predictions
Alessandrini
JTTI
check_circle
2021
Level 3
This project aims to apply the analog ensemble (AnEn) capability to reduce the errors of the Community Multi-scale Air Quality (CMAQ) model fine particulate matter (PM) 2.5 and ozone surface concentrations using a combination of past gridded chemical reanalysis from the Copernicus Atmosphere Monitoring Service (CAMS) Near-Real-Time model with measurements from the Environmental Protection Agency's (EPA) AirNow stations.
Stefano Alessandrini, Irina Djalalova, James Wilczak
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Composition and Chemistry, Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Reanalysis & Reforecasting, Post-processing
JTTI
Community Multiscale Air Quality (CMAQ) Model
This proposal aims to apply a machine learning (ML) post-processing to improve the Community Multi-scale Air Quality (CMAQ) model operational air quality forecasts issued over the US by NOAA/NCEP. Specifically, we propose an extension of the analog ensemble (AnEn) capability currently implemented at NCEP from point-based to 2D gridded predictions.
This project is expected to provide more accurate air quality predictions along with uncertainty information which will help air quality forecasters around the country determine the value of air quality forecasts in their decision-making process. Timely and high confidence air quality alerts resulting from these products will help the public reduce their exposure to air pollution. Although it is difficult to quantify the benefits of accurate air quality predictions, if such predictions can reduce the premature deaths and associated costs by even 1%, then over 1,000 lives and over $1 billion could be saved annually in the U.S.
EMC
Jeff McQueen (EMC)
National Center for Atmospheric Research
CIRES/University of Colorado
NOAA/OAR/GSL
NA21OAR4590185
NA21OAR4590186
BOP
Relevant to All Hazards
Colorado
Joint Technology Transfer Initiative (JTTI)
August 2021 - July 2024
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Composition and Chemistry, Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Reanalysis & Reforecasting, Post-processing
post processing of cmaq forecast for improving air quality predictions, this project aims to apply the analog ensemble anen capability to reduce the errors of the community multi scale air quality cmaq model fine particulate matter pm 2 5 and ozone surface concentrations using a combination of past gridded chemical reanalysis from the copernicus atmosphere monitoring service cams near real time model with measurements from the environmental protection agency's epa airnow stations, this proposal aims to apply a machine learning ml post processing to improve the community multi scale air quality cmaq model operational air quality forecasts issued over the us by noaa ncep specifically we propose an extension of the analog ensemble anen capability currently implemented at ncep from point based to 2d gridded predictions
Environmental Modeling Center (EMC)
National Center for Atmospheric Research, CIRES/University of Colorado, NOAA/OAR/GSL
This project aims to apply the analog ensemble (AnEn) capability to reduce the errors of the Community Multi-scale Air Quality (CMAQ) model fine particulate matter (PM) 2.5 and ozone surface concentrations using a combination of
7
Evaluating Alternative Risk Communication Strategies for the Storm Prediction Center's Convective Outlook
Marsh
JTTI
search_activity
2022
Level 3
This project looks to provide options for consistent understanding by core partners and the general public of the Storm Prediction Center's Convective Outlook by testing multiple alternatives to the current labeling system.
Patrick Marsh, Makenzie Krocak, Joseph Ripberger, Sean Ernst, Carol Silva
Active
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
"Risk Categories, Indices, or Levels", Risk Communication, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
JTTI
Previous research into the interpretation and use of the Storm Prediction Center's Convective
Outlook has largely focused on challenges with the current system. Few studies have proposed
alternative options that provide a more consistent interpretation by core partners and members of
the public. This project will test multiple alternatives to the current risk labelling system, including
levels, probabilities, and an ordinal verbal scale. These tests will be conducted with both core
partners (broadcast meteorologists and emergency managers) and members of the public. Testing
with partners will include a Hazardous Weather Testbed experiment that will evaluate the
challenges and benefits of the alternative labelling systems and the downstream impacts that a
system change may have on core partner operations. A secondary component of the testbed
experiments involve an evaluation of an interactive outlook display that can be customized to the
needs of different partners. Tests with members of the public will include survey experiments
designed to elicit the interpretation, risk perception, and intended response actions to each of the
three systems in comparison to the current system. Specific emphasis will be given to underserved
and vulnerability communities. The outcomes of the project will include a proposed alternative
labelling system at RL-8 and a rigorous evaluation of how a future interactive outlook display will
be utilized by core partners.
The primary outcome of this work will be a proposed change to the Convective Outlook categorical
labelling system that is supported by research conducted with core partners and members of the
public. A secondary outcome will be an evaluation of how a future interactive outlook display
might be utilized by core partners. This work is relevant to the NWS 2019-2022 Strategic Plan,
the OAR Strategy 2020-2026 Plan, and NOAA's 2020-2026 Research and Development Vision
Areas because it incorporates social science research early in the process of redesigning the outlook
visualizations, ultimately creating a more accessible product that will improve IDSS,
communication of severe weather uncertainty, and reduce societal impacts.
SPC
Patrick Marsh (SPC)
NOAA/NWS/SPC
WPO22-JTTI-I-1
Severe Weather
Oklahoma
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2025
"Risk Categories, Indices, or Levels", Risk Communication, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)"
evaluating alternative risk communication strategies for the storm prediction center's convective outlook, this project looks to provide options for consistent understanding by core partners and the general public of the storm prediction center's convective outlook by testing multiple alternatives to the current labeling system, previous research into the interpretation and use of the storm prediction center's convective outlook has largely focused on challenges with the current system few studies have proposed alternative options that provide a more consistent interpretation by core partners and members of the public this project will test multiple alternatives to the current risk labelling system including levels probabilities and an ordinal verbal scale these tests will be conducted with both core partners broadcast meteorologists and emergency managers and members of the public testing with partners will include a hazardous weather testbed experiment that will evaluate the challenges and benefits of the alternative labelling systems and the downstream impacts that a system change may have on core partner operations a secondary component of the testbed experiments involve an evaluation of an interactive outlook display that can be customized to the needs of different partners tests with members of the public will include survey experiments designed to elicit the interpretation risk perception and intended response actions to each of the three systems in comparison to the current system specific emphasis will be given to underserved and vulnerability communities the outcomes of the project will include a proposed alternative labelling system at rl 8 and a rigorous evaluation of how a future interactive outlook display will be utilized by core partners
Storm Prediction Center (SPC)
NOAA/NWS/SPC
This project looks to provide options for consistent understanding by core partners and the general public of the Storm Prediction Center's Convective Outlook by testing multiple alternatives to the current labeling system.
0
Accelerating transition to JEDI ensemble solvers for coupled data assimilation in the GEFS system
Frolov
JTTI
search_activity
2022
Level 3
This project focuses the efforts of the NOAA OAR PSL and NOAA NWS EMC on accelerated transition of the JEDI EnKF solver in the context of the future coupled GEFS implementation within the UFS.
Sergey Frolov, Clara Draper, Daryl Kleist, Jeffrey S. Whitaker
Active
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles
JTTI
Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
The JEDI solvers are currently undergoing testing for initialization of the atmospheric,
ocean, ice, land, and atmospheric composition components of the UFS model. However, the
current work on the testing of the JEDI EnKF solvers is performed in isolation and at a lower
readiness level (especially in the atmosphere). As a result, the next version of the GEFS system
will possibly need to use a combination of JEDI components (ocean, ice, land) and the legacy
software (GSI for atmosphere). Acceleration of the transition to the full JEDI solver suite was
identified as a priority during the 2021 UFS R2O meeting. This proposal will focus the resources
of the NOAA PSL and NOAA EMC teams on accelerating this transition, with the focus on
initialization of the ensembles for the coupled UFS model. This will be achieved through a
combination of hierarchical demonstrations culminating in a pre-operational configuration of
the GEFS system.
This project will benefit NOAA by further developing a JEDI-based solution to initialize ensembles of the coupled UFS models for the medium-range Global Ensemble Forecast System (GEFS) and for the extended-range Seasonal Forecast System (SFS). This project will also lead to the replacement of the existing legacy. GSI-based ensemble solvers with JEDI-based solvers.
EMC
Cory Martin (EMC)
NOAA/OAR/PSL
NOAA/NWS/EMC
WPO22-JTTI-I-2
Relevant to All Hazards
Colorado, Maryland
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2025
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles
accelerating transition to jedi ensemble solvers for coupled data assimilation in the gefs system, this project focuses the efforts of the noaa oar psl and noaa nws emc on accelerated transition of the jedi enkf solver in the context of the future coupled gefs implementation within the ufs, the jedi solvers are currently undergoing testing for initialization of the atmospheric ocean ice land and atmospheric composition components of the ufs model however the current work on the testing of the jedi enkf solvers is performed in isolation and at a lower readiness level especially in the atmosphere as a result the next version of the gefs system will possibly need to use a combination of jedi components ocean ice land and the legacy software gsi for atmosphere acceleration of the transition to the full jedi solver suite was identified as a priority during the 2021 ufs r2o meeting this proposal will focus the resources of the noaa psl and noaa emc teams on accelerating this transition with the focus on initialization of the ensembles for the coupled ufs model this will be achieved through a combination of hierarchical demonstrations culminating in a pre operational configuration of the gefs system
Environmental Modeling Center (EMC)
NOAA/OAR/PSL, NOAA/NWS/EMC
This project focuses the efforts of the NOAA OAR PSL and NOAA NWS EMC on accelerated transition of the JEDI EnKF solver in the context of the future coupled GEFS implementation within the UFS.
0
Hazard Services: Modernizing Weather Prediction Center, Ocean Prediction Center, National Hurricane Center/Tropical Analysis and Forecasting Branch, and Honolulu Weather Forecast Office Workflows
Hardin
JTTI
search_activity
2022
Level 3
This project proposes to transition the Excessive Rainfall Outlook and Mesoscale Preciptation Discussion from the Weather Prediction Center, as well as the High Seas Warning product from the Ocean Prediction Center into NWS operations on AWIPS using Hazard Services.
Nathan Hardin, Taylor Trogdon, Daniel Nietfield, Darrel Kingfield, David Tomalak, James Nelson
Active
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Advanced Weather Interactive Processing System (AWIPS)
The proposed work for this project is to transition the WPC's ERO, MPD, and the
High Seas Warning product suite into operations on AWIPS-II using Hazard Services. This
project would build upon progress made by the FY19 "Hazard Services: National Center
Evolve" project to establish a pathway for additional NCEP products to be operationalized in
AWIPS-II using Hazard Services, reducing reliance upon the outdated N-AWIPS system.
Operationalization will be achieved by (1) conducting iterative software development informed
by feedback from forecaster evaluations from the initial FY19 JTTI, (2) continuing forecaster
evaluations and testing using the existing Hazard Services process as described in section 1.6,
and (3) working closely with NWS Office of Central Processing (OCP) to transition these
workflows into the AWIPS baseline for operationalization.
In addition to facilitating the transition of these operational products off of N-AWIPS and onto the AWIPS system, using Hazard Services allows for modernization in the creation and dissemination of said products. Hazard Services allows the Weather Prediction Center products to be created on a common platform for the National Weather Service, and allows for future integration into the recommender interface. The recommender interface can be used to harness AI and machine learning technology to automatically recommend a first guess field for the three workflows.
For the High Seas Warning Suite, this project allows for a modernization in the way the legacy text product is constructed. The central registry provides an interface for full collaboration between the three receiving offices, and a seamless high seas text product. In addition, a curated suite of outputs are created (e.g. JSON, XML, GML, CAP, Text), allowing for increased IDSS for partners and users of the high seas warnings. The S-412 compliant GML, in particular, will be used to disseminate high seas hazards for display onboard ship navigational displays. This will, for the first time, allow mariners to see a visual representation of hazardous marine weather and conditions.
NWS/OCP
Ashley Kells (OCP)
OAR/GSL/WPC/CIRA
WPO22-JTTI-I-3
Water Extremes
Colorado
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2025
Model & Forecast Guidance
hazard services modernizing weather prediction center ocean prediction center national hurricane center tropical analysis and forecasting branch and honolulu weather forecast office workflows, this project proposes to transition the excessive rainfall outlook and mesoscale preciptation discussion from the weather prediction center as well as the high seas warning product from the ocean prediction center into nws operations on awips using hazard services, the proposed work for this project is to transition the wpc's ero mpd and the high seas warning product suite into operations on awips ii using hazard services this project would build upon progress made by the fy19 "hazard services national center evolve" project to establish a pathway for additional ncep products to be operationalized in awips ii using hazard services reducing reliance upon the outdated n awips system operationalization will be achieved by 1 conducting iterative software development informed by feedback from forecaster evaluations from the initial fy19 jtti 2 continuing forecaster evaluations and testing using the existing hazard services process as described in section 1 6 and 3 working closely with nws office of central processing ocp to transition these workflows into the awips baseline for operationalization
OAR/GSL/WPC/CIRA
This project proposes to transition the Excessive Rainfall Outlook and Mesoscale Preciptation Discussion from the Weather Prediction Center, as well as the High Seas Warning product from the Ocean Prediction Center into NWS operations on
0
Transition and evaluation of Snow-to-Liquid Ratio algorithms into the Unified Post Processor for UFS medium range and short range applications
Chuang
JTTI
check_circle
2022
Level 3
This project aims to transition two Snow-to-Liquid Ratio (SLR) algorithms, currently in use by the Weather Prediction Center (WPC), to NCEP's Unified Post Processor (UPP) which supports post processing for all NOAA's Unified Forecast System (UFS) applications.
Hui-Ya Chuang, , Geoffrey Manikin, , Jason Levit
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting, Post-processing
JTTI
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Forecast System (GFS), Rapid Refresh (RAP), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS), UFS-Medium Range Weather Application (MRWA), UFS-Short-Range Weather Application (SRWA), Unified Post Processor (UPP)
The National Weather Service Environmental Modeling Center (EMC) is planning to
transition both types of SLR algorithms into its Unified Post Processor (UPP) as the first step.
The advantages to transitioning the SLR algorithm into the UPP include 1) providing an unified
centralized framework to test and output SLR for all NOAA operational models, including UFS
Medium-Range Weather (MRW, currently the GFS) and Short-Range Weather (SRW, soon to be
the Rapid Refresh Forecast System) applications; 2) providing more accurate forecasts by using
the highest resolution model output ingested by UPP; 3) delivering SLR products faster by
reducing another layer of IO and taking advantage of existing UPP MPI framework. The end goal is to develop a SLR physics module containing both SLR algorithms that can be easily transferred to the FV3 Physics suite. This will ensure consistency in snow accumulation between the output directly from the model integration and the post-processed output.
The final outcome is to develop a Snow Liquid Ratio (SLR) physics module containing both the Roebber and Alcott-Steenburgh SLR algorithms that can be easily transferred to the UFS Physics suite.This will ensure consistency in snow accumulation between the output directly from the model integration and the post-processed output.
EMC
Jason Levit (EMC)
NOAA/NWS/NCEP/EMC
WPO22-JTTI-I-4
Winter Weather
Maryland
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2025
Atmospheric Physics, Dynamics and Nesting, Post-processing
transition and evaluation of snow to liquid ratio algorithms into the unified post processor for ufs medium range and short range applications, this project aims to transition two snow to liquid ratio slr algorithms currently in use by the weather prediction center wpc to ncep's unified post processor upp which supports post processing for all noaa's unified forecast system ufs applications, the national weather service environmental modeling center emc is planning to transition both types of slr algorithms into its unified post processor upp as the first step the advantages to transitioning the slr algorithm into the upp include 1 providing an unified centralized framework to test and output slr for all noaa operational models including ufs medium range weather mrw currently the gfs and short range weather srw soon to be the rapid refresh forecast system applications 2 providing more accurate forecasts by using the highest resolution model output ingested by upp 3 delivering slr products faster by reducing another layer of io and taking advantage of existing upp mpi framework the end goal is to develop a slr physics module containing both slr algorithms that can be easily transferred to the fv3 physics suite this will ensure consistency in snow accumulation between the output directly from the model integration and the post processed output
Environmental Modeling Center (EMC)
NOAA/NWS/NCEP/EMC
This project aims to transition two Snow-to-Liquid Ratio (SLR) algorithms, currently in use by the Weather Prediction Center (WPC), to NCEP's Unified Post Processor (UPP) which supports post processing for all NOAA's Unified Forecast System
0
Further Development of the Unified Gravity Wave Physics Suite for the UFS
Olson
JTTI
check_circle
2022
Level 3
The objective of this project is to further improve and tune the Unified Gravity Wave Physics (UGWP) suite to maximize the UFS forecast skill in terms of upper level winds, surface winds, and the 500hPa Anomaly Correlation Coefficient (ACC).
Joseph Olson, , Michael Toy, , Fanglin Yang
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting, Forecast Skill Metrics, Parameterization
JTTI
Common Community Physics Package (CCPP), Global Forecast System (GFS), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
The use of gravity wave drag (GWD) schemes in global models has a great impact on
forecast skill metrics, such as the 500 mb height anomaly correlation coefficient (ACC). For example, in the FV3GFS with the global C768 (x~13km) grid, there is a 3% improvement in the ACC by forecast day 5 attributable to the GWD parameterization alone. Therefore, it is vital to have continued support of GWD scheme development for NOAA's Unified Forecast System (UFS).
The GSL gravity wave drag suite has been unified with EMC's gravity wave drag scheme to form Version 1 of the Unified Gravity Wave Physics (UGWPv1) Suite. This new suite is a more comprehensive representation of gravity wave physics and is slated for operational use in the UFS at all scales. Despite the reasonable early success of UGWPv1 in the UFS, considerable development opportunities still exist within the suite, such as further calibration of each subcomponent to improve the representation of their distinct physical processes and better calibrate the scale-aware functionality for specific applications. The work proposed in this project will allow for continuation of the testing and development of the UGWPv1 suite at both
global and regional applications to fulfill shorter-term goals associated with high-readiness level R2O application support.
Updated UFS model physics code that enhances the representation of parameterized gravity wave physics for all operational applications at all scales. The proposed project will improve the scale-aware UGWPv1 suite, which is a component of the Common Community Physics Package (CCPP), and will improve the performance of both the GFS and RRFS as well as any other UFS application. The final form of the UGWPv1 suite is planned to be at least as comprehensive and cutting edge as any gravity wave drag representation found in any other operational center.
EMC
Fanglin Yang (EMC)
NOAA/OAR/GSL
WPO22-JTTI-I-5
Relevant to All Hazards
Colorado
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2025
Atmospheric Physics, Dynamics and Nesting, Forecast Skill Metrics, Parameterization
further development of the unified gravity wave physics suite for the ufs, the objective of this project is to further improve and tune the unified gravity wave physics ugwp suite to maximize the ufs forecast skill in terms of upper level winds surface winds and the 500hpa anomaly correlation coefficient acc, the use of gravity wave drag gwd schemes in global models has a great impact on forecast skill metrics such as the 500 mb height anomaly correlation coefficient acc for example in the fv3gfs with the global c768 x~13km grid there is a 3% improvement in the acc by forecast day 5 attributable to the gwd parameterization alone therefore it is vital to have continued support of gwd scheme development for noaa's unified forecast system ufs the gsl gravity wave drag suite has been unified with emc's gravity wave drag scheme to form version 1 of the unified gravity wave physics ugwpv1 suite this new suite is a more comprehensive representation of gravity wave physics and is slated for operational use in the ufs at all scales despite the reasonable early success of ugwpv1 in the ufs considerable development opportunities still exist within the suite such as further calibration of each subcomponent to improve the representation of their distinct physical processes and better calibrate the scale aware functionality for specific applications the work proposed in this project will allow for continuation of the testing and development of the ugwpv1 suite at both global and regional applications to fulfill shorter term goals associated with high readiness level r2o application support
Environmental Modeling Center (EMC)
NOAA/OAR/GSL
The objective of this project is to further improve and tune the Unified Gravity Wave Physics (UGWP) suite to maximize the UFS forecast skill in terms of upper level winds, surface winds, and the 500hPa
1
The Continued Evolution of Severe Weather Warnings through Threats-in-Motion
Berry
JTTI
search_activity
2022
Level 3
This project focuses on developing a real-time capability for Threats-in-Motion (TIM) by ensuring: robust inter-office collaboration capability between multiple NWS weather forecast offices; warning output that meets NWS dissemination requirements; development of a preliminary forecaster training plan; and addressing mitigation efforts previously identified by broadcast meteorologists and emergency managers.
Kodi Berry, Daniel Nietfeld, Judy Ghirardelli
Active
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Probabilistic Weather Forecasting, Flow of Weather Risk Information, "Social, Behavioral, and Economic Sciences (SBES)"
JTTI
Advanced Weather Interactive Processing System (AWIPS)
Threats-In-Motion (TIM) is a proposed warning methodology that would enable
NWS forecasters to advance severe thunderstorm and tornado warnings from the current static
polygon system to continuously-updating warning polygons that move with the storm. TIM
represents an initial step in shifting the current NWS convective warning paradigm toward a
more continuous flow of information. TIM can provide more uniform, or equitable, lead times to
core partners and the public. With TIM, the forecaster only needs to issue a single warning,
updating regularly as they see fit, embodying a 3one storm-one story concept. This approach
can reduce forecaster workload because downstream warning issuance would be replaced by a
less time-consuming warning update. In addition, TIM can provide a continuous history for each
storm, leading to simplified and consistent messaging for key partners and improving event
verification. This proposal seeks to continue development, evaluation, and testing of TIM, and to
bring the AWIPS Hazard Services-TIM tool to Readiness Level 8.
Threats-In-Motion (TIM) can provide more uniform, or equitable, lead times to core partners and the public. With TIM, the forecaster only needs to issue a single warning, updating regularly as they see fit, embodying a one storm-one story concept.
NWS/OCP
Ashley Kells (OCP)
NOAA/OAR/NSSL
WPO22-JTTI-I-6
Severe Weather
Oklahoma
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2025
Model & Forecast Guidance, Probabilistic Weather Forecasting, Flow of Weather Risk Information, "Social Science (SBES)"
the continued evolution of severe weather warnings through threats in motion, this project focuses on developing a real time capability for threats in motion tim by ensuring robust inter office collaboration capability between multiple nws weather forecast offices warning output that meets nws dissemination requirements development of a preliminary forecaster training plan and addressing mitigation efforts previously identified by broadcast meteorologists and emergency managers, threats in motion tim is a proposed warning methodology that would enable nws forecasters to advance severe thunderstorm and tornado warnings from the current static polygon system to continuously updating warning polygons that move with the storm tim represents an initial step in shifting the current nws convective warning paradigm toward a more continuous flow of information tim can provide more uniform or equitable lead times to core partners and the public with tim the forecaster only needs to issue a single warning updating regularly as they see fit embodying a 3one storm one story concept this approach can reduce forecaster workload because downstream warning issuance would be replaced by a less time consuming warning update in addition tim can provide a continuous history for each storm leading to simplified and consistent messaging for key partners and improving event verification this proposal seeks to continue development evaluation and testing of tim and to bring the awips hazard services tim tool to readiness level 8
NOAA/OAR/NSSL
This project focuses on developing a real-time capability for Threats-in-Motion (TIM) by ensuring: robust inter-office collaboration capability between multiple NWS weather forecast offices; warning output that meets NWS dissemination requirements; development of a preliminary forecaster
0
Transitioning Critical MET Supported Software to Operations at the Weather Prediction Center
Nelson
JTTI
search_activity
2022
Level 3
This project aims to transition several experimetal products and websites for heavy preciptation tracking, snowband tracking, improvements to first-guess field for the Excessive Rainfall Outlook (ERO), and enhancements to ERO verification into WPC operations.
James Nelson, Michael Erickson
Active
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Flash flooding is one of the most deadly and damaging weather phenomena in the United
States resulting in 415 fatalities and $66.5 billion in damages between 2015 and 2019 (National
Weather Service [NWS], 2019). The NWS Weather Prediction Center (WPC) is the nation's
expert in forecasting heavy rain events and produces two high profile forecast products: The
Mesoscale Precipitation Discussion (MPD) for short term (e.g., 0 to 6 hours) flash flood threats
and the Excessive Rainfall Outlook (ERO) for flash flood threats extending out to three days. To
support the MPD and ERO, WPC has developed several novel experimental products that exist
at high readiness levels (e.g. RL 6 to 7), but are not fully operational. This project will finish the
development and transition several verification and model post-processing projects to
operations, providing a plethora of new information for NWS forecasters and the public. Specifically, an hourly and sub-hourly heavy precipitation tracking website will be
enhanced and transitioned to operations, allowing forecasters to assess displacement and
uncertainty of heavy precipitation objects leading to flash flooding in real-time. A similar
experimental snowband tracking website will be optimized and transitioned to operations. An
experimental first-guess field for the ERO will be improved, providing forecasters with a helpful
starting point when drawing their forecast contours. Finally, the current ERO verification
database will be improved, applied to the MPD, and transitioned to operations.
Detecting and Tracking precipitation objects (heavy snowfall and intense rainfall) provides a step forward in meeting the forecast needs to help quantify impact events.
WPC
Jim Nelson (WPC)
NOAA/NWS/NCEP/WPC
WPO22-JTTI-I-7
Water Extremes
Maryland
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2025
Post-processing, Verification & Validation
transitioning critical met supported software to operations at the weather prediction center, this project aims to transition several experimetal products and websites for heavy preciptation tracking snowband tracking improvements to first guess field for the excessive rainfall outlook ero and enhancements to ero verification into wpc operations, flash flooding is one of the most deadly and damaging weather phenomena in the united states resulting in 415 fatalities and $66 5 billion in damages between 2015 and 2019 national weather service [nws] 2019 the nws weather prediction center wpc is the nation's expert in forecasting heavy rain events and produces two high profile forecast products the mesoscale precipitation discussion mpd for short term e g 0 to 6 hours flash flood threats and the excessive rainfall outlook ero for flash flood threats extending out to three days to support the mpd and ero wpc has developed several novel experimental products that exist at high readiness levels e g rl 6 to 7 but are not fully operational this project will finish the development and transition several verification and model post processing projects to operations providing a plethora of new information for nws forecasters and the public specifically an hourly and sub hourly heavy precipitation tracking website will be enhanced and transitioned to operations allowing forecasters to assess displacement and uncertainty of heavy precipitation objects leading to flash flooding in real time a similar experimental snowband tracking website will be optimized and transitioned to operations an experimental first guess field for the ero will be improved providing forecasters with a helpful starting point when drawing their forecast contours finally the current ero verification database will be improved applied to the mpd and transitioned to operations
Weather Prediction Center (WPC)
NOAA/NWS/NCEP/WPC
This project aims to transition several experimetal products and websites for heavy preciptation tracking, snowband tracking, improvements to first-guess field for the Excessive Rainfall Outlook (ERO), and enhancements to ERO verification into WPC operations.
0
Operationalizing the computation and dissemination of the Evaporative Demand Drought Index (EDDI) and value-added products for CONUS-wide drought monitoring and early warning at the NOAA-Climate Prediction Center
Abel
JTTI
check_circle
2022
Level 3
This project focuses on transitioning both the estimation and the end-user dissemination of Evaporative Demand Drought Index (EDDI) to CPC and to add value-added products requested by end-users.
Mimi Hughes Abel, Hailan Wang, Mark Glaudemans, Mike Hobbins, Brad Pugh, Andrea Ray
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
This proposal seeks to transition both the estimation and the end-user dissemination of EDDI
to CPC and to add value-added products requested by end-users. Value-added products include
maps and data (i) specific to individual stakeholders, (ii) on temporal changes in EDDI, and (iii)
on live attribution of the changes in E0 that drive EDDI. These have been co-developed with end-
users and stakeholders since the completion of the previous transition project.
Our goal is to advance the EDDI service to RL 9, by operationalizing the computation and dissemination of the EDDI from CPC to all NOAA stakeholders.
CPC
David Dewitt (CPC)
OAR/PSL/CIRES/CPC
WPO22-JTTI-I-8
Water Extremes
Colorado
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2024
operationalizing the computation and dissemination of the evaporative demand drought index eddi and value added products for conus wide drought monitoring and early warning at the noaa climate prediction center, this project focuses on transitioning both the estimation and the end user dissemination of evaporative demand drought index eddi to cpc and to add value added products requested by end users, this proposal seeks to transition both the estimation and the end user dissemination of eddi to cpc and to add value added products requested by end users value added products include maps and data i specific to individual stakeholders ii on temporal changes in eddi and iii on live attribution of the changes in e0 that drive eddi these have been co developed with end users and stakeholders since the completion of the previous transition project
Climate Prediction Center (CPC)
OAR/PSL/CIRES/CPC
This project focuses on transitioning both the estimation and the end-user dissemination of Evaporative Demand Drought Index (EDDI) to CPC and to add value-added products requested by end-users.
0
Transitioning Bayesian Joint Probability (BJP) Calibration of Week 3-4 Predictions to Operations
Collins
JTTI
check_circle
2022
Level 3
The goal of this project is to apply Bayesian Joint Probability (BJP) methodology to operational models that support week 3-4 precipitation forecasts at CPC and transitioning these codes to the CPC Compute Farm.
Dan Collins, Johnna Infanti
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting
JTTI
Climate Forecast System (CFS), Coupled Forecast System (CFSv2), Global Ensemble Forecast System (GEFS), North American Multi-Model Ensemble (NMME), Unified Forecast System (UFS)
Week 3-4 outlooks at the Climate Prediction Center (CPC) rely on a variety of
dynamical and statistical tools, such as week 3-4 forecasts from operational models including the
Climate Forecast System version 2 (CFSv2), European Centre for Medium-Range Weather
Forecasts (ECMWF), and NOAA Global Ensemble Forecast System version 12 (GEFSv12).
However, while helpful for informing outlooks, dynamical climate models can be systematically
biased and lack reliability of probabilities in temperature and precipitation forecasts. Statistical
calibration of models can improve bias and reliability beyond the potential gains of advances in
dynamical models, and without the need to make changes to the base model code, necessitating
an updated model release. Currently, CFSv2, ECMWF, and the Japan Meteorological Agency
(JMA) models are calibrated for week 3-4 using ensemble regression. However, a novel
calibration method based on Bayesian statistics for the week 3-4 and seasonal timescales -
Bayesian Joint Probability (BJP) calibration - has shown to improve skill and reliability of
2-meter temperature over raw dynamical models in a test environment using three Subseasonal
Prediction Experiment (SubX) models; CFSv2, GEFSv12, and the NOAA Earth System
Research Laboratories (ESRL-FIMv2) model; and to improve the skill and reliability of seasonal
precipitation predictions when applied to the North American Multi-Model Ensemble (NMME).
Given the success of BJP calibration for week 3-4 2-meter temperature, and seasonal 2-meter
temperature and precipitation, we propose to (1) apply the BJP calibration methodology to the
operational models that support week 3-4 forecasts at CPC including CFSv2, ECMWF,
GEFSv12, and the Unified Forecast System (UFS) (2) apply the BJP calibration methodology
that successful on seasonal timescales to week 3-4 precipitation from CFSv2, ECMWF,
GEFSv12, and UFS; and (3) transfer BJP code and modules to the Compute Farm (CF).
Realtime Bayesian Joiunt Probability (BJP) calibrated forecasts will be assessed along with other operational models and tools.
CPC
David Dewitt (CPC)
NOAA/NWS/NCEP/CPC
WPO22-JTTI-I-9
Relevant to All Hazards
Maryland
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2024
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting
transitioning bayesian joint probability bjp calibration of week 3 4 predictions to operations, the goal of this project is to apply bayesian joint probability bjp methodology to operational models that support week 3 4 precipitation forecasts at cpc and transitioning these codes to the cpc compute farm, week 3 4 outlooks at the climate prediction center cpc rely on a variety of dynamical and statistical tools such as week 3 4 forecasts from operational models including the climate forecast system version 2 cfsv2 european centre for medium range weather forecasts ecmwf and noaa global ensemble forecast system version 12 gefsv12 however while helpful for informing outlooks dynamical climate models can be systematically biased and lack reliability of probabilities in temperature and precipitation forecasts statistical calibration of models can improve bias and reliability beyond the potential gains of advances in dynamical models and without the need to make changes to the base model code necessitating an updated model release currently cfsv2 ecmwf and the japan meteorological agency jma models are calibrated for week 3 4 using ensemble regression however a novel calibration method based on bayesian statistics for the week 3 4 and seasonal timescales bayesian joint probability bjp calibration has shown to improve skill and reliability of 2 meter temperature over raw dynamical models in a test environment using three subseasonal prediction experiment subx models cfsv2 gefsv12 and the noaa earth system research laboratories esrl fimv2 model and to improve the skill and reliability of seasonal precipitation predictions when applied to the north american multi model ensemble nmme given the success of bjp calibration for week 3 4 2 meter temperature and seasonal 2 meter temperature and precipitation we propose to 1 apply the bjp calibration methodology to the operational models that support week 3 4 forecasts at cpc including cfsv2 ecmwf gefsv12 and the unified forecast system ufs 2 apply the bjp calibration methodology that successful on seasonal timescales to week 3 4 precipitation from cfsv2 ecmwf gefsv12 and ufs and 3 transfer bjp code and modules to the compute farm cf
Climate Prediction Center (CPC)
NOAA/NWS/NCEP/CPC
The goal of this project is to apply Bayesian Joint Probability (BJP) methodology to operational models that support week 3-4 precipitation forecasts at CPC and transitioning these codes to the CPC Compute Farm.
0
Transitioning HYSPLIT to Operations
Crawford
JTTI
check_circle
2022
Level 3
This proposal advances two capabilities: the use of NOAA's ensemble forecasting systems to produce probabilistic forecasts and the implementation of a Transfer Coefficient Matrix, TCM, approach for HYSPLIT.
Alice Crawford, Tianfang Chai, Jeff McQueen
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Aviation, Data Assimilation and Ensembles, Probabilistic Weather Forecasting
JTTI
Global Ensemble Forecast System (GEFS), High Resolution Ensemble Forecast (HREF), Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Rapid Refresh Forecast System (RRFS)
The HYSPLIT model, developed and maintained at NOAA Air Resources Laboratory
(ARL), is one of the mostly widely used models for atmospheric trajectory and dispersion
calculations in the world (Stein and Draxler et. al 2015). It is NOAA's operational model for
emergency response applications including the simulation of atmospheric plumes from
radionuclide emissions, chemical releases, smoke originating from wildfires or prescribed burns,
volcanic eruptions, and wind-blown dust. Several HYSPLIT applications are close to
transitioning to operations including probabilistic products and the Transfer Coefficient Matrix,
TCM, approach for nuclear release applications. Probabilistic products for volcanic ash
developed under the JTTI FY18 project will help fulfill new requirements expected from the
International Civil Aviation Organization, ICAO, for volcanic ash advisories issued by NOAA's
two Volcanic Ash Advisory Centers. The TCM approach for nuclear release applications will
fulfill requests by the International Atomic Energy Agency, IAEA, and the World Meteorological
Organization, WMO. In future projects, the TCM approach can be further expanded to support
top-down estimation of wildfire smoke emissions as well as volcanic ash emissions and utilize
ensemble output.
In emergency response situations, uncertainty in the magnitudes of modeled concentrations may be up to a few orders of magnitude due to uncertainty in the source term. However, this uncertainty often decreases as better emissions estimates become available. During an emergency, estimates of emissions are often updated frequently as new measurements and information becomes available. With a Transfer COefficient Matrix (TCM) approach, model forecasts do not have to be re-run every time new emissions estimates are available. Simulations may even be completed before any emissions estimates are available, e.g. in the case of an ongoing event in which future emissions are expected but unknown. It also allows for efficient implementation of source term ensembles. When emissions are uncertain, an ensemble of possible emissions may be utilized to produce probabilistic output. The TCM approach allows the same set of model runs to be used for different emissions estimates. The TCM is also utilized in an inversion algorithm along with observations to produce estimates of emissions. This approach is effective for estimating emissions from volcanoes, wildfires, and radiological releases (Chai et. al 2015, Chai et. al 2017, Cheol et. al. 2020). Such emissions estimates can then be fed back into the Unified Forecast System (UFS) system.
EMC
Jeff McQueen (EMC)
NOAA/OAR/ARL
WPO22-JTTI-I-10
Relevant to All Hazards
Maryland
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2025
Atmospheric Composition and Chemistry, Aviation, Data Assimilation and Ensembles, Probabilistic Weather Forecasting
transitioning hysplit to operations, this proposal advances two capabilities the use of noaa's ensemble forecasting systems to produce probabilistic forecasts and the implementation of a transfer coefficient matrix tcm approach for hysplit, the hysplit model developed and maintained at noaa air resources laboratory arl is one of the mostly widely used models for atmospheric trajectory and dispersion calculations in the world stein and draxler et al 2015 it is noaa's operational model for emergency response applications including the simulation of atmospheric plumes from radionuclide emissions chemical releases smoke originating from wildfires or prescribed burns volcanic eruptions and wind blown dust several hysplit applications are close to transitioning to operations including probabilistic products and the transfer coefficient matrix tcm approach for nuclear release applications probabilistic products for volcanic ash developed under the jtti fy18 project will help fulfill new requirements expected from the international civil aviation organization icao for volcanic ash advisories issued by noaa's two volcanic ash advisory centers the tcm approach for nuclear release applications will fulfill requests by the international atomic energy agency iaea and the world meteorological organization wmo in future projects the tcm approach can be further expanded to support top down estimation of wildfire smoke emissions as well as volcanic ash emissions and utilize ensemble output
Environmental Modeling Center (EMC)
NOAA/OAR/ARL
This proposal advances two capabilities: the use of NOAA's ensemble forecasting systems to produce probabilistic forecasts and the implementation of a Transfer Coefficient Matrix, TCM, approach for HYSPLIT.
0
Atmospheric Composition Data Assimilation for the Unified Forecast System (UFS) on Global and Regional Scales
Martin
JTTI
search_activity
2022
Level 3
This proposal aims to implements an initial aerosol data assimilation capability into the UFS Medium Range Weather (MRW) and Subseasonal-to-Seasonal (S2S) applications, as well as continuing development of trace gas and aerosol data assimilation for future versions of the Rapid Refresh Forecast System (RRFS).
Cory Martin, Daryl Kleist, Jeff McQueen, Ivanka Stajner, Sonny Zinn, Binyu Wang, Mark Cohen
Active
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles
JTTI
Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS), UFS-Medium Range Weather Application (MRWA), UFS-Season to Subseasonal (UFS-S2S)
As part of NOAA's Unified Forecast System (UFS), the National Weather Service is building
with partners a coupled modeling system across spatial and temporal scales. Aerosols and
atmospheric composition are being included in the UFS for improved subseasonal-to-seasonal
prediction, and air quality forecasts, respectively. Building upon prior work demonstrating initial
global aerosol and regional air quality assimilation capabilities, we propose final evaluation and
tuning to implement initial aerosol data assimilation capabilities using the Joint Effort for Data
assimilation Integration (JEDI) system as part of the UFS Medium-Range Weather (MRW) and
Subseasonal-to-Seasonal (S2S) applications. In addition to the more mature global aerosol
assimilation system, we also propose continued development of regional aerosol and trace gas
assimilation for a future version of the Rapid Refresh Forecast System. For the global aerosol
analysis system, the focus will be on a year-long retrospective analysis and evaluation of
performance with and without assimilation compared to independent observations as well as
determining the optimal configuration (analysis resolution, hybrid-3DEnVar vs 3DVar, etc.) for
the global aerosol analysis system considering scientific performance and
operational/computational constraints. Additionally, this plan will include creation and
refinement of static background covariance matrices for each system, the exploration of
variational quality control for aerosol optical depth observations, and development of new
observation operators and quality control capabilities for additional observations for the regional
system.
Using Data Assimilation (DA) to constrain both global aerosol species in the coupled Earth system model for more accurate forecasts at the weather and sub-seasonal timescales and regional trace gases and aerosols for improved air quality forecasts.
EMC
Cory Martin (EMC)
NOAA/NWS/NCEP/EMC
WPO22-JTTI-I-11
Relevant to All Hazards
Maryland
Joint Technology Transfer Initiative (JTTI)
May 2022 - April 2025
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles
atmospheric composition data assimilation for the unified forecast system ufs on global and regional scales, this proposal aims to implements an initial aerosol data assimilation capability into the ufs medium range weather mrw and subseasonal to seasonal s2s applications as well as continuing development of trace gas and aerosol data assimilation for future versions of the rapid refresh forecast system rrfs, as part of noaa's unified forecast system ufs the national weather service is building with partners a coupled modeling system across spatial and temporal scales aerosols and atmospheric composition are being included in the ufs for improved subseasonal to seasonal prediction and air quality forecasts respectively building upon prior work demonstrating initial global aerosol and regional air quality assimilation capabilities we propose final evaluation and tuning to implement initial aerosol data assimilation capabilities using the joint effort for data assimilation integration jedi system as part of the ufs medium range weather mrw and subseasonal to seasonal s2s applications in addition to the more mature global aerosol assimilation system we also propose continued development of regional aerosol and trace gas assimilation for a future version of the rapid refresh forecast system for the global aerosol analysis system the focus will be on a year long retrospective analysis and evaluation of performance with and without assimilation compared to independent observations as well as determining the optimal configuration analysis resolution hybrid 3denvar vs 3dvar etc for the global aerosol analysis system considering scientific performance and operational computational constraints additionally this plan will include creation and refinement of static background covariance matrices for each system the exploration of variational quality control for aerosol optical depth observations and development of new observation operators and quality control capabilities for additional observations for the regional system
Environmental Modeling Center (EMC)
NOAA/NWS/NCEP/EMC
This proposal aims to implements an initial aerosol data assimilation capability into the UFS Medium Range Weather (MRW) and Subseasonal-to-Seasonal (S2S) applications, as well as continuing development of trace gas and aerosol data assimilation for
0
Transitioning Weather-Aware Rapid Refresh Emission Modeling Capability (WAR2-EMC) to Support National Air Quality Forecast Capability Operations
Baek
JTTI
search_activity
2022
Level 3
The project aims to integrate a new system (WAR2EMC) for weather-aware emission modeling into the NAQFC system to improve air quality forecasts.
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, High-Performance Computing (HPC)
JTTI
NOAA's National Air Quality Forecasting Capability (NAQFC), Rapid Refresh (RAP), Unified Forecast System (UFS)
We plan to enhance the NAQFC modeling system with our latest Weather-Aware Rapid Refresh
Emissions Modeling Capability (WAR2EMC) system developed from the ongoing NAQFC
Community Emission Testbed (NCET) project that accelerates emission research to operation
(R2O) transition funded by NOAA [NA19OAR4590081]. It will have a readiness level (RL) of 7-
8 by the end of June 2022. We expect this proposed work to be at RL 9 once we successfully
deploy the WAR2EMC to the NOAA operational environment. The goals of this proposed project
mainly include:
1) Transferring the latest emissions to NAQFC
2) Deployment of WAR2EMC to inline NAQFC
3) Evaluation of inline NAQFC with WAR2EMC
It is expected that this work will significantly improve the temporal and spatial representations of
meteorology-induced emission sources with the meteorology predictions from NAQFC, which
could result in improving the regional atmospheric chemicals and aerosol predictions, especially
during the high ozone and PM2.5 episodes. We will work closely with NOAA NCEP/EMC team
to successfully implement the WAR2EMC into the latest regional inline/offline NAQFC, and
perform the complete evaluations. The work will be performed primarily on NOAA
NOAA/NCEP's operational machines, the Weather Climate Operational Supercomputing System
(WCOSS) and R&D High Performance Computing System (RDHPCS: Hera), to expedite the
operational implementation with the supports from our co-Investigators (Drs. Youhua Tang and
Patrick Campbell) and collaborator Rick Saylor from ARL and Jeff McQueen from NCEP/EMC.
Exposures to air pollution cause 7 million premature deaths per year, making it the single largest environmental risk today. Air quality forecasting is one of the key tools commonly used by state and local agencies to protect the public from adverse health effects of poor air quality. In the United States, the NOAA's National Air Quality Forecast Capability Operations (NAQFC) provides full chemistry forecasts of ozone and PM2.5for up to 72 hours over north America. Emission is a key input to air quality forecasting systems such as the NAQFC and Next Generation Global PRediction System (NGGPS) FInite-Volume Cubed-Spherer Dynamical Core-CHemistry (FV3)-chem. In the past, operational forecasts benefited greatly from emission improvements made by the scientific community. We believe that NAQFC Community Emission Testbed (NCET) will provide a common modeling platform for the community to contribute to the emissions update in NAQFC and FV3-chem.
EMC
Jeff McQueen (EMC)
George Mason University
NOAA/OAR/ARL
NA22OAR4590167
Relevant to All Hazards
Virginia
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Atmospheric Composition and Chemistry, High-Performance Computing (HPC)
transitioning weather aware rapid refresh emission modeling capability war2 emc to support national air quality forecast capability operations, the project aims to integrate a new system war2emc for weather aware emission modeling into the naqfc system to improve air quality forecasts, we plan to enhance the naqfc modeling system with our latest weather aware rapid refresh emissions modeling capability war2emc system developed from the ongoing naqfc community emission testbed ncet project that accelerates emission research to operation r2o transition funded by noaa [na19oar4590081] it will have a readiness level rl of 7 8 by the end of june 2022 we expect this proposed work to be at rl 9 once we successfully deploy the war2emc to the noaa operational environment the goals of this proposed project mainly include 1 transferring the latest emissions to naqfc 2 deployment of war2emc to inline naqfc 3 evaluation of inline naqfc with war2emc it is expected that this work will significantly improve the temporal and spatial representations of meteorology induced emission sources with the meteorology predictions from naqfc which could result in improving the regional atmospheric chemicals and aerosol predictions especially during the high ozone and pm2 5 episodes we will work closely with noaa ncep emc team to successfully implement the war2emc into the latest regional inline offline naqfc and perform the complete evaluations the work will be performed primarily on noaa noaa ncep's operational machines the weather climate operational supercomputing system wcoss and r d high performance computing system rdhpcs hera to expedite the operational implementation with the supports from our co investigators drs youhua tang and patrick campbell and collaborator rick saylor from arl and jeff mcqueen from ncep emc
Environmental Modeling Center (EMC)
George Mason University, NOAA/OAR/ARL
The project aims to integrate a new system (WAR2EMC) for weather-aware emission modeling into the NAQFC system to improve air quality forecasts.
5
Deep Learning Hybrid Dynamical-statistical model for US Precipitation Subseasonal Forecasting
Chang
JTTI
search_activity
2022
Level 3
The goal of this project is to develop a Deep Learning Hybrid Dynamical-Statistical model that incorporates bias-corrected Madden-Julian Oscillation (MJO) projections and teleconnections to improve forecasting of US precipitation over subseasonal-to-seasonal timescales.
The goal of this project is to develop a Deep Learning Hybrid Dynamical-Statistical
model for implementation at the NOAA CPC in support of post-processing subseasonal products
to improve forecasting of US precipitation. The Deep Learning Hybrid model incorporates (i)
bias-corrected MJO forecasts and (ii) nonlinearity of MJO teleconnections using Deep Learning
(DL) approaches. A recent study by the PI has demonstrated the effectiveness of the DL-bias
correction in reduction of MJO forecast errors in S2S and SubX reforecasts. Deep learning can
also successfully capture the nonlinear sources of predictability and help identify the forecasts of
opportunity for tropical-extratropical teleconnections. The existing DL approaches will be applied
to UFS and other dynamical models. We will provide (i) bias corrected MJO forecast that will
help CPC forecasters in the decision making process and (ii) US precipitation forecasts with the
DL hybrid model based on the bias corrected MJO to be tested in a pseudo-operational setting at
CPC. The DL hybrid model will provide post-processed model that supports CPC precipitation
outlooks for weeks 1-4 and can be added as new tools for CPC forecasters to use in support of
official products. The CPC Co-PI will be responsible for guiding and facilitating the testing and
transition of project deliverable to CPC and coordinate with NOAA Climate Test Bed (CTB).
The project will transition the latest scientific knowledge and technology to sup-
port CPC operational products. This project develops a new Deep learning based hybrid model that
supports extracting actionable and critical information for the forecaster. The DL-hybrid model de-
livers both MJO forecast and US precipitation forecast. The bias-corrected MJO forecasts will help
the forecasters to review MJO activity and enhance their decision making process as well as their
official, public-facing, forecast discussions. The DL-hybrid model will provide improved forecasts
of US precipitation and enhance the performance of CPC forecasts. The hybrid model can be added
as new tools for CPC forecasters to use in support of official products. Therefore, the outcome of
this project will provide useful information to local weather offices, national operational forecast
centers, and the general public. In addition, the analysis of model biases and errors will help the
NOAA/NCEP model developers to help identify physical mechanisms that need to be improved
and will ultimately enhance the forecast skill at S2S time scales.
deep learning hybrid dynamical statistical model for us precipitation subseasonal forecasting, the goal of this project is to develop a deep learning hybrid dynamical statistical model that incorporates bias corrected madden julian oscillation mjo projections and teleconnections to improve forecasting of us precipitation over subseasonal to seasonal timescales, the goal of this project is to develop a deep learning hybrid dynamical statistical model for implementation at the noaa cpc in support of post processing subseasonal products to improve forecasting of us precipitation the deep learning hybrid model incorporates i bias corrected mjo forecasts and ii nonlinearity of mjo teleconnections using deep learning dl approaches a recent study by the pi has demonstrated the effectiveness of the dl bias correction in reduction of mjo forecast errors in s2s and subx reforecasts deep learning can also successfully capture the nonlinear sources of predictability and help identify the forecasts of opportunity for tropical extratropical teleconnections the existing dl approaches will be applied to ufs and other dynamical models we will provide i bias corrected mjo forecast that will help cpc forecasters in the decision making process and ii us precipitation forecasts with the dl hybrid model based on the bias corrected mjo to be tested in a pseudo operational setting at cpc the dl hybrid model will provide post processed model that supports cpc precipitation outlooks for weeks 1 4 and can be added as new tools for cpc forecasters to use in support of official products the cpc co pi will be responsible for guiding and facilitating the testing and transition of project deliverable to cpc and coordinate with noaa climate test bed ctb
The goal of this project is to develop a Deep Learning Hybrid Dynamical-Statistical model that incorporates bias-corrected Madden-Julian Oscillation (MJO) projections and teleconnections to improve forecasting of US precipitation over subseasonal-to-seasonal timescales.
12
Creation and evaluation of a CONUS-wide gridded analysis-of-record for ice accumulation
Reeves
JTTI
search_activity
2022
Level 3
This project looks to integrate the Freezing Rain Accumulation Model (FRAM) into the existing Multi-Radar Multi-Sensor (MRMS) system, allowing for high resolution, frequently updated ice accumulation analyses for the CONUS.
Heather Reeves, Daniel Tripp, Kris Sanders, Brian Barjenbruch, Kirstin Harnos, James Correia
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Radar, Surface Observing Networks, Verification & Validation
JTTI
Multi-Radar/Multi-Sensor System (MRMS)
Freezing rain is a dangerous form of precipitation that can cause slippery roads and power out-
ages. While the NWS generates forecasts of ice accumulations, there is limited validation data
available to evaluate the accuracy of these forecasts. Moreover, there is no real-time analysis of
ice accumulation to aid in nowcasting. The aim of this research is to produce such an analysis as
well as to evaluate the efficacy for enhancing the analysis with observations from Local Storm
Reports (LSRs) a nd ASOS.
A new algorithm for observing and documenting ice accretion on a high-resolution grid that provides 1 to 72 h accumulations at an update frequency of up to 2 min will be available to onboard to the NWS's operational version of Multi-Radar/Multi-Sensor System (MRMS). In addition, a set of recommendations for whether to adopt said algorithm will be provided along with the particular findings of the statistical evaluation and identification of edge events.
WPC
Kirstin Harnos (WPC)
CIWRO/University of Oklahoma
NA22OAR4590169
Winter Weather
Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Dynamics and Nesting, Radar, Surface Observing Networks, Verification & Validation
creation and evaluation of a conus wide gridded analysis of record for ice accumulation, this project looks to integrate the freezing rain accumulation model fram into the existing multi radar multi sensor mrms system allowing for high resolution frequently updated ice accumulation analyses for the conus, freezing rain is a dangerous form of precipitation that can cause slippery roads and power out ages while the nws generates forecasts of ice accumulations there is limited validation data available to evaluate the accuracy of these forecasts moreover there is no real time analysis of ice accumulation to aid in nowcasting the aim of this research is to produce such an analysis as well as to evaluate the efficacy for enhancing the analysis with observations from local storm reports lsrs a nd asos
Weather Prediction Center (WPC)
CIWRO/University of Oklahoma
This project looks to integrate the Freezing Rain Accumulation Model (FRAM) into the existing Multi-Radar Multi-Sensor (MRMS) system, allowing for high resolution, frequently updated ice accumulation analyses for the CONUS.
2
Expanding Audiences, Removing Barriers, Promoting Action: Addressing the diverse needs of audiences for flood forecast information
Carr
JTTI
search_activity
2022
Level 3
The objectives of this project are to understand the range of flood information needs and concerns, elicit how the various audiences prefer to receive information, and obtain feedback on the extent to which existing flood forecast products help with decision-making.
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Communicating Probabilities & Uncertainty, Decision-Making, Information Seeking & Processing, Social Vulnerability
JTTI
In this project, NNC will use its established relationships with River Forecast Centers (RFCs), the
Weather Prediction Center (WPC), and WFOs, as well as the National Water Center (NWC), to
advance its model of focus group and survey testing to reach underserved audiences in various
regions across the country, including low-income urban, rural, and tribal communities not previously
studied. The study will look at a broad suite of probabilistic and other flood forecast tools (including
hydrographs, Hydrologic Ensemble Forecast System outputs, Weather Forecast Office products and
Weather Prediction Center forecast products including the Excessive Rainfall Outlook and
Mesoscale Precipitation Discussion), for both riverine and flash flood risk, and will work in
partnership with National Weather Service (NWS) offices and local community-based organizations
(CBOs) to identify and engage with diverse audiences in regions across the country.
The study design involves close cooperation with NWS and community-based organizations (CBOs) in each of the regions to understand and reach these audiences, who might not otherwise self-select for participation, and devotes significant time to working closely with partners to ensure successful engagement strategies. The project will produce an advanced understanding of the needs of a broad range of users not previously studied in social science research, allowing NWS to improve its product delivery to at-risk flood-prone communities.
NWS/AFSO, WPC
Alex Lamers (WPC)
Mark Glaudemans (AFS)
Kate Abshire (AFS)
Jocelyn Burston (OWP)
David Vallee (OWP)
Nurture Nature Center, Inc.
NA22OAR4590170
Water Extremes
Pennsylvania
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Communicating Probabilities & Uncertainty, Decision-Making, Information Seeking & Processing, Social Vulnerability
expanding audiences removing barriers promoting action addressing the diverse needs of audiences for flood forecast information, the objectives of this project are to understand the range of flood information needs and concerns elicit how the various audiences prefer to receive information and obtain feedback on the extent to which existing flood forecast products help with decision making, in this project nnc will use its established relationships with river forecast centers rfcs the weather prediction center wpc and wfos as well as the national water center nwc to advance its model of focus group and survey testing to reach underserved audiences in various regions across the country including low income urban rural and tribal communities not previously studied the study will look at a broad suite of probabilistic and other flood forecast tools including hydrographs hydrologic ensemble forecast system outputs weather forecast office products and weather prediction center forecast products including the excessive rainfall outlook and mesoscale precipitation discussion for both riverine and flash flood risk and will work in partnership with national weather service nws offices and local community based organizations cbos to identify and engage with diverse audiences in regions across the country
Nurture Nature Center, Inc.
The objectives of this project are to understand the range of flood information needs and concerns, elicit how the various audiences prefer to receive information, and obtain feedback on the extent to which existing flood
0
Probabilistic Prediction of Thunderstorm Hazards using the NOAA Warn-on-Forecast System and Machine Learning
Flora
JTTI
check_circle
2022
Level 3
This research seeks to improve and evaluate the WoFS-ML-Severe system for short-term probabilistic severe weather guidance.
Montgomery Flora, Corey Potvin, Burkley Gallo, Jorge Guerra, Kent Knopfmier, Brett Roberts, Patrick Skinner, Brian Matilla, Amy McGOvern
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Model & Forecast Guidance, Numerical Weather Prediction (NWP), Post-processing, Probabilistic Weather Forecasting, Visual Risk Communication
JTTI
Unified Forecast System (UFS), Warn on Forecast System (WoFS)
Deep learning (DL) and traditional machine learning (ML) models are powerful postprocessing methods for maximizing the value of convection-allowing model (CAM) ensembles. We have recently developed ML models that operation on NSSL Warn-on-Forecast System (WoFS) forecasts and generate calibrated short-term (0-3 hr) probabilistic guidance for individual thunderstorm hazards: severe hail, severe wind, and tornadoes (hereafter known as the WoFS-ML-Serve suite). These models have performed skillfully against carefully calibrated baseline systems in retrospective testes (Flora et at. 2021) and real-time experiments (Clark et al 2021). These promising results motivate the three overarching objectives of the proposed work: (1) continued development and evaluation of the ML system in collaboration with SPC and OUN WFO forecasters and testbed experiments; (2) developing novel DL-based severe weatehr guidance between watch and warning timescales (e.g., 3-6 hr) using WoFS; and (3) implementing visualization to explain ML/DL predictions to forecasters and other users in real-time. This project directly addresses the following priorities: JTTI-1: "Utilize Artificial Intelligence/Machine Learning (AI/ML) for improving the forecasts"; JTTI-3(a): "Further develop, test, and evaluate promising probabilistic tools and technologies to improve the prediction and communication of hazardous weather events"; and JTTI-3(c): "Further develop tools and techniques to improve the translation of probabilistic NWP into meaningful NWS products and services that convey probabilistic hazard information for hazardous weather events."
The WoFS-ML-Severe products developed by this project will be implemented in the public WoFS Web Viewer. Some of the products will additionally be implemented as an add-on for the Advanced Weather Interactive Processing System (AWIPS) and delivered to our collaborators at the NEW Weather Forecast Office in Norman, Oklahoma, Training materials will be developed to assist forecasters in using the WoFS-ML-Severe products. At least two papers will be published in high-quality journals. By the end of the project, the WoFS-ML-Severe models will have been demonstrated in multiple HWT SFEs and implemented into AWIPS II, thereby attaining a RL of 6. TO facilitate eventual operationalization at NOAA, we will engage operational forecasters throughout the project, including evaluating the guidance for its role in forecaster duties such as mesoanalysis, impact-based decision services, and event-driven updates to national products.
SPC
Israel Jirak (SPC)
CIWRO/University of Oklahoma
NA22OAR4590171
Severe Weather
Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2024
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Model & Forecast Guidance, Numerical Weather Prediction (NWP), Post-processing, Probabilistic Weather Forecasting, Visual Risk Communication
probabilistic prediction of thunderstorm hazards using the noaa warn on forecast system and machine learning, this research seeks to improve and evaluate the wofs ml severe system for short term probabilistic severe weather guidance, deep learning dl and traditional machine learning ml models are powerful postprocessing methods for maximizing the value of convection allowing model cam ensembles we have recently developed ml models that operation on nssl warn on forecast system wofs forecasts and generate calibrated short term 0 3 hr probabilistic guidance for individual thunderstorm hazards severe hail severe wind and tornadoes hereafter known as the wofs ml serve suite these models have performed skillfully against carefully calibrated baseline systems in retrospective testes flora et at 2021 and real time experiments clark et al 2021 these promising results motivate the three overarching objectives of the proposed work 1 continued development and evaluation of the ml system in collaboration with spc and oun wfo forecasters and testbed experiments 2 developing novel dl based severe weatehr guidance between watch and warning timescales e g 3 6 hr using wofs and 3 implementing visualization to explain ml dl predictions to forecasters and other users in real time this project directly addresses the following priorities jtti 1 "utilize artificial intelligence machine learning ai ml for improving the forecasts" jtti 3 a "further develop test and evaluate promising probabilistic tools and technologies to improve the prediction and communication of hazardous weather events" and jtti 3 c "further develop tools and techniques to improve the translation of probabilistic nwp into meaningful nws products and services that convey probabilistic hazard information for hazardous weather events "
Storm Prediction Center (SPC)
CIWRO/University of Oklahoma
This research seeks to improve and evaluate the WoFS-ML-Severe system for short-term probabilistic severe weather guidance.
6
Implementation of a Unified Workflow for the Unified Forecast System (UFS) Short Range Weather (SRW) Application
Holt
JTTI
check_circle
2022
Level 3
Overall goals of the project include contributing to the development of the Unified Workflow (UW) toolbox and framework, and implementing the UW toolbox and framework in the Short Range Weather (SRW) Application of the Unified Forecast System (UFS).
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Earth Prediction Innovation Center (EPIC)
JTTI
Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS), UFS-Short-Range Weather Application (SRWA)
NOAA GSL is proposing a 3 year project to join a Unified Workflow Framework
development team, led by NOAA EMC, in coordination with National Centers for
Environmental Prediction (NCEP) Central Operations (NCO), and composed of
developers from the Earth Prediction Innovation Center (EPIC), the UFS R20 Unified
Workflows Project, and various centers, labs, and institutions that have amassed critical
knowledge about each of the UFS Apps and their features. The proposed work are
meant to fund NOAA GSL's in-kind contributions to the development of the generic tool
set and implementation of those tools and libraries in refactoring the Short Range
Weather Application, which is the basis for the real-time Rapid Refresh Forecast
System (RRFS) slated for its first operational implementation at NCEP in FY2023.
The most direct and immediate impact of the proposed work is that it will enable the
Short Range Weather App with fully configurable data assimilation workflow options, as
well as extend the current ensemble functionality to include cycled data assimilation
ensembles, multi-physics ensembles, and ensembles with perturbed initial and lateral
boundary conditions. Not only will these features be enabled, they should be easily
reconfigured for scientific discovery of promising new techniques, including the ease of
scientific and equivalence testing needed for the transition from GSI to JEDI. For this
impact, it is most likely that the UFS Community interested in the SRW App
development (including NOAA GSL and NOAA EMC) will be the primary beneficiaries
as the groups work toward designing the NWP system with the most useful forecasts
possible. These tools are being designed to comply with operational standards (in collaboration with NCO) and will make up the core of the RRFS workflow as it is
transitioned to operations.
EMC
Jacob Carley (EMC)
CIRES/University of Colorado
NA22OAR4590172
Relevant to All Hazards
Colorado
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Earth Prediction Innovation Center (EPIC)
implementation of a unified workflow for the unified forecast system ufs short range weather srw application, overall goals of the project include contributing to the development of the unified workflow uw toolbox and framework and implementing the uw toolbox and framework in the short range weather srw application of the unified forecast system ufs, noaa gsl is proposing a 3 year project to join a unified workflow framework development team led by noaa emc in coordination with national centers for environmental prediction ncep central operations nco and composed of developers from the earth prediction innovation center epic the ufs r20 unified workflows project and various centers labs and institutions that have amassed critical knowledge about each of the ufs apps and their features the proposed work are meant to fund noaa gsl's in kind contributions to the development of the generic tool set and implementation of those tools and libraries in refactoring the short range weather application which is the basis for the real time rapid refresh forecast system rrfs slated for its first operational implementation at ncep in fy2023
Environmental Modeling Center (EMC)
CIRES/University of Colorado
Overall goals of the project include contributing to the development of the Unified Workflow (UW) toolbox and framework, and implementing the UW toolbox and framework in the Short Range Weather (SRW) Application of the Unified
1
Advancement of background ensemble covariance at the air-sea interface toward the UFS HAFS fully coupled data assimilation
Wang
JTTI
search_activity
2022
Level 3
This project aims to improve the Hurricane Analysis and Forecast System (HAFS) by identifying the best ocean state variable and model physics parameters to use in creating realistic background ensemble conditions across the ocean-atmosphere boundary and applying the improved coupled ensemble covariance to data assimilation within the HAFS.
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, In-situ Observation Technologies and Sensors
JTTI
Hurricane Analysis and Forecast System (HAFS), Unified Forecast System (UFS)
Given the separate data assimilation (DA) performed for the ocean and atmospheric
components, large inconsistencies at the air-sea interface between the atmospheric and
oceanic states exist, which degrades hurricane intensity and track forecasts of the next
generation UFS Hurricane Analysis and Forecast System (HAFS). The primary goal of the
project is to advance the background ensemble covariance at the air-sea interface, one critical
aspect that needs to be addressed toward developing the eventual UFS HAFS fully coupled
DA system. This project will leverage the capabilities of the HAFS atmospheric DA
established under the hurricane supplement funding and the Marine JEDI DA being extended
for HAFS under other existing NOAA supports. JTTI funding is sought to achieve the
following primary objectives: 1) Determine the best ocean state variable and model physics
parameter perturbation strategy to establish physically consistent background ensemble
perturbations across the ocean and atmosphere boundary, including the use of targeted in-situ
observations to evaluate the coupled background ensemble and its covariances; 2) Apply the
improved coupled ensemble covariance to allow effective weakly coupled DA for HAFS; 3)
Further develop capabilities toward the fully coupled DA for HAFS, with the initial focus on
testing the use of the hurricane-sampling atmospheric observations to update the SST.
This project will gain new knowledge on the impact of coupled ocean-atmosphere
covariance and coupled update on hurricane forecast. The scientific findings of this project
will provide guidance for the design of, and accelerate the national efforts in developing and
implementing the next-generation operational convection allowing hurricane DA and
forecast system.
EMC
Shun Liu (EMC)
University of Oklahoma
University of Miami
NA22OAR4590173
NA22OAR4590174
Tropical Cyclones
Florida, Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, In-situ Observation Technologies and Sensors
advancement of background ensemble covariance at the air sea interface toward the ufs hafs fully coupled data assimilation, this project aims to improve the hurricane analysis and forecast system hafs by identifying the best ocean state variable and model physics parameters to use in creating realistic background ensemble conditions across the ocean atmosphere boundary and applying the improved coupled ensemble covariance to data assimilation within the hafs, given the separate data assimilation da performed for the ocean and atmospheric components large inconsistencies at the air sea interface between the atmospheric and oceanic states exist which degrades hurricane intensity and track forecasts of the next generation ufs hurricane analysis and forecast system hafs the primary goal of the project is to advance the background ensemble covariance at the air sea interface one critical aspect that needs to be addressed toward developing the eventual ufs hafs fully coupled da system this project will leverage the capabilities of the hafs atmospheric da established under the hurricane supplement funding and the marine jedi da being extended for hafs under other existing noaa supports jtti funding is sought to achieve the following primary objectives 1 determine the best ocean state variable and model physics parameter perturbation strategy to establish physically consistent background ensemble perturbations across the ocean and atmosphere boundary including the use of targeted in situ observations to evaluate the coupled background ensemble and its covariances 2 apply the improved coupled ensemble covariance to allow effective weakly coupled da for hafs 3 further develop capabilities toward the fully coupled da for hafs with the initial focus on testing the use of the hurricane sampling atmospheric observations to update the sst
Environmental Modeling Center (EMC)
University of Oklahoma, University of Miami
This project aims to improve the Hurricane Analysis and Forecast System (HAFS) by identifying the best ocean state variable and model physics parameters to use in creating realistic background ensemble conditions across the ocean-atmosphere boundary
6
Implementation of an Ensemble Sensitivity Tool to Better Assess Uncertainty in Mid-Latitude Extreme Weather Forecasts
Colle
JTTI
search_activity
2022
Level 3
The goal of this proposed project is to make the current Stony Brook University (SBU) Ensemble Sensitivity Analysis (ESA) tool operational while testing and including additional mid-latitude hazards, such as heavy precipitation, strong winds, extreme cold, heat, and wind.
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Decision Support
JTTI
Global Ensemble Forecast System (GEFS)
There are a growing number of ensemble systems available to forecasters; however, the benefits of using these ensembles in operations have not been fully realized. Forecasters working under strict time constraints often do not have time to thoroughly interrogate and investigate the wealth of information contained in an ensemble forecast. To address this, Stony Brook University (SBU) in collaboration with Weather Prediction Center (WPC) has a current JTTI project to implement a real-time ensemble clustering tool using a 100-member ensemble from the GEFS, CMC, and EC ensembles for days 3-7 and 8-10. The generation of cluster scenarios are enhancing forecaster understanding of the ensemble solutions and are in increasingly heavy use throughout the NWS. However, users have requested more information and tools to understand how the atmosphere needs to evolve in the proceeding days for a particular cluster scenario to come to fruition. In a recent CSTAR project, Stony Brook University (SBU) developed an Ensemble Sensitivity Analysis (ESA) tool that has been running operationally at EMC using either GEFS, GEFS+CMC, or EC ensembles. This tool uses the patterns in the ensemble spread of SLP to calculate upstream sensitive regions for high impact weather, which can help forecasters better understand the origin of the ensemble uncertainty. This will help with forecaster situational awareness and Impact-based Decision Support, as well as potentially help with NOAA reconnaissance targeting efforts during the winter. The overarching goal of this proposed project is to make the current SBU ESA tool operational while testing and including additional mid-latitude hazards, such as heavy precipitation, strong winds, extreme cold, heat, and wind. We will focus on the days 3-7 and 8-10 given the interest in extremes in the medium and long-range forecasts using the same set of ensembles as the clustering (GEFS, CMC, and EC). We will develop an interactive way for forecasters to choose a variable and region of interest. This expanded ESA tool will be validated objectively to determine if it is targeting the correct upstream areas, and it will also be evaluated within the Hydrometeorological Testbed, in particular the Winter Weather Experiment and Extended Forecast Experiments. Forecasters will learn how to utilize this tool in concert with our recently developed clustering tool as well as other ensemble tools. There will also be forecaster training to learn how to use the ensemble sensitivity tools in operations using online resources, modules, testbed discussions, and outreach during NWS operational workshops.
As with the ensemble clustering tool, the beneficiaries of the ESA tool will be WPC and NWS WFOs. THe ESA website and training materials will also be publicly available so the public and private organization may also access the data and directly reap its benefits. Overall, this project will have large societal benefits, since forecasters will have more situational awareness about the ensemble solutions and model trends, which will in turn likely help with the forecasts and communication of the scenarios to stakeholders. Similar to the tropical storm reconnaissance, there is interest in using an ESA tool for winter storm reconnaissance flights over Gulf of Mexico, eastern Pacific, or western Atlantic (Dr. Peter Black, personal communication 2021), and these targeted observations may help improve the model forecast guidance for major storm events.
WPC
Jim Nelson (WPC)
Stony Brook University
CIRES
NA22OAR4590175
NA22OAR4590176
Relevant to All Hazards
Maryland, New York
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Data Assimilation and Ensembles, Decision Support
implementation of an ensemble sensitivity tool to better assess uncertainty in mid latitude extreme weather forecasts, the goal of this proposed project is to make the current stony brook university sbu ensemble sensitivity analysis esa tool operational while testing and including additional mid latitude hazards such as heavy precipitation strong winds extreme cold heat and wind, there are a growing number of ensemble systems available to forecasters however the benefits of using these ensembles in operations have not been fully realized forecasters working under strict time constraints often do not have time to thoroughly interrogate and investigate the wealth of information contained in an ensemble forecast to address this stony brook university sbu in collaboration with weather prediction center wpc has a current jtti project to implement a real time ensemble clustering tool using a 100 member ensemble from the gefs cmc and ec ensembles for days 3 7 and 8 10 the generation of cluster scenarios are enhancing forecaster understanding of the ensemble solutions and are in increasingly heavy use throughout the nws however users have requested more information and tools to understand how the atmosphere needs to evolve in the proceeding days for a particular cluster scenario to come to fruition in a recent cstar project stony brook university sbu developed an ensemble sensitivity analysis esa tool that has been running operationally at emc using either gefs gefs+cmc or ec ensembles this tool uses the patterns in the ensemble spread of slp to calculate upstream sensitive regions for high impact weather which can help forecasters better understand the origin of the ensemble uncertainty this will help with forecaster situational awareness and impact based decision support as well as potentially help with noaa reconnaissance targeting efforts during the winter the overarching goal of this proposed project is to make the current sbu esa tool operational while testing and including additional mid latitude hazards such as heavy precipitation strong winds extreme cold heat and wind we will focus on the days 3 7 and 8 10 given the interest in extremes in the medium and long range forecasts using the same set of ensembles as the clustering gefs cmc and ec we will develop an interactive way for forecasters to choose a variable and region of interest this expanded esa tool will be validated objectively to determine if it is targeting the correct upstream areas and it will also be evaluated within the hydrometeorological testbed in particular the winter weather experiment and extended forecast experiments forecasters will learn how to utilize this tool in concert with our recently developed clustering tool as well as other ensemble tools there will also be forecaster training to learn how to use the ensemble sensitivity tools in operations using online resources modules testbed discussions and outreach during nws operational workshops
Weather Prediction Center (WPC)
Stony Brook University, CIRES
The goal of this proposed project is to make the current Stony Brook University (SBU) Ensemble Sensitivity Analysis (ESA) tool operational while testing and including additional mid-latitude hazards, such as heavy precipitation, strong winds, extreme
0
A Scale-Aware Three-Dimensional Sub-Grid Scale Turbulent Mixing Parameterization for the Hurricane Analysis and Forecast System
Zhu
JTTI
search_activity
2022
Level 3
This project proposes to implement a scale-adaptive parameterization of the 3D sub-grid scale (SGS) turbulent mixing that can be used by all of NOAA Unified Forecast System (UFS) applications.
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting, Parameterization, Weather Buoy Systems
JTTI
Common Community Physics Package (CCPP), Hurricane Analysis and Forecast System (HAFS), Unified Forecast System (UFS)
The proposed implementation, initially targeted for operational implementation in the
Hurricane Analysis and Forecast System (HAFS) model, allows for a unified treatment of the
horizontal and vertical turbulent mixing induced by small isotropic and large anisotropic eddies
adaptively applicable from the large-eddy simulations (LESs) to mesoscale simulations. The
objectives of this proposal are (a) to develop a scale-adaptive 3D, moist turbulent kinetic energy
(TKE) turbulence scheme that includes the cloud-induced buoyancy in the turbulent transport
parameterization so that the 3D scheme is applicable in all weather conditions, including weather
extremes such as tropical cyclones (TCs); (b) to implement the 3D scheme in a UFS operational
model, the multiple-scale HAFS, as an extension to the existing 1D TKE scheme in the Common
Community Physics Package (CCPP); (c) to improve the 3D scheme working in concert with other
schemes in CCPP, and (d) to evaluate the physics suite with the 3D scheme as a whole in
operational forecasting setting, in particular for prediction of tracks, intensity, and structure of
TCs. This project contributes directly to the overarching goal of JTTI for improving NOAA's
National Weather Service (NWS) operational capability. Specifically, this proposed project
addresses the priority JTTI-1(d), focusing on improving and enhancing CCPP by developing a
scale-aware 3D turbulence parameterization to better represent the SGS mixing processes and
multiscale interactions.
The specific goals of this proposal are (a) to refine and upgrade the existing 3D TKE
turbulence scheme with a scale-aware capability and implement it in the newly developed UFS
multiple-scale model HAFS as an extension to the existing 1D EDMF-TKE scheme in the
Common Community Physics Package (CCPP) that is used in the operational GFS; (b) to tune and
improve the 3D scheme working in concert with other schemes in CCPP; and (c) to evaluate and
refine the performance of the 3D scheme in the operational forecasting settings of various cross-
scale UFS applications. By doing so, this project contributes directly to the overarching goal of
JTTI for improving NOAA's NWS operational capability. Specifically, this proposal addresses the
priority JTTI-1(d) focusing on improving and enhancing CCPP by developing a scale-aware 3D
turbulence parameterization to better represent the SGS processes and multiscale process
interactions.
EMC
Jongil Han (EMC)
Florida International University
University of Miami/CIMAS
NA22OAR4590177
NA22OAR4590178
Tropical Cyclones
Florida
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Atmospheric Physics, Dynamics and Nesting, Parameterization, Weather Buoy Systems
a scale aware three dimensional sub grid scale turbulent mixing parameterization for the hurricane analysis and forecast system, this project proposes to implement a scale adaptive parameterization of the 3d sub grid scale sgs turbulent mixing that can be used by all of noaa unified forecast system ufs applications, the proposed implementation initially targeted for operational implementation in the hurricane analysis and forecast system hafs model allows for a unified treatment of the horizontal and vertical turbulent mixing induced by small isotropic and large anisotropic eddies adaptively applicable from the large eddy simulations less to mesoscale simulations the objectives of this proposal are a to develop a scale adaptive 3d moist turbulent kinetic energy tke turbulence scheme that includes the cloud induced buoyancy in the turbulent transport parameterization so that the 3d scheme is applicable in all weather conditions including weather extremes such as tropical cyclones tcs b to implement the 3d scheme in a ufs operational model the multiple scale hafs as an extension to the existing 1d tke scheme in the common community physics package ccpp c to improve the 3d scheme working in concert with other schemes in ccpp and d to evaluate the physics suite with the 3d scheme as a whole in operational forecasting setting in particular for prediction of tracks intensity and structure of tcs this project contributes directly to the overarching goal of jtti for improving noaa's national weather service nws operational capability specifically this proposed project addresses the priority jtti 1 d focusing on improving and enhancing ccpp by developing a scale aware 3d turbulence parameterization to better represent the sgs mixing processes and multiscale interactions
Environmental Modeling Center (EMC)
Florida International University, University of Miami/CIMAS
This project proposes to implement a scale-adaptive parameterization of the 3D sub-grid scale (SGS) turbulent mixing that can be used by all of NOAA Unified Forecast System (UFS) applications.
16
A Wind-Wave-Current Data Assimilation Scheme for the 3D-Real Time Mesoscale Analysis
Pea
JTTI
search_activity
2022
Level 3
The goal is to advance the science and technology of data assimilation of surface wind, sea wave, and surface ocean current in the
3-Dimensional Real Time Mesoscale Analysis (3D-RTMA) system.
Malaquas Pea, Stylianos Flampouris, Enrique Curchister, Leonel Romero, Cesar Rocha, Manuel Pondeca
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Model & Forecast Guidance, Radar, Satellite & Remote Sensing Technologies, Verification & Validation, Visual Risk Communication
JTTI
Modular Ocean Model (MOM), Rapid Refresh Forecast System (RRFS), Real-Time Mesoscale Analysis (RTMA), Unified Forecast System (UFS)
This project aims to advance the science and technology of data assimilation of surface wind, sea
wave, and surface ocean current in the 3-Dimensional Real Time Mesoscale Analysis (3D-
RTMA). Analysis of marine variables requires an effective exploitation of observational
information and the integration of models that capture key processes and air-sea interactions
occurring over a wide range of spatial and time scales. The proposed system aims to improve the
blending of global and local-scale weather and oceanographic variability in the RTMA by
expanding the JEDI protocols to include marine observations, use advanced data assimilation
schemes, and by using sound scientific principles of mesoscale modeling. The products of the
operational (2D) RTMA are used for guidance, monitoring, and forecast verification in NWS
forecast offices. This project will extend this capability to support offices in charge of providing
marine and ocean analysis products and information by establishing skillful estimates of the state
of waters at the margins of the continental U.S. Its outcomes will facilitate mesoscale data to a
community of practice for observing data impact, data integration, and coastal model evaluation.
This project pursues four key analysis enhancements: physical consistency in the three-way
interaction wind-waves-currents, reduced bias of the background fields, higher fidelity of fields,
and improved analysis uncertainty. To achieve the proposed enhancements the following
objectives are pursued: (1) increase the robustness of the data quality control system for marine
and ocean variables-especially the High-Frequency radar and satellite altimetry data, (2)
implement the IODA libraries for preparation of forward operators for each of the new
observations to be included, (3) implement an appropriate configuration of the regional MOM6
ocean model to provide surface current's first guess fields, (4) implement a field-alignment
processing scheme for positional and amplitude error reduction in the background fields, (5)
develop and evaluate configurations of the background error covariance across the three fields,
and (6) design, develop, and test a hybrid data assimilation scheme of wind-wave assimilation
constrained to current conditions. The proposed advancements of the data assimilation scheme will
leverage on the ongoing NOAA developments of the 3D-RTMA, the UFS, and the JEDI project.
In turn, this project will lead developments towards future coupled wind-wave-current data
assimilation schemes.
The final product of this proposal is a data assimilation scheme for the marine component of the
3D-RTMA with the following enhancements: (1) increased robustness of the data quality control
system for marine and ocean variables-especially the High Frequency radar and satellite altimetry
data, (2) implementation of the IODA libraries for preparation of forward operators for each of the
new observations to be included, (3) implementation of an appropriate configuration of the
regional MOM6 ocean model to provide surface current's first guess fields, (4) implementation of
a field-alignment processing scheme for positional and amplitude error reduction in the
background fields, (5) background error covariance across the three field interphases, and (6)
comparative evaluation of at least two data assimilation schemes for the marine component of the
3D-RTMA.
EMC
Shun Liu (EMC)
University of Connecticut
NA22OAR4590179
Relevant to All Hazards
Connecticut
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Model & Forecast Guidance, Radar, Satellite & Remote Sensing Technologies, Verification & Validation, Visual Risk Communication
a wind wave current data assimilation scheme for the 3d real time mesoscale analysis, the goal is to advance the science and technology of data assimilation of surface wind sea wave and surface ocean current in the 3 dimensional real time mesoscale analysis 3d rtma system, this project aims to advance the science and technology of data assimilation of surface wind sea wave and surface ocean current in the 3 dimensional real time mesoscale analysis 3d rtma analysis of marine variables requires an effective exploitation of observational information and the integration of models that capture key processes and air sea interactions occurring over a wide range of spatial and time scales the proposed system aims to improve the blending of global and local scale weather and oceanographic variability in the rtma by expanding the jedi protocols to include marine observations use advanced data assimilation schemes and by using sound scientific principles of mesoscale modeling the products of the operational 2d rtma are used for guidance monitoring and forecast verification in nws forecast offices this project will extend this capability to support offices in charge of providing marine and ocean analysis products and information by establishing skillful estimates of the state of waters at the margins of the continental u s its outcomes will facilitate mesoscale data to a community of practice for observing data impact data integration and coastal model evaluation this project pursues four key analysis enhancements physical consistency in the three way interaction wind waves currents reduced bias of the background fields higher fidelity of fields and improved analysis uncertainty to achieve the proposed enhancements the following objectives are pursued 1 increase the robustness of the data quality control system for marine and ocean variables-especially the high frequency radar and satellite altimetry data 2 implement the ioda libraries for preparation of forward operators for each of the new observations to be included 3 implement an appropriate configuration of the regional mom6 ocean model to provide surface current's first guess fields 4 implement a field alignment processing scheme for positional and amplitude error reduction in the background fields 5 develop and evaluate configurations of the background error covariance across the three fields and 6 design develop and test a hybrid data assimilation scheme of wind wave assimilation constrained to current conditions the proposed advancements of the data assimilation scheme will leverage on the ongoing noaa developments of the 3d rtma the ufs and the jedi project in turn this project will lead developments towards future coupled wind wave current data assimilation schemes
Environmental Modeling Center (EMC)
University of Connecticut
The goal is to advance the science and technology of data assimilation of surface wind, sea wave, and surface ocean current in the
3-Dimensional Real Time Mesoscale Analysis (3D-RTMA) system.
2
Advanced Coupling Evaluation Metrics in METplus for UFS Land Surface Models
Miller
JTTI
search_activity
2022
Level 3
This project aims to apply a hierarchical testing approach in the evaluation of UFS land models with the corresponding development and application of land-atmosphere coupling metrics, as well as improving process understanding of UFS forecast biases.
Scott Miller, Cheng-Hsuan Lu, Andrew Newman, Mike Ek, Tara Jensen, Kathryn Newman
EMC Verification System (EVS), Model Evaluation Tools Plus (METPlus), Noah Multiparameterization Land Surface Model (Noah-MP LSM), Unified Forecast System (UFS)
In support of NOAA's efforts to improve the physics suite in the Unified Forecast System
(UFS) atmospheric model, we propose to build a prototype framework for evaluating UFS land
surface models (LSMs), land-atmosphere coupling, and planetary boundary layer (PBL)
schemes, and to assess the UFS performance using our framework across the Contiguous United
States (CONUS) using data from a variety of observing networks. We will perform a
retrospective analysis of UFS forecasts and prototype a near-real time UFS land-atmosphere
coupling evaluation system. We will use mesonets and flux sites from a variety of sources such
as MADIS aggregated surface observations, Ameriflux, ARM Southern Great Plains (SGP), and
the New York State Mesonet (NYSM). A standard evaluation metric suite will be developed for
UFS model evaluation, intercomparison, and verification by developing use-cases for the
enhanced Model Evaluation Tools framework (METplus) and will include commonly-used
quantities for time series analysis and forecast validation. Next, an advanced evaluation metric
suite will focus on information theory metrics including mutual information, transfer entropy,
and conditional transfer entropy to provide new insights into coupled model behavior.
This project aims to demonstrate how advanced evaluation metrics and advanced
ground-based observational networks can be combined to facilitate and guide improvements to
LSM representations in coupled models. EMC will be the primary recipient of these results and
will use the capability developed in this project to add the LSM metrics identified during the
Metrics Workshop to the EMC Verification System (EVS). The broader community will benefit
from the availability of LSM metrics in METplus by using the same use-cases that will be
available on the METplus GitHub repository at https://github.com/dtcenter/METplus. Results
will also be disseminated to the research community via conference papers and presentations and
refereed journal publications. We envision the following potential publications: 1) description of
standard metric suite, incorporation into METplus use-cases, and demonstration on standard
observations; 2) description of advanced metric suite, incorporation into METplus use-cases, and
demonstration using NYSM standard data; and 3) demonstration of standard and advanced
metric suites on enhanced NYSM data (flux, profiler, and snow site data).
EMC
Alicia Bentley (EMC)
SUNY Albany
National Center for Atmospheric Research
advanced coupling evaluation metrics in metplus for ufs land surface models, this project aims to apply a hierarchical testing approach in the evaluation of ufs land models with the corresponding development and application of land atmosphere coupling metrics as well as improving process understanding of ufs forecast biases, in support of noaa's efforts to improve the physics suite in the unified forecast system ufs atmospheric model we propose to build a prototype framework for evaluating ufs land surface models lsms land atmosphere coupling and planetary boundary layer pbl schemes and to assess the ufs performance using our framework across the contiguous united states conus using data from a variety of observing networks we will perform a retrospective analysis of ufs forecasts and prototype a near real time ufs land atmosphere coupling evaluation system we will use mesonets and flux sites from a variety of sources such as madis aggregated surface observations ameriflux arm southern great plains sgp and the new york state mesonet nysm a standard evaluation metric suite will be developed for ufs model evaluation intercomparison and verification by developing use cases for the enhanced model evaluation tools framework metplus and will include commonly used quantities for time series analysis and forecast validation next an advanced evaluation metric suite will focus on information theory metrics including mutual information transfer entropy and conditional transfer entropy to provide new insights into coupled model behavior
Environmental Modeling Center (EMC)
SUNY Albany, National Center for Atmospheric Research
This project aims to apply a hierarchical testing approach in the evaluation of UFS land models with the corresponding development and application of land-atmosphere coupling metrics, as well as improving process understanding of UFS forecast
0
Multi-institution Collaborative Proposal: Integration of a Fully Functional Atmospheric UFS-HASFS into JEDI with Weakly Coupled Ocean Data Assimilation Capability
Aksoy
JTTI
search_activity
2022
Level 3
This muti-institution project aims to improve the Hurricane Analysis and Forecast System (HAFS) by developing and transitioning new data assimilation capabilities and unifying these advancements within the JEDI framework.
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance
JTTI
Hurricane Analysis and Forecast System (HAFS), Unified Forecast System (UFS)
The goal of this project is to transfer new technology into a coupled modeling and data
assimilation (DA) framework called the NOAA Hurricane Analysis and Forecasting System (HAFS). The goal of HAFS is to provide forecast guidance for wind, storm surge, rainfall, and other weather hazards associated with landfalling hurricanes. This project will be a coordinated effort directed by the lead-PI, Aksoy (University of Miami, UM), and institutional PI's Wang (Oklahoma University, OU), and Poterjoy (University of Maryland, UMD). Our project will coordinate with NOAA's Environmental Modeling Center (EMC) to directly address the JTTI-1 goal to "further develop, test and enhance data assimilation (DA) techniques" for HAFS. The PIs are already collaborating in a joint project in response to the NOAA RFA "FY19 Disaster Supplement'' through June 2022 to develop various components of the atmospheric HAFS-DA system. This work has yielded a self-cycled, dual-resolution-capable, hybrid DA system and has been tested on a relatively small number of cases. Results so far indicate that further optimization is needed to improve the performance of this system, which is one of the main goals of our proposed work. New developments, as jointly agreed upon with the EMC, are also proposed. These developments include added capability to assimilate GOES-R Advanced Baseline Imager (ABI) all-sky radiance observations and improve the HAFS ensemble to obtain suitable ocean-aware background error covariances for atmospheric data assimilation. Finally, all developments will be integrated to the framework of the Joint Effort for Data assimilation Integration (JEDI).
A further goal of this proposal is to address the goals JTTI-1(a) to "develop, test and enhance coupled data assimilation techniques" and JTTI-1(c) to "develop and refine ensemble framework for the coupled UFS prototypes." Although our main goal in the first two years of the project is to focus on providing a fully functional atmospheric hybrid DA system to the EMC, in the third year we will focus on re-optimizing atmospheric DA in the context of a coupled atmosphere-ocean system that is expected to be available at that time. This will yield a fully optimized, ocean-aware atmospheric DA capability that will be the natural stepping stone toward NOAA's ultimate goal of a fully integrated atmosphere-ocean modeling and DA system for HAFS.
Integrated outcomes and qualitative assessment: In addition to the individual developments by the three groups that will become operational, a revisit of all atmospheric DA parameters for a coupled atmosphere-ocean DA system will allow a re-tuned and optimized atmospheric DA capability to emerge within the JEDI system ready for operational application by the end of the project period. This activity will naturally require the availability of a functional coupled atmosphere-ocean DA system that is expected to be developed independently from the proposed work here. However, the lead PI Aksoy (UM) is directly involved in a separate proposal being submitted to the same opportunity by UM that is titled "Advancing UFS-HAFS toward the Next-Generation Modeling System with Coupled Data Assimilation Using JEDI," which focuses on advancing the ocean data assimilation for HAFS through further developing the Marine JEDI for regional hurricane application. Meanwhile, Co-PI (Wang) also leads a third proposal focusing on
developing the coupled ocean - atmosphere background ensemble covariances for both weakly and strongly coupled DA for hurricane prediction. The three proposals are complementary to each other and naturally synergistic.
EMC
May need new POC
Currently Kleist
University of Miami
University of Maryland
University of Oklahoma
NA22OAR4590182
NA22OAR4590183
NA22OAR4590184
Tropical Cyclones
Florida, Maryland, Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance
multi institution collaborative proposal integration of a fully functional atmospheric ufs hasfs into jedi with weakly coupled ocean data assimilation capability, this muti institution project aims to improve the hurricane analysis and forecast system hafs by developing and transitioning new data assimilation capabilities and unifying these advancements within the jedi framework, the goal of this project is to transfer new technology into a coupled modeling and data assimilation da framework called the noaa hurricane analysis and forecasting system hafs the goal of hafs is to provide forecast guidance for wind storm surge rainfall and other weather hazards associated with landfalling hurricanes this project will be a coordinated effort directed by the lead pi aksoy university of miami um and institutional pi's wang oklahoma university ou and poterjoy university of maryland umd our project will coordinate with noaa's environmental modeling center emc to directly address the jtti 1 goal to "further develop test and enhance data assimilation da techniques" for hafs the pis are already collaborating in a joint project in response to the noaa rfa "fy19 disaster supplement'' through june 2022 to develop various components of the atmospheric hafs da system this work has yielded a self cycled dual resolution capable hybrid da system and has been tested on a relatively small number of cases results so far indicate that further optimization is needed to improve the performance of this system which is one of the main goals of our proposed work new developments as jointly agreed upon with the emc are also proposed these developments include added capability to assimilate goes r advanced baseline imager abi all sky radiance observations and improve the hafs ensemble to obtain suitable ocean aware background error covariances for atmospheric data assimilation finally all developments will be integrated to the framework of the joint effort for data assimilation integration jedi a further goal of this proposal is to address the goals jtti 1 a to "develop test and enhance coupled data assimilation techniques" and jtti 1 c to "develop and refine ensemble framework for the coupled ufs prototypes " although our main goal in the first two years of the project is to focus on providing a fully functional atmospheric hybrid da system to the emc in the third year we will focus on re optimizing atmospheric da in the context of a coupled atmosphere ocean system that is expected to be available at that time this will yield a fully optimized ocean aware atmospheric da capability that will be the natural stepping stone toward noaa's ultimate goal of a fully integrated atmosphere ocean modeling and da system for hafs
Environmental Modeling Center (EMC)
University of Miami, University of Maryland, University of Oklahoma
This muti-institution project aims to improve the Hurricane Analysis and Forecast System (HAFS) by developing and transitioning new data assimilation capabilities and unifying these advancements within the JEDI framework.
5
Effectively Communicating Uncertainty in Tropical Cyclone Intensity Forecasts
Halperin
JTTI
search_activity
2022
Level 3
The goals of the proposed project are to develop modified, experimental formats of NHC's prototype intensity forecast products and to evaluate each message condition using the Internalization, Distribution, Explanation, Action (IDEA) model framework.
Daniel Halperin, Deanna Sellnow, Robert Eicher, Timothy Sellnow
Accurately predicting tropical cyclone (TC) intensity remains a primary challenge in operational
forecasting. The National Hurricane Center's (NHC) hurricane specialists can subjectively convey
uncertainty or confidence in an intensity forecast through the forecast discussion. This product is
available to the public, but it routinely includes jargon that renders it difficult for non-
meteorologists to interpret. Currently, no operational product objectively communicates
uncertainty in TC intensity forecasts (Bhatia & Nolan 2015). However, NHC recently developed
prototype intensity forecast products that include a measure of uncertainty. The PIs are currently
collaborating with NHC via the UCAR/COMET NWS Partners program to assess the public's
understanding of these prototype forecast products. The goals of the proposed project are:
1. to develop modified, experimental formats (i.e., message conditions) of NHC's prototype
intensity forecast products based on the results and recommendations of the ongoing
UCAR/COMET project and stakeholder input (e.g., NHC and NWS forecasters,
emergency managers, broadcast meteorologists);
2. to evaluate each message condition using the Internalization, Distribution, Explanation,
Action (IDEA) model framework (e.g., Sellnow et al., 2017; Sellnow & Sellnow, 2019) to
determine the forecast product format and messaging channel (e.g., static image, television
broadcast) that exhibits the greatest message clarity regarding the risk of wind impacts
from TCs and exhibits the greatest message effectiveness based on the public's perceived
risks of wind impacts from TCs to their life and property.
The primary outcome of this project will be the improved communication of TC intensity forecast
uncertainty to general publics via a new TC intensity forecast product. Prototype products have
been developed at NHC. The PIs, in collaboration with NOAA stakeholders and partners, will
create multiple redesigns of the prototype products and test their clarity and effectiveness among
general publics. The experimental forecast products will be considered successful if survey results from public end-users indicate that the experimental forecast products receive statistically
significantly higher scores for clarity compared to the NHC's current operational deterministic
intensity forecast. The NHC will be the primary recipient of all project outcomes/products.
NHC
Wallace Hogsett
Embry-Riddle Aeronautical University
University of Central Florida
effectively communicating uncertainty in tropical cyclone intensity forecasts, the goals of the proposed project are to develop modified experimental formats of nhc's prototype intensity forecast products and to evaluate each message condition using the internalization distribution explanation action idea model framework, accurately predicting tropical cyclone tc intensity remains a primary challenge in operational forecasting the national hurricane center's nhc hurricane specialists can subjectively convey uncertainty or confidence in an intensity forecast through the forecast discussion this product is available to the public but it routinely includes jargon that renders it difficult for non meteorologists to interpret currently no operational product objectively communicates uncertainty in tc intensity forecasts bhatia nolan 2015 however nhc recently developed prototype intensity forecast products that include a measure of uncertainty the pis are currently collaborating with nhc via the ucar comet nws partners program to assess the public's understanding of these prototype forecast products the goals of the proposed project are 1 to develop modified experimental formats i e message conditions of nhc's prototype intensity forecast products based on the results and recommendations of the ongoing ucar comet project and stakeholder input e g nhc and nws forecasters emergency managers broadcast meteorologists 2 to evaluate each message condition using the internalization distribution explanation action idea model framework e g sellnow et al 2017 sellnow sellnow 2019 to determine the forecast product format and messaging channel e g static image television broadcast that exhibits the greatest message clarity regarding the risk of wind impacts from tcs and exhibits the greatest message effectiveness based on the public's perceived risks of wind impacts from tcs to their life and property
National Hurricane Center (NHC)
Embry-Riddle Aeronautical University, University of Central Florida
The goals of the proposed project are to develop modified, experimental formats of NHC's prototype intensity forecast products and to evaluate each message condition using the Internalization, Distribution, Explanation, Action (IDEA) model framework.
2
A Weather-Ready Nation Para Todos: Evaluating Current Practices in Communicating Hazardous Weather Risks to Spanish Speakers
Ripberger
JTTI
search_activity
2022
Level 3
This project aims to use a mixed-methods approach of surveys and interviews to investigate how Spanish-speaking US residents understand weather risks and interpret current NWS products involving weather risks.
Joseph Ripberger, Justin Reedy, Joseph Trujillo-Falcon, Kimberly Klockow-McClain
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Language & Cultural Contexts, Risk Communication, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
JTTI
We propose a mixed-methods project examining how Spanish-speaking communities understand weather hazards and interpret current warning products from the NWS that answers three primary research questions: 1) How do Hispanic and Latinx heritage, culture, nation of origin, length of stay and/or family history in the U.S., region of residence in the U.S., and other social variables affect the way these groups receive information about weather hazards, including current NWS products? 2) How do these factors in turn affect their perceptions of weather risks, interpretations of weather warning products, and planned protective actions from weather hazards? 3) What differences exist between different Spanish-speaking communities (e.g., nation of origin, region of residence in the U.S.) in perceptions of the risks of various weather hazards and their plans for taking protective action? We also propose building the results of our analysis into the Severe Weather and Society Dashboard (WxDash), a data browsing tool being incorporated into NWS operations, and we propose studying forecasters' use of this data in an experimental setting in NOA's Hazardous Weather Testbed.
This project will provide NWS forecasters with rich data about their Spanish-speaking Hispanic and Latinx populations. Collaborating with the Severe Weather and Society Dashboard project (Ripberger et al. 2015; 2020), this study will provide NWS WFOs with data that pertains to the specific regions they serve. Through annual, nationwide surveys and supplemental qualitative interviews of Spanish speakers in the U.S., we will address disparities that hinder the weather enterprise from developing effective, inclusive, and culturally responsive disaster preparedness plans for U.S. Spanish-speaking communities.
NWS/OSTI
NWS SBES
Krizia Negron
University of Oklahoma
NA22OAR4590187
Relevant to All Hazards
Oklahoma
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Language & Cultural Contexts, Risk Communication, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)"
a weather ready nation para todos evaluating current practices in communicating hazardous weather risks to spanish speakers, this project aims to use a mixed methods approach of surveys and interviews to investigate how spanish speaking us residents understand weather risks and interpret current nws products involving weather risks, we propose a mixed methods project examining how spanish speaking communities understand weather hazards and interpret current warning products from the nws that answers three primary research questions 1 how do hispanic and latinx heritage culture nation of origin length of stay and or family history in the u s region of residence in the u s and other social variables affect the way these groups receive information about weather hazards including current nws products? 2 how do these factors in turn affect their perceptions of weather risks interpretations of weather warning products and planned protective actions from weather hazards? 3 what differences exist between different spanish speaking communities e g nation of origin region of residence in the u s in perceptions of the risks of various weather hazards and their plans for taking protective action? we also propose building the results of our analysis into the severe weather and society dashboard wxdash a data browsing tool being incorporated into nws operations and we propose studying forecasters' use of this data in an experimental setting in noa's hazardous weather testbed
University of Oklahoma
This project aims to use a mixed-methods approach of surveys and interviews to investigate how Spanish-speaking US residents understand weather risks and interpret current NWS products involving weather risks.
1
Advancing the Lake-Coupling Techniques for the Unified Forecast System (UFS)
Jablonowski
JTTI
search_activity
2022
Level 3
The overarching goal of this project is to improve the representation of lakes in NOAA's Unified Forecast System (UFS) and thereby increase the accuracy of weather predictions from the UFS.
Christiane Jablonowski, Ayumi Fujisaki-Manome, Eric Anderson, Bryan Mroczka, Philip Chu
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, High-Performance Computing (HPC)
JTTI
Community Ice CodE (CICE), Earth System Modeling Framework (ESMF), Global Forecast System (GFS), National Unified Operational Prediction Capability (NUOPC), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS), UFS-Medium Range Weather Application (MRWA), UFS-Short-Range Weather Application (SRWA)
Specifically, this project will (a) utilize the UFS ESMF NUOPC-NEMS-CMEPS-CDEPS
software infrastructure to loosely couple the Great Lakes temperature and ice information from
the operational FVCOM-CICE model to the UFS-MWR/GFS and UFS-SWR/RRFS applications
on an hourly basis, (b) contribute to advanced representations of small lakes via 1D lake
components in UFS' land model, (c) work with NWS/WFO weather forecasters on objective
evaluations of the coupling capabilities via the HMT Winter Weather Experiment (WWE), and
(d) advance the FVCOM-CICE-WAVEWATCH III coupling capabilities to provide wave
information for coastal applications and the UFS. This project will thereby address critical gaps
in the UFS and provide NWS/WFO operational forecasters with improved forecasting tools for,
e.g., lake-effect snowfall, precipitation, and ice forecasts to protect the safety of the citizens in
the Great Lakes region.
This project will lead to an enhanced UFS-based forecasting system with extended atmosphere-
lake-ice coupling capabilities. These are expected to provide improved short-range forecasts of
lake-effect snow and rain events, more accurate ice forecast guidance, as well as improved
forecast performances over two-week (MRW) time scales due to more realistic lake-air
interactions. In addition, the enhanced lake-wave coupling capabilities will build the basis for
improved coastal applications in the Great Lakes domain and will make the coastal applications
UFS-compliant. This accomplishes the mission of the NWS to protect life and property, and to
enhance the national economy. In particular, improved forecasts of LES will protect the safety of
the citizens in the Great Lakes region and enable NOAA to achieve the overarching goals of a
weather-ready nation and healthy oceans.
EMC
Fanglin Yang
University of Michigan/CIGLR/Colorado School of Mines
NA22OAR4590188
Winter Weather
Colorado, Michigan
Joint Technology Transfer Initiative (JTTI)
September 2022 - August 2025
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, High-Performance Computing (HPC)
advancing the lake coupling techniques for the unified forecast system ufs, the overarching goal of this project is to improve the representation of lakes in noaa's unified forecast system ufs and thereby increase the accuracy of weather predictions from the ufs, specifically this project will a utilize the ufs esmf nuopc nems cmeps cdeps software infrastructure to loosely couple the great lakes temperature and ice information from the operational fvcom cice model to the ufs mwr gfs and ufs swr rrfs applications on an hourly basis b contribute to advanced representations of small lakes via 1d lake components in ufs' land model c work with nws wfo weather forecasters on objective evaluations of the coupling capabilities via the hmt winter weather experiment wwe and d advance the fvcom cice wavewatch iii coupling capabilities to provide wave information for coastal applications and the ufs this project will thereby address critical gaps in the ufs and provide nws wfo operational forecasters with improved forecasting tools for e g lake effect snowfall precipitation and ice forecasts to protect the safety of the citizens in the great lakes region
Environmental Modeling Center (EMC)
University of Michigan/CIGLR/Colorado School of Mines
The overarching goal of this project is to improve the representation of lakes in NOAA's Unified Forecast System (UFS) and thereby increase the accuracy of weather predictions from the UFS.
2
Non-Gaussian Ensemble Data Assimilation within a Rapidly Updating Global Prediction System
Poterjoy
JTTI
search_activity
2023
Level 3
This project aims to advance global ensemble prediction capabilities for the NOAA Unified Forecast System (UFS) through recently developed Bayesian data assimilation methodology based on particle filters. The proposed research will expand on previous research for performing hourly updates within the Global Forecast System (GFS) in place of current operational four-dimensional data assimilation that operates every 6 hours.
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, High-Performance Computing (HPC)
JTTI
Global Forecast System (GFS), Unified Forecast System (UFS)
This project aims to advance global ensemble prediction capabilities for the NOAA Unified Forecast System (UFS) through recently developed Bayesian data assimilation methodology based on particle filters. This study will deliver (1) the first-ever evaluation of fully non-Gaussian data assimilation for NOAA's global modeling system, (2) a new data assimilation method for UFS applications in JEDI, and (3) a global evaluation of precipitation bias within a novel modeling framework-that is expected to expose bias more readily than current operational systems.
The proposed data assimilation system developments are better suited to handle nonlinear measurement
operators and non-Gaussian observation uncertainty, which are major obstacles for assimilating observations that are impacted by cloud processes, such as all-sky radiance and precipitation measurements. This project will also establish a foundation for expanding research-to-operations efforts for global weather prediction in ways that have not yet been explored by the data assimilation community. Lastly, the proposed data assimilation methods will be used to examine model process errors that impact forecasts for precipitation, which presents a prediction challenge that stands to gain from this research.
EMC
University of Maryland
CIRES/University of Colorado Boulder
NA23OAR4590379
Relevant to All Hazards
Colorado, Maryland
Joint Technology Transfer Initiative (JTTI)
September 2023 - August 2025
Data Assimilation and Ensembles, High-Performance Computing (HPC)
non gaussian ensemble data assimilation within a rapidly updating global prediction system, this project aims to advance global ensemble prediction capabilities for the noaa unified forecast system ufs through recently developed bayesian data assimilation methodology based on particle filters the proposed research will expand on previous research for performing hourly updates within the global forecast system gfs in place of current operational four dimensional data assimilation that operates every 6 hours, this project aims to advance global ensemble prediction capabilities for the noaa unified forecast system ufs through recently developed bayesian data assimilation methodology based on particle filters this study will deliver 1 the first ever evaluation of fully non gaussian data assimilation for noaa's global modeling system 2 a new data assimilation method for ufs applications in jedi and 3 a global evaluation of precipitation bias within a novel modeling framework-that is expected to expose bias more readily than current operational systems
Environmental Modeling Center (EMC)
University of Maryland, CIRES/University of Colorado Boulder
This project aims to advance global ensemble prediction capabilities for the NOAA Unified Forecast System (UFS) through recently developed Bayesian data assimilation methodology based on particle filters. The proposed research will expand on previous research
0
Improving warm-rain moist physics in the Unified Forecast System
Mass
JTTI
search_activity
2023
Level 3
The proposed project aims to implement and evaluate the RCON parameterization in the UFS model and will include a global evaluation using the GFS and regional evaluations using the HRRR/RRFS modeling systems.
Global Forecast System (GFS), High-Resolution Rapid Refresh (HRRR), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
Modern, state-of-the-science modeling systems require realistic simulations of clouds and
precipitation. In the U.S., particularly over coastal regions along the West Coast, Alaska, and
Hawaii, a significant fraction of precipitation results from warm rain, which is formed by the
collision and coalescence of liquid water droplets. Despite its importance, warm rain is often
poorly represented by modern numerical weather prediction models. Specifically, warm rain is
characterized by large numbers of small raindrops; in contrast, virtually all current microphysical
parameterizations incorrectly model such situations as more like cold rain, with greater numbers
of large drops. Results of model evaluations undertaken by the PI and his research group have
shown that the Thompson-Eidhammer microphysical parameterization scheme, which is the
parameterization that will be used by the UFS, suffers from these warm rain deficiencies.
To address these biases, we have modified the Thomson-Eidhammer parameterization to
simulate warm rain more accurately. The parameterization modifications, called RCON, have been
extensively tested over coastal Washington State and show significant promise toward improving
precipitation forecasts over Alaska and Hawaii. RCON produces broader cloud droplet size
distributions during warm rain environments, resulting in greater production of warm rain.
During the proposed project, we will work with scientists and collaborators at
NOAA/EMC, as well as scientists/forecasters in Washington State, Alaska, and Hawaii to
implement and evaluate the RCON parameterization in the UFS model. The evaluation will
include a global evaluation using the GFS and regional evaluations using the HRRR/RRFS
modeling systems. The proposed project promises more accurate precipitation forecasts for a
variety of environmental conditions. By adopting the RCON changes into the UFS model, we also
expect other benefits, including better forecasting of wet and icy surface conditions, as well as
better forecasts of solar radiation and convection.
The primary impacts and benefits of the proposed project are more accurate UFS
precipitation and microphysics forecasts during warm-rain situations.
improving warm rain moist physics in the unified forecast system, the proposed project aims to implement and evaluate the rcon parameterization in the ufs model and will include a global evaluation using the gfs and regional evaluations using the hrrr rrfs modeling systems, modern state of the science modeling systems require realistic simulations of clouds and precipitation in the u s particularly over coastal regions along the west coast alaska and hawaii a significant fraction of precipitation results from warm rain which is formed by the collision and coalescence of liquid water droplets despite its importance warm rain is often poorly represented by modern numerical weather prediction models specifically warm rain is characterized by large numbers of small raindrops in contrast virtually all current microphysical parameterizations incorrectly model such situations as more like cold rain with greater numbers of large drops results of model evaluations undertaken by the pi and his research group have shown that the thompson eidhammer microphysical parameterization scheme which is the parameterization that will be used by the ufs suffers from these warm rain deficiencies to address these biases we have modified the thomson eidhammer parameterization to simulate warm rain more accurately the parameterization modifications called rcon have been extensively tested over coastal washington state and show significant promise toward improving precipitation forecasts over alaska and hawaii rcon produces broader cloud droplet size distributions during warm rain environments resulting in greater production of warm rain during the proposed project we will work with scientists and collaborators at noaa emc as well as scientists forecasters in washington state alaska and hawaii to implement and evaluate the rcon parameterization in the ufs model the evaluation will include a global evaluation using the gfs and regional evaluations using the hrrr rrfs modeling systems the proposed project promises more accurate precipitation forecasts for a variety of environmental conditions by adopting the rcon changes into the ufs model we also expect other benefits including better forecasting of wet and icy surface conditions as well as better forecasts of solar radiation and convection
Environmental Modeling Center (EMC)
University of Washington
The proposed project aims to implement and evaluate the RCON parameterization in the UFS model and will include a global evaluation using the GFS and regional evaluations using the HRRR/RRFS modeling systems.
0
A machine learning postprocessor to mitigate QPF errors for improved hydrometeorological forecasting
Franz
JTTI
search_activity
2023
Level 3
The proposed work will work to advance NOAA forecasting by using machine learning (ML) to develop a QPF post-processing technique informed by QPF error climatology, member uniqueness, and the meteorology of the event.
Kristie J. Franz, William A. Gallus, Jr., Somak Dutta
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, High-Performance Computing (HPC), Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting
JTTI
The outcome of this project will be an ML-based postprocessor that effectively and efficiently accounts for quantitative precipitation forecast (QPF) displacement, timing, and magnitude errors in ensembles to create more accurate probabilistic quantitative precipitation forecast (PQPF) for predicting excessive rainfall. Our method will produce several forecast products, including probability-matched means and uncertainty information from the ML-adjusted QPFs and PQPF. We will work with our NOAA collaborators to test and evaluate our products, including as part of Flash Flood and Intense Rainfall Experiment (FFaIR). The forecast products will be verified using both standard statistics and via their application to the Flooded Locations and Simulated Hydrographs (FLASH) system. Finally, we will develop guidance for WPC to apply our algorithms to account for the major sources of precipitation forecast uncertainty and error in ensemble QPF.
The major outcome of this project will be a post-processing technique that creates more accurate probabilistic quantitative precipitation forecast (PQPF) by effectively and efficiently accounting for quantitative precipitation forecast (QPF) displacement, timing, and magnitude errors in ensembles. Although we will work with Rapid Refresh Forecasting System (RRFS) since it is currently planned to be the primary short-range ensemble within the Unified Forecast System (UFS), our research also will apply broadly because the QPF adjustment technique could be adapted to work with any meteorological model,
and therefore could be implemented nationally at various NOAA offices that conduct hydrometeorological forecasting. Our method will be evaluated across a large portion of the US that experiences summer convective storms; thus, demonstrating that our method will be easy to
implement on a large scale.
Using familiar forecast and verification tools, such as RRFS and Method for Object-based Diagnostic Evaluation (MODE), and forecast verification metrics regularly used in NOAA, we will be able to put results from this work into the context of current operations. Further, the QPF and flood analyses would inform River Forecast Centers (RFCs) about the benefit that could be gained from our method in their operations and be relevant to Weather Forecast Offices (WFOs) in their flash flood forecasting applications. The impact will be a nationally applicable tool that the NWS forecasters can use to provide decision makers with more informative forecasts of excessive rainfall. Methods to improve QPF are needed to give local officials and the public actionable information at increased lead-times, allowing them to be more proactive in their efforts to mitigate severe storm-related losses.
WFO DMX
Iowa State University
NA23OAR4590377
Water Extremes
Iowa
Joint Technology Transfer Initiative (JTTI)
September 2023 - August 2025
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, High-Performance Computing (HPC), Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting
a machine learning postprocessor to mitigate qpf errors for improved hydrometeorological forecasting, the proposed work will work to advance noaa forecasting by using machine learning ml to develop a qpf post processing technique informed by qpf error climatology member uniqueness and the meteorology of the event, the outcome of this project will be an ml based postprocessor that effectively and efficiently accounts for quantitative precipitation forecast qpf displacement timing and magnitude errors in ensembles to create more accurate probabilistic quantitative precipitation forecast pqpf for predicting excessive rainfall our method will produce several forecast products including probability matched means and uncertainty information from the ml adjusted qpfs and pqpf we will work with our noaa collaborators to test and evaluate our products including as part of flash flood and intense rainfall experiment ffair the forecast products will be verified using both standard statistics and via their application to the flooded locations and simulated hydrographs flash system finally we will develop guidance for wpc to apply our algorithms to account for the major sources of precipitation forecast uncertainty and error in ensemble qpf
Iowa State University
The proposed work will work to advance NOAA forecasting by using machine learning (ML) to develop a QPF post-processing technique informed by QPF error climatology, member uniqueness, and the meteorology of the event.
0
A Hybrid Statistical-Dynamical System for the Seamless Prediction of Daily Extremes and Subseasonal to Seasonal Climate Variability
Collins
Testbeds
check_circle
2018
Level 3
To demonstrate the skill and suitability for operations of a statistical-dynamical prediction system that yields seamless probabilistic forecasts of daily extremes and sub seasonal-to-seasonal temperature and precipitation.
Dan Collins
Complete
Wed Aug 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting, Forecast Skill Metrics, Post-processing, Probabilistic Weather Forecasting, Teleconnections
CTB
Community Atmosphere Model (CAM), Community Earth System Model (CESM), North American Multi-Model Ensemble (NMME), Whole Atmosphere Community Climate Model (WACCM)
We propose to demonstrate the skill and suitability for operations of a statistical- dynamical prediction system that yields seamless probabilistic forecasts of daily extremes and subseasonal-to-seasonal temperature and precipitation. We recently demonstrated a Bayesian statistical method for post-processing seasonal forecasts of mean temperature and precipitation from the North American Multi-Model Ensemble (NMME). We now seek to test the utility of an updated hybrid statistical-dynamical prediction system that facilitates seamless subseasonal and seasonal forecasting. Specific updates we intend to implement for the forecast system include: 1) Aggregation of post-processed daily forecasts to enhance the skill of subseasonal forecasts on weekly and biweekly timescales; and 2) Disaggrega- tion of seasonal forecasts to determine the probability of daily extremes. We propose to apply the method developed by the co-PIs of this proposal (Schepen et al., 2017b) to first calibrate climate model daily forecasts through Bayesian joint probability modeling and then relate these calibrated daily forecasts made at different leads through application of the Schaake Shuffle approach (Clark et al., 2004). The calibrated and shuffled daily forecasts will then be aggregated for subseasonal and seasonal prediction. Through this approach, forecast skill that exists at shorter subseasonal leads (e.g., weeks 1-2) will be used to improve forecast skill at longer leads (e.g., weeks 3-4). Furthermore, using the methodology developed by the co-PIs (Schepen et al., 2017a), we propose to disaggregate seasonal forecasts from the NMME into distributions of daily values. We will first develop hybrid statistical-dynamical models that use skillful NMME forecasts of large scale climate patterns (e.g., ENSO) in statistical models that relate these remote climate patterns to North American temperature and precipitation variability. Forecasts from these hybrid models first will be used to predict seasonal temperature and precipitation, and then will be statistically disaggregated to generate consistent, seamless forecasts of the distribution of daily temperatures or precipitation amounts. The probability of daily extremes of tem- perature or precipitation during a seasonal forecast period will be produced, taking full advantage of the enhanced predictability offered by interannual models of variability, such as ENSO, the Arctic Oscillation, or climate change. Importantly, this method allows for the representation of daily extremes consistent with climate conditions. This project explores the use of machine learning.
Reliable probabilistic forecasts of the occurrence of daily extremes associated with subseasonal and seasonal variability from calibrated dynamical models will increase the public awareness of the link between climate events and daily extremes, potentially reduce the cost of such events economically, and reduce risk to the health and safety of the population.
CPC
Dan Collins
NOAA/NWS/CPC
OWAQ18-CTB-I-1
Extreme Temperatures, Water Extremes
Maryland
Testbeds
August 2018 - September 2021
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting, Forecast Skill Metrics, Post-processing, Probabilistic Weather Forecasting, Teleconnections
a hybrid statistical dynamical system for the seamless prediction of daily extremes and subseasonal to seasonal climate variability, to demonstrate the skill and suitability for operations of a statistical dynamical prediction system that yields seamless probabilistic forecasts of daily extremes and sub seasonal to seasonal temperature and precipitation, we propose to demonstrate the skill and suitability for operations of a statistical dynamical prediction system that yields seamless probabilistic forecasts of daily extremes and subseasonal to seasonal temperature and precipitation we recently demonstrated a bayesian statistical method for post processing seasonal forecasts of mean temperature and precipitation from the north american multi model ensemble nmme we now seek to test the utility of an updated hybrid statistical dynamical prediction system that facilitates seamless subseasonal and seasonal forecasting specific updates we intend to implement for the forecast system include 1 aggregation of post processed daily forecasts to enhance the skill of subseasonal forecasts on weekly and biweekly timescales and 2 disaggrega tion of seasonal forecasts to determine the probability of daily extremes we propose to apply the method developed by the co pis of this proposal schepen et al 2017b to first calibrate climate model daily forecasts through bayesian joint probability modeling and then relate these calibrated daily forecasts made at different leads through application of the schaake shuffle approach clark et al 2004 the calibrated and shuffled daily forecasts will then be aggregated for subseasonal and seasonal prediction through this approach forecast skill that exists at shorter subseasonal leads e g weeks 1 2 will be used to improve forecast skill at longer leads e g weeks 3 4 furthermore using the methodology developed by the co pis schepen et al 2017a we propose to disaggregate seasonal forecasts from the nmme into distributions of daily values we will first develop hybrid statistical dynamical models that use skillful nmme forecasts of large scale climate patterns e g enso in statistical models that relate these remote climate patterns to north american temperature and precipitation variability forecasts from these hybrid models first will be used to predict seasonal temperature and precipitation and then will be statistically disaggregated to generate consistent seamless forecasts of the distribution of daily temperatures or precipitation amounts the probability of daily extremes of tem perature or precipitation during a seasonal forecast period will be produced taking full advantage of the enhanced predictability offered by interannual models of variability such as enso the arctic oscillation or climate change importantly this method allows for the representation of daily extremes consistent with climate conditions this project explores the use of machine learning
Climate Prediction Center (CPC)
NOAA/NWS/CPC
To demonstrate the skill and suitability for operations of a statistical-dynamical prediction system that yields seamless probabilistic forecasts of daily extremes and sub seasonal-to-seasonal temperature and precipitation.
2
A New Technique for Improved MJO Prediction
Zhang
Testbeds
check_circle
2018
Level 3
To revise and test the new method of MJO tracking for real-time application in enhancing our ability to predict the MJO and its related extremes.
Chidong Zhang, Wanqiu Wang
Complete
Wed Aug 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Jan 26 2022 03:00:00 GMT-0500 (Eastern Standard Time)
Forecast Skill Metrics, Teleconnections
CTB
Coupled Forecast System (CFSv2), North American Multi-Model Ensemble (NMME)
Predicting the Madden-Julian Oscillation (MJO) is key to global prediction on subseasonal- to-seasonal (S2S) timescales. The Real-time Multivariate MJO (RMM) index is commonly used to measure MJO prediction skill and used as a predictor for predictions of other parameters over the globe. This index has proven to be very useful in providing information of the planetary-scale circulation pattern associated with the MJO and eastward propagation of the MJO in a statistical sense. But it is known to be ineffective in accurately identifying longitudinal locations of convection centers for individual MJO events. This shortcoming of the RMM index has hindered its applications in predicting remote influences of the MJO, which sensitively depend on longitudinal locations of MJO convection centers. Recently, we have developed a new method that identifies individual MJO events by tracking eastward motion of large-scale precipitation anomalies along the equator. This method allows several key parameters to be quantitatively and accurately defined for individual MJO events. The parameters include the longitude and time of MJO initiation and termination, speed and range of MJO propagation, life span and mean strength of the MJO, and intervals of neighboring MJO events. Prediction of these parameters is important in capturing the tropical forcing of subtropical and extratropical circulations at intraseasonal timescales, but is difficult to derive from the RMM index. The new MJO tracking method has been used successfully in quantifying the barrier effect on MJO propagation by the Indo-Pacific Maritime Continent and interpreting the issue of MJO simulations by global models. With suitable minor adjustment, this method can be applied to real-time MJO forecast and provide an alternative technique for monitoring MJO events and measuring their prediction skill. The objective of this proposed project is to revise and test the new method of MJO tracking for real-time application in enhancing our ability to predict the MJO and its related extremes. We plan to take the existing MJO tracking method and develop it into one that can be used in real- time forecast. The proposed work includes: (1) Adjust the existing MJO tracking method to make it applicable to real-time forecast. (2) Apply the real-time tracking method to CFSv2 historical reforecast to develop a statistical base of MJO forecast skill as a function of the season and the MJO (initiation location, etc.). (3) Compare MJO prediction measurement based on this new tracking method and other EOF- based methods (RMM index, OLR index) and seek possible ways to complement each other. (4) Test the new MJO tracking method or tracking-EOF hybrid method in real-time environment. These steps will be applied to CFSv2 forecast only. If successful, we plan to expand this work to S2S Prediction products or NMME in a follow-up project.
Accurate identification of individual MJO events, especially longitudinal locations of their convection, is critical to assess their global impacts and its predictability. The proposed work aims at applying a new MJO tracking method for improvement of the real-time MJO monitoring and prediction. Such an effort will enhance our understanding of the MJO relationship to the weather and climate in the subtropics and extratropics and the MJO predictability, and is beneficial to the scientific community in general. Improved MJO monitoring and prediction will benefit the general public by providing more reliable climate assessment and prediction.
CPC
Wanqiu Wang
NOAA/OAR/PMEL
OWAQ18-CTB-I-2
Relevant to All Hazards
Washington
Testbeds
August 2018 - January 2022
Forecast Skill Metrics, Teleconnections
a new technique for improved mjo prediction, to revise and test the new method of mjo tracking for real time application in enhancing our ability to predict the mjo and its related extremes, predicting the madden julian oscillation mjo is key to global prediction on subseasonal to seasonal s2s timescales the real time multivariate mjo rmm index is commonly used to measure mjo prediction skill and used as a predictor for predictions of other parameters over the globe this index has proven to be very useful in providing information of the planetary scale circulation pattern associated with the mjo and eastward propagation of the mjo in a statistical sense but it is known to be ineffective in accurately identifying longitudinal locations of convection centers for individual mjo events this shortcoming of the rmm index has hindered its applications in predicting remote influences of the mjo which sensitively depend on longitudinal locations of mjo convection centers recently we have developed a new method that identifies individual mjo events by tracking eastward motion of large scale precipitation anomalies along the equator this method allows several key parameters to be quantitatively and accurately defined for individual mjo events the parameters include the longitude and time of mjo initiation and termination speed and range of mjo propagation life span and mean strength of the mjo and intervals of neighboring mjo events prediction of these parameters is important in capturing the tropical forcing of subtropical and extratropical circulations at intraseasonal timescales but is difficult to derive from the rmm index the new mjo tracking method has been used successfully in quantifying the barrier effect on mjo propagation by the indo pacific maritime continent and interpreting the issue of mjo simulations by global models with suitable minor adjustment this method can be applied to real time mjo forecast and provide an alternative technique for monitoring mjo events and measuring their prediction skill the objective of this proposed project is to revise and test the new method of mjo tracking for real time application in enhancing our ability to predict the mjo and its related extremes we plan to take the existing mjo tracking method and develop it into one that can be used in real time forecast the proposed work includes 1 adjust the existing mjo tracking method to make it applicable to real time forecast 2 apply the real time tracking method to cfsv2 historical reforecast to develop a statistical base of mjo forecast skill as a function of the season and the mjo initiation location etc 3 compare mjo prediction measurement based on this new tracking method and other eof based methods rmm index olr index and seek possible ways to complement each other 4 test the new mjo tracking method or tracking eof hybrid method in real time environment these steps will be applied to cfsv2 forecast only if successful we plan to expand this work to s2s prediction products or nmme in a follow up project
Climate Prediction Center (CPC)
NOAA/OAR/PMEL
To revise and test the new method of MJO tracking for real-time application in enhancing our ability to predict the MJO and its related extremes.
0
Operational transition of novel statistical-dynamical forecasts for tropical subseasonal-to-seasonal drivers
Baxter
Testbeds
check_circle
2018
Level 3
To transition NCICS.org/mjo fully into operations at CPC.
Stephen Baxter, Carl Schreck
Complete
Wed Aug 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Dynamics and Nesting, Teleconnections
CTB
Climate Forecast System (CFS)
Subseasonal-to-seasonal (S2S) has emerged as one of the great frontiers for atmospheric predictability. These time scales of weeks-to-months are at the heart of the mission for NOAA's Climate Prediction Center (CPC), which has been particularly focused on expanding and improving their 3-4-week forecasts. Dynamical S2S models have improved significantly over recent years, but they have yet to fully tap the potential predictability of coherent tropical modes like the Madden-Julian Oscillation (MJO). A unique approach to this problem has been implemented on NCICS.org/mjo. This website takes recent observations and appends them with 45-day forecasts from the Climate Forecast System version 2 (CFSv2). The combined data are then Fourier filtered in space and time for some of the dominant modes of S2S variability in the tropics: the MJO, convectively coupled equatorial waves, and low-frequency variability like the El Nio-Southern Oscillation (ENSO). This filtering highlights the most predictable aspects of the S2S system. The website includes numerous maps, Hovmllers, and indices for identifying and predicting these modes. It has been updating daily since 2011 with several upgrades and iterations over the years. These diagnostics have become routine inputs for CPC's Global Tropical Hazards (GTH) outlook. NCICS.org/mjo has reached a level a maturity (Readiness Level 7) where it is a prime candidate to be transitioned fully into operations at CPC. This proposal outlines that transition. It also includes some modest development work that will use proven methods to further tailor these diagnostics to CPC's goals. Once these processes have been transferred to CPC, future work could easily expand them to other models that CPC is already ingesting. The proposed project is directly to the MAPP/NOAA Climate Test Bed competition because it takes a demonstrated research product and transitions it into operations at NOAA's CPC. In particular, the proposal leverages a newly proved methodology to improve key forecast products like the Weeks 3-4 outlooks. Transitioning this product to operations will help NOAA fulfill its long-term climate goals by expanding CPC's capabilities for monitoring and prediction using existing models and observations. These diagnostics are already used by a number of public, private, academic, and international users to provide them the climate intelligence they need for resilience against climate variability.
NCICS.org/mjo currently provides tropical diagnostics to hundreds of users every month (Fig. 11). These users come from nearly every U.S. state and from countries around the globe (Fig. 12). The website includes a link to a Google form (https:goo.gl/forms/ezvEcAMLK5zDgOSu1) where users may register to receive email updates regarding the website and its diagnostics. Over 100 users have registered with a variety of backgrounds including forecasters at CPC and NHC, the Air Force, and academia. Private sector users include those in the financial, reinsurance, agricultural, and energy industry that use these forecasts to inform commodities futures trading. It also includes forecasters from meteorological agencies in 22 countries, predominantly in South America and Southeast Asia.
CPC
Stephen Baxter
NOAA/NWS/CPC
North Carolina State University
NA18OAR4310297
Relevant to All Hazards
Maryland, North Carolina
Testbeds
August 2018 - July 2021
Coupled Models & Techniques, Dynamics and Nesting, Teleconnections
operational transition of novel statistical-dynamical forecasts for tropical subseasonal to seasonal drivers, to transition ncics org mjo fully into operations at cpc, subseasonal to seasonal s2s has emerged as one of the great frontiers for atmospheric predictability these time scales of weeks to months are at the heart of the mission for noaa's climate prediction center cpc which has been particularly focused on expanding and improving their 3-4 week forecasts dynamical s2s models have improved significantly over recent years but they have yet to fully tap the potential predictability of coherent tropical modes like the madden-julian oscillation mjo a unique approach to this problem has been implemented on ncics org mjo this website takes recent observations and appends them with 45 day forecasts from the climate forecast system version 2 cfsv2 the combined data are then fourier filtered in space and time for some of the dominant modes of s2s variability in the tropics the mjo convectively coupled equatorial waves and low frequency variability like the el nio-southern oscillation enso this filtering highlights the most predictable aspects of the s2s system the website includes numerous maps hovmllers and indices for identifying and predicting these modes it has been updating daily since 2011 with several upgrades and iterations over the years these diagnostics have become routine inputs for cpc's global tropical hazards gth outlook ncics org mjo has reached a level a maturity readiness level 7 where it is a prime candidate to be transitioned fully into operations at cpc this proposal outlines that transition it also includes some modest development work that will use proven methods to further tailor these diagnostics to cpc's goals once these processes have been transferred to cpc future work could easily expand them to other models that cpc is already ingesting the proposed project is directly to the mapp noaa climate test bed competition because it takes a demonstrated research product and transitions it into operations at noaa's cpc in particular the proposal leverages a newly proved methodology to improve key forecast products like the weeks 3 4 outlooks transitioning this product to operations will help noaa fulfill its long term climate goals by expanding cpc's capabilities for monitoring and prediction using existing models and observations these diagnostics are already used by a number of public private academic and international users to provide them the climate intelligence they need for resilience against climate variability
Climate Prediction Center (CPC)
NOAA/NWS/CPC, North Carolina State University
To transition NCICS.org/mjo fully into operations at CPC.
2
Sensitivity of NMME Seasonal Predictions to Ocean Eddy Resolving Coupled Models
Kirtman
Testbeds
check_circle
2018
Level 3
To repeat the NMME retrospective predictions with a version of CCSM4 that utilizes significantly higher resolution in the ocean (and ice) component model (i.e., 0.1 degree vs. 1 degree) and increased atmospheric component model resolution (0.5 degree vs. 1 degree).
Benjamin Kirtman, Robert Burgman
Complete
Wed Aug 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
We propose to repeat the NMME retrospective predictions with a version of CCSM4 that utilizes significantly higher resolution in the ocean (and ice) component model (i.e., 0.1 degree vs. 1 degree) and increased atmospheric component model resolution (0.5 degree vs. 1 degree). In addition to implementing the NMME retrospective forecast protocol, the proposed work includes a detailed large-scale forecast quality assessment using both deterministic and probabilistic measures. While the proposed research focuses on one particular model, we will examine how the inclusion of this model affects the multi-model forecast quality.
The proposed research contributes to NOAA's long-term climate goal as outlined in NOAA's Next Generation Strategic Plan (NGSP). In particular, we will address the objective of "Improved scientific understanding of the changing climate system and its impacts", via advancing sub-seasonal to seasonal prediction of extreme events.
CPC
David Dewitt
University of Miami-RSMAS
Florida International University
sensitivity of nmme seasonal predictions to ocean eddy resolving coupled models, to repeat the nmme retrospective predictions with a version of ccsm4 that utilizes significantly higher resolution in the ocean and ice component model i e 0 1 degree vs 1 degree and increased atmospheric component model resolution 0 5 degree vs 1 degree, we propose to repeat the nmme retrospective predictions with a version of ccsm4 that utilizes significantly higher resolution in the ocean and ice component model i e 0 1 degree vs 1 degree and increased atmospheric component model resolution 0 5 degree vs 1 degree in addition to implementing the nmme retrospective forecast protocol the proposed work includes a detailed large scale forecast quality assessment using both deterministic and probabilistic measures while the proposed research focuses on one particular model we will examine how the inclusion of this model affects the multi model forecast quality
Climate Prediction Center (CPC)
University of Miami-RSMAS, Florida International University
To repeat the NMME retrospective predictions with a version of CCSM4 that utilizes significantly higher resolution in the ocean (and ice) component model (i.e., 0.1 degree vs. 1 degree) and increased atmospheric component model resolution
27
Skillfully Predicting Atmospheric Rivers and Their Impacts in Weeks 2-5 Based on the State of the MJO and QBO
Barnes
Testbeds
check_circle
2018
Level 3
To successfully transition the AR frequency forecast tool to operations, (2) refine and extend the methodology of Mundhenk et al. (2017) to maximize skill of AR activity and AR-related variables, and (3) leverage additional predictors, including dynamical model MJO forecasts, to extend the skillful forecasts beyond Week 5.
Elizabeth Barnes
Complete
Wed Aug 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Jul 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Teleconnections
CTB
The goals of the proposed activities are threefold: (1) successfully transition the AR frequency forecast tool to operations, (2) refine and extend the methodology of Mundhenk et al. (2017) to maximize skill of AR activity and AR-related variables, and (3) leverage additional predictors, including dynamical model MJO forecasts, to extend the skillful forecasts beyond Week 5.
Efforts will support CPC to improve operational subseasonal forecasts of extreme precipitation and drought for the protection of life and property
CPC
Daniel Harnos
Colorado State University
NA18OAR4310296
Water Extremes
Colorado, Maryland
Testbeds
August 2018 - July 2022
Dynamics and Nesting, Teleconnections
skillfully predicting atmospheric rivers and their impacts in weeks 2 5 based on the state of the mjo and qbo, to successfully transition the ar frequency forecast tool to operations 2 refine and extend the methodology of mundhenk et al 2017 to maximize skill of ar activity and ar related variables and 3 leverage additional predictors including dynamical model mjo forecasts to extend the skillful forecasts beyond week 5, the goals of the proposed activities are threefold 1 successfully transition the ar frequency forecast tool to operations 2 refine and extend the methodology of mundhenk et al 2017 to maximize skill of ar activity and ar related variables and 3 leverage additional predictors including dynamical model mjo forecasts to extend the skillful forecasts beyond week 5
Climate Prediction Center (CPC)
Colorado State University
To successfully transition the AR frequency forecast tool to operations, (2) refine and extend the methodology of Mundhenk et al. (2017) to maximize skill of AR activity and AR-related variables, and (3) leverage additional predictors,
7
Subseasonal to Seasonal Prediction with NCAR's Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model (CESM2-WACCM)
Richter
Testbeds
check_circle
2018
Level 3
To test and demonstrate the utility of NCAR's Community Earth System Model, version 2 with the Whole Atmosphere Community Climate
Model as its atmospheric component (CESM2-WACCM) as a sub seasonal-to-seasonal (S2S) prediction system that could improve NCEP's operational multi-model S2S prediction capability.
Jadwiga Richter, Dan Collins, Judith Perlwitz
Complete
Wed Aug 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Community Earth System Model (CESM), Whole Atmosphere Community Climate Model (WACCM)
The goal of this joint project between NCAR, NOAA ESRL Physical Sciences Division and NOAA/NCEP Climate Prediction Center (CPC) is to test and demonstrate the utility of NCAR's Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model as its atmospheric component (CESM2-WACCM) as a subseasonal-to-seasonal (S2S) prediction system that could improve NCEP's operational multi-model S2S prediction capability.
The research proposed here aims to improve NOAA's S2S prediction capability, by including a new model in the ensemble prediction system which includes several sources of potential predictability (SSWs, MJO and QBO), and hence has the potential to directly impact key industries and lives of many individuals. In addition, the data from this project will be made available to the research community, so further studies can be undertaken to improve the understanding of predictability on S2S timescales. A comparison of S2S forecast skill in a series of models that differ in representation of stratospheric and tropospheric processes will inform NOAA on necessary model requirements for its Next Generation Global Prediction System (NGGPS).
CPC
Dan Collins
University Corporation for Atmospheric Research
NOAA/NWS/CPC
NOAA/ESRL/PSD
subseasonal to seasonal prediction with ncar's community earth system model version 2 with the whole atmosphere community climate model cesm2 waccm, to test and demonstrate the utility of ncar's community earth system model version 2 with the whole atmosphere community climate model as its atmospheric component cesm2 waccm as a sub seasonal to seasonal s2s prediction system that could improve ncep's operational multi model s2s prediction capability, the goal of this joint project between ncar noaa esrl physical sciences division and noaa ncep climate prediction center cpc is to test and demonstrate the utility of ncar's community earth system model version 2 with the whole atmosphere community climate model as its atmospheric component cesm2 waccm as a subseasonal to seasonal s2s prediction system that could improve ncep's operational multi model s2s prediction capability
Climate Prediction Center (CPC)
University Corporation for Atmospheric Research, NOAA/NWS/CPC, NOAA/ESRL/PSD
To test and demonstrate the utility of NCAR's Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model as its atmospheric component (CESM2-WACCM) as a sub seasonal-to-seasonal (S2S) prediction system that could
3
Testing, refinement and demonstration of probabilistic multi-model, calibrated sub-seasonal global forecast products
Robertson
Testbeds
check_circle
2018
Level 3
To test, refine, and demonstrate the potential of our previously-developed calibration and multi-model ensemble methodology to improve CPC's sub-seasonal probabilistic climate outlooks, including individual weekly and bi-weekly (weeks 2-3, weeks 3-4) and weeks 1-4 averages of precipitation and temperature, and transition the software developed at IRI to CPC operations.
Andrew Robertson, Michael Tippett, Nicholas Vigaud
Complete
Wed Aug 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Jul 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Probabilistic Weather Forecasting
CTB
Coupled Forecast System (CFSv2), Global Ensemble Forecast System (GEFS)
This CTB project will test, refine, and demonstrate the potential of our previously-developed calibration and multi-model ensemble methodology to improve CPC's sub-seasonal probabilistic climate outlooks, including individual weekly and bi-weekly (weeks 2-3, weeks 3-4) and weeks 1-4 averages of precipitation and temperature, and transition the software developed at IRI to CPC operations.
The proposed work will benefit the S2S research and forecasting communities by extending previous sub-seasonal forecast calibration and MME results to additional models and larger multi- model combinations (SubX and S2S models).
CPC
Dan Collins
Columbia University
NA18OAR4310295
Extreme Temperatures, Water Extremes
New York
Testbeds
August 2018 - July 2022
Data Assimilation and Ensembles, Probabilistic Weather Forecasting
testing refinement and demonstration of probabilistic multi model calibrated sub seasonal global forecast products, to test refine and demonstrate the potential of our previously developed calibration and multi model ensemble methodology to improve cpc's sub seasonal probabilistic climate outlooks including individual weekly and bi weekly weeks 2-3 weeks 3-4 and weeks 1-4 averages of precipitation and temperature and transition the software developed at iri to cpc operations, this ctb project will test refine and demonstrate the potential of our previously developed calibration and multi model ensemble methodology to improve cpc's sub seasonal probabilistic climate outlooks including individual weekly and bi weekly weeks 2-3 weeks 3-4 and weeks 1-4 averages of precipitation and temperature and transition the software developed at iri to cpc operations
Climate Prediction Center (CPC)
Columbia University
To test, refine, and demonstrate the potential of our previously-developed calibration and multi-model ensemble methodology to improve CPC's sub-seasonal probabilistic climate outlooks, including individual weekly and bi-weekly (weeks 2-3, weeks 3-4) and weeks 1-4 averages
1
Improving NCEP GEFS for Subseasonal Forecasts
Liang
Testbeds
check_circle
2020
Level 3
To configure, test, and prepare for NCEP operational implementation a global ensemble forecast system for the lead-time of 1-45 days using a fully coupled model with more advanced physics representations and ensemble methods that will outperform the benchmark GEFSv12.
Xin-Zhong Liang, Yuejian Zhu
Complete
Sat Aug 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles
CTB
Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
This project will configure, test, and evaluate a global ensemble forecast system for the lead-time of 1-45 days using a fully coupled model with more advanced physics representations and ensemble methods (hereafter called a candidate GEFSv13 or the new model) that will outperform the benchmark GEFSv12.
The goal of this project is to improve the Climate Prediction Center's weeks 3&4 and monthly outlooks as well as the NMME/SubX project and the NAEFS subseasonal/monthly forecasts.
CPC
Wanqiu Wang
University of Maryland
NOAA/NWS/EMC
NA20OAR4590313
Relevant to All Hazards
Maryland
Testbeds
August 2020 - July 2023
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles
improving ncep gefs for subseasonal forecasts, to configure test and prepare for ncep operational implementation a global ensemble forecast system for the lead time of 1 45 days using a fully coupled model with more advanced physics representations and ensemble methods that will outperform the benchmark gefsv12, this project will configure test and evaluate a global ensemble forecast system for the lead time of 1 45 days using a fully coupled model with more advanced physics representations and ensemble methods hereafter called a candidate gefsv13 or the new model that will outperform the benchmark gefsv12
Climate Prediction Center (CPC)
University of Maryland, NOAA/NWS/EMC
To configure, test, and prepare for NCEP operational implementation a global ensemble forecast system for the lead-time of 1-45 days using a fully coupled model with more advanced physics representations and ensemble methods that will
1
Accelerating Progress in Subseasonal to Seasonal Prediction Capabilities by Improving Subgrid-Scale Parameterizations in the Unified Forecast System
Green
Testbeds
check_circle
2020
Level 3
To test the impact of incorporating three new parameterizations of atmospheric subgrid-scale physical processes into the existing physics suite used for NOAA's Unified Forecast System (UFS) - specifically, the coupled atmosphere (FV3), ocean (MOM6), and sea ice (CICE5)
model - in order to advance NOAA's subseasonal to seasonal (S2S) prediction capabilities, including but not limited to fields such as precipitation over the U.S. and globally.
Benjamin Green, Vijay Tallapragada
Complete
Sat Aug 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Jul 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Dynamics and Nesting, Parameterization
CTB
Unified Forecast System (UFS)
We propose to test the impact of incorporating three new parameterizations of atmospheric subgrid-scale physical processes into the existing physics suite used for NOAA's Unified Forecast System (UFS) - specifically, the coupled atmosphere (FV3), ocean (MOM6), and sea ice (CICE5) model - in order to advance subseasonal to seasonal (S2S) prediction capabilities, including but not limited to fields such as precipitation over the U.S. and globally.
The goal of the proposed work is to advance S2S prediction capabilities, via development and maturation of the S2S portions (here, one-by-one testing of model physics) of the (coupled) UFS, with a strong (but not sole) emphasis on precipitation. NOAA as a whole will be one of the direct and immediate beneficiaries given the mandate in the Weather Research and Forecasting Innovation Act of 2017
EMC
Vijay Tallapragada
CIRES/University of Colorado
NOAA/ESRL/GSL
NOAA/NCEP/EMC
NA20OAR4590314
Water Extremes
Colorado, Maryland
Testbeds
August 2020 - July 2022
Atmospheric Physics, Coupled Models & Techniques, Dynamics and Nesting, Parameterization
accelerating progress in subseasonal to seasonal prediction capabilities by improving subgrid scale parameterizations in the unified forecast system, to test the impact of incorporating three new parameterizations of atmospheric subgrid scale physical processes into the existing physics suite used for noaa's unified forecast system ufs - specifically the coupled atmosphere fv3 ocean mom6 and sea ice cice5 model - in order to advance noaa's subseasonal to seasonal s2s prediction capabilities including but not limited to fields such as precipitation over the u s and globally, we propose to test the impact of incorporating three new parameterizations of atmospheric subgrid scale physical processes into the existing physics suite used for noaa's unified forecast system ufs - specifically the coupled atmosphere fv3 ocean mom6 and sea ice cice5 model - in order to advance subseasonal to seasonal s2s prediction capabilities including but not limited to fields such as precipitation over the u s and globally
Environmental Modeling Center (EMC)
CIRES/University of Colorado, NOAA/ESRL/GSL, NOAA/NCEP/EMC
To test the impact of incorporating three new parameterizations of atmospheric subgrid-scale physical processes into the existing physics suite used for NOAA's Unified Forecast System (UFS) - specifically, the coupled atmosphere (FV3), ocean (MOM6), and
1
Developing and Assessing Storminess Indices for Monitoring and Predicting Subseasonal Variations in Storminess near Alaska
Chang
Testbeds
check_circle
2020
Level 3
To extend results from previous studies to develop and further assess subseasonal storminess guidance products for the Alaska region.
Edmund Kar-Man Chang, Wanqiu Wang
Complete
Sat Aug 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Oct 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance
CTB
Climate Forecast System (CFS), Global Ensemble Forecast System (GEFS)
This project makes use of both existing and innovative statistical techniques to post- process NOAA CFS and GEFS SubX ensemble models output to develop new subseasonal monitoring and prediction tools for storminess for the Alaska region, thus contributing to advancing subseasonal to seasonal prediction capabilities.
These new tools will provide various stakeholders with new and important information on storminess which is closely linked to high impact weather, and thus will contribute to NOAA's and OWAQ's mission to support "research to save lives, reduce property damage, and enhance the national economy".
CPC
Wanqiu Wang
Stony Brook University
NOAA/NWS/CPC
NA20OAR4590315
Severe Weather, Water Extremes
Maryland, New York
Testbeds
August 2020 - October 2023
Data Assimilation and Ensembles, Model & Forecast Guidance
developing and assessing storminess indices for monitoring and predicting subseasonal variations in storminess near alaska, to extend results from previous studies to develop and further assess subseasonal storminess guidance products for the alaska region, this project makes use of both existing and innovative statistical techniques to post process noaa cfs and gefs subx ensemble models output to develop new subseasonal monitoring and prediction tools for storminess for the alaska region thus contributing to advancing subseasonal to seasonal prediction capabilities
Climate Prediction Center (CPC)
Stony Brook University, NOAA/NWS/CPC
To extend results from previous studies to develop and further assess subseasonal storminess guidance products for the Alaska region.
2
ENSO-MJO Diagnostics in an Energetic Framework
Stan
Testbeds
check_circle
2020
Level 3
To improve the new diagnostics proposed by Lybarger et al. (2019) to meet operational requirements and integrate them into an experimental version of
the suite of tools used for operational ENSO forecast and to diagnose the ENSO-MJO relationship in the UFS benchmark runs and to inform the model developers on the strengths and weaknesses of UFS in simulating the physical mechanisms observed to drive ENSO in nature.
Cristiana Stan, Zeng-Zhen Hu, Avichal Mehra
Complete
Sat Aug 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Model Evaluation Tools Plus (METPlus), Unified Forecast System (UFS)
Diagnostics of the ENSO and MJO relationship will be improved to meet operational requirements and integrated into an experimental set of tools. The diagnostics will be integrated into METplus. The second objective is to diagnose the ENSO-MJO relationship in the UFS benchmark runs and to inform the model developers on the strengths and weaknesses of UFS in simulating the physical mechanisms observed to drive ENSO in nature.
The long term goal is to improve ENSO diagnostics and forecast ability.
CPC
Zeng-Zhen Hu
George Mason University
NOAA/NWS/CPC
NOAA/NCEP/EMC
enso mjo diagnostics in an energetic framework, to improve the new diagnostics proposed by lybarger et al 2019 to meet operational requirements and integrate them into an experimental version of the suite of tools used for operational enso forecast and to diagnose the enso mjo relationship in the ufs benchmark runs and to inform the model developers on the strengths and weaknesses of ufs in simulating the physical mechanisms observed to drive enso in nature, diagnostics of the enso and mjo relationship will be improved to meet operational requirements and integrated into an experimental set of tools the diagnostics will be integrated into metplus the second objective is to diagnose the enso mjo relationship in the ufs benchmark runs and to inform the model developers on the strengths and weaknesses of ufs in simulating the physical mechanisms observed to drive enso in nature
Climate Prediction Center (CPC)
George Mason University, NOAA/NWS/CPC, NOAA/NCEP/EMC
To improve the new diagnostics proposed by Lybarger et al. (2019) to meet operational requirements and integrate them into an experimental version of the suite of tools used for operational ENSO forecast and to diagnose
8
Ensemble Post Processing to Remove Projections onto Temporal Spatial Error Eigenvectors to Optimize Seasonal to Subseasonal Forecasts
Roundy
Testbeds
check_circle
2022
Level 3
To improve the quality of subseasonal forecasts of temperature, geopotential height, and precipitation targeting periods days to weeks in advance.
Paul Roundy, Emerson LaJoie
Complete
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Post-processing, Verification & Validation
CTB
This project calculates the climatology of model error, along with the leading geographical patterns
of model error that grow and evolve with lead time. It then develops algorithms to identify these
patterns in forecasts while forecasts are being made and before validation data are available to
calculate model error directly. It then removes the projected error patterns from the forecast data,
and attempts to construct the most likely evolutions of likely outcomes that will evolve instead of
those error patterns. Code produced will be implemented in real time at the NOAA Climate
Prediction Center over a two year period.
The ultimate recipient of the code, for implementation, is coinvestigator Emerson N. LaJoie at NOAA/NWS/NCEP/CPC, emerson.lajoie@noaa.gov. The ultimate objective is to improve the quality of subseasonal forecasts of temperature, geopotential height, and precipitation targeting periods days to weeks in advance. Societal benefits will include that forecasters and end users will be made more aware of the most likely pathways whereby model forecasts fail, and results will highlight these patterns as they begin to emerge in forecasts. Estimation and removal of these signals will concentrate patterns more likely to include useful skill in the residual after subtraction.
Providing access to these error patterns and real time projection onto them in real time will also allow forecasters to represent the fraction of future variability that a given forecast is likely to represent accurately, versus aspects that are likely to emerge as error. The specific processes implemented in the products to be transferred, including assessment of related improvements in forecast skill, are discussed below under Methods and Activities.
CPC
Wassila Thiaw
State University of New York , University at Albany
NA22OAR4590218
Extreme Temperatures, Water Extremes
New York
Testbeds
August 2022 - July 2025
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Post-processing, Verification & Validation
ensemble post processing to remove projections onto temporal spatial error eigenvectors to optimize seasonal to subseasonal forecasts, to improve the quality of subseasonal forecasts of temperature geopotential height and precipitation targeting periods days to weeks in advance, this project calculates the climatology of model error along with the leading geographical patterns of model error that grow and evolve with lead time it then develops algorithms to identify these patterns in forecasts while forecasts are being made and before validation data are available to calculate model error directly it then removes the projected error patterns from the forecast data and attempts to construct the most likely evolutions of likely outcomes that will evolve instead of those error patterns code produced will be implemented in real time at the noaa climate prediction center over a two year period
Climate Prediction Center (CPC)
State University of New York , University at Albany
To improve the quality of subseasonal forecasts of temperature, geopotential height, and precipitation targeting periods days to weeks in advance.
2
Diagnostics Package for MJO-Teleconnections
Stan
Testbeds
check_circle
2022
Level 3
To expand diagnostics and to unify them into a portable package that can be applied to evaluation of UFS.
Christiana Stan, Avichal Mehra, F. Martin Ralph, Daniela Domeisen, Yutian Wu, Chaim Garfinkel, Jiabao Wang, Andrea Jenney, Cheng Zheng, Hyemi Kim
Complete
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Model Evaluation Tools Plus (METPlus), Seasonal Forecast System (SFS), Unified Forecast System (UFS)
Recognizing the importance of MJO teleconnections for the S2S forecast skill, the MJO and Teleconnections sub-project of the WMO/WWRP/WCRP/S2S Prediction Project, organized most of the MJO-teleconnections diagnostics available in the community into a collection of diagnostics suitable for operational forecasts and applied them to all models that publish their S2S forecast into the S2S database (Stan et al., 2021). The suite of diagnostics is designed to evaluate models' ability to predict the MJO teleconnections to the Northern Hemisphere (NH) geopotential height distribution through the tropospheric (STRIPES index, pattern correlation coefficient, relative amplitude) and stratospheric pathways (Rossby wave flux, polar vortex, height of the polar cap) and the forecast skill of theses teleconnections. Another set of diagnostics is focused on the MJO teleconnections to the NH storm tracks and surface air temperature distribution. The main objective of the project is to expand the above diagnostics and to unify them into a portable package that can be applied to evaluation of UFS. The diagnostics package will allow a direct comparison of UFS to other S2S operational forecast systems. The diagnostics package will be compliant with METplus software requirements for an easy integration with METplus.
The main objective of this project is to develop a diagnostics package to facilitate evaluation of the MJO teleconnections predicted by S2S models along with the impact of these teleconnections on the S2S forecast skill. The package will include process level diagnostics and performance metrics and will be developed as Python-based toolkit in the portable framework of METplus. PI Stan has an ongoing NOAA funded project (NA20OAR4590316: ENSO-MJO Diagnostics in an energetic framework, 08/01/2020-07/31/2022) in which one of the objectives is to implement an ENSO-MJO diagnostic into METplus. The technical expertise acquired for building the METplus wrapper will be leveraged to the implementation of the newly created teleconnections package into METplus. The METplus framework will provide the visualization platform and make the metrics and diagnostics quickly available to the model developers.
EMC
Needs new POC;
Formerly Manikin
George Mason University
NOAA/NCEP/EMC
University of California San Diego
ETH Zurich
Columbia University
Hebrew University of Jerusalem
University of California Irvine
Stony Brook University
diagnostics package for mjo teleconnections, to expand diagnostics and to unify them into a portable package that can be applied to evaluation of ufs, recognizing the importance of mjo teleconnections for the s2s forecast skill the mjo and teleconnections sub project of the wmo wwrp wcrp s2s prediction project organized most of the mjo teleconnections diagnostics available in the community into a collection of diagnostics suitable for operational forecasts and applied them to all models that publish their s2s forecast into the s2s database stan et al 2021 the suite of diagnostics is designed to evaluate models' ability to predict the mjo teleconnections to the northern hemisphere nh geopotential height distribution through the tropospheric stripes index pattern correlation coefficient relative amplitude and stratospheric pathways rossby wave flux polar vortex height of the polar cap and the forecast skill of theses teleconnections another set of diagnostics is focused on the mjo teleconnections to the nh storm tracks and surface air temperature distribution the main objective of the project is to expand the above diagnostics and to unify them into a portable package that can be applied to evaluation of ufs the diagnostics package will allow a direct comparison of ufs to other s2s operational forecast systems the diagnostics package will be compliant with metplus software requirements for an easy integration with metplus
Environmental Modeling Center (EMC)
George Mason University, NOAA/NCEP/EMC, University of California San Diego, ETH Zurich, Columbia University, Hebrew University of Jerusalem, University of California Irvine, Stony Brook University
To expand diagnostics and to unify them into a portable package that can be applied to evaluation of UFS.
6
Development of a Global Aerosol Reanalysis at NOAA in Support of Climate Monitoring and Prediction
Huang
Testbeds
search_activity
2022
Level 3
To produce a long-term global aerosol reanalysis product at NOAA in support of climate monitoring and prediction.
Bo Huang, Mariusz Pagowski
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting, Parameterization, Satellite & Remote Sensing Technologies
CTB
Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
Atmospheric aerosols become increasingly important in weather and climate systems due to their impact on solar radiation and clouds. As a major contribution to regional air pollution, they affect public health and the economy. Accurate monitoring of global aerosols thus has significant societal and environmental benefits. To improve global aerosol representation, an ensemble-based global aerosol data assimilation (DA) system has been developed at NOA by "optimally" combining information from aerosol observations and model outputs. Due to large aerosol model uncertainty and limited temporal and/or spatial coverage of aerosol observations, continuous global aerosol reanalysis produced through DA plays an essential role in understanding aerosol characteristics and its impact on weather, climate, and air quality. Here, we propose to produce a five-year global aerosol reanalysis for t2018-2022 as an extension of our currently funded project of "Joint NOAA-NASA Development of a Data Assimilation System for Aerosol Reanalysis and Forecasting" (NA18OAR4310281), 2018-2021). Specifically, the global aerosol reanalysis datasets will be produced by (a) using the NOA United Forecast System (UFS)-Aerosols coupled with NASA's second-generation Goddard Chemistry Aerosol Radiation and Transport (GOCART) parameterization that includes nitrate aerosols, (b) leveraging three-dimensional ensemble-variational (3DEnVar) application with the local ensemble transform Kalman filter (LETKF) for ensemble update in the Joint Efforts for Data Assimilation (JEDI) for aerosol assimilation, (c) assimilating aerosol optical depth (AOD) retrievals at 550 nm derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments onboard NOAA's satellites, and (d) verifying against independent ground- and satellite-based aerosol observations and aerosol reanalysis following NCEP/EMC's metrics.
The main goal of this project is to produce a long-term global aerosol reanalysis product at NOAA in support of climate monitoring and prediction. As a result, this project will help fill NOAA's gap in aerosol reanalysis compared to other research and operational centers (e.g., NASA, ECMWF, and JMA). On the other hand, the development and evaluation of multiple aerosol reanalysis products offer an opportunity to better investigate uncertainties associated with aerosol processes, measurements, and modeling. This will help enhance understanding of aerosol characteristics, aerosol modeling, and its impact on weather, climate, and air quality. This will further benefit weather, climate and air pollution prediction. IN addition, this project contributes to NOAA's NGGPS effort towards developing a unified Earth modeling system by advancing the aerosol component to keep up with other components. Specifically, S. Frolov from NOAA/OAR/PSL and S. Penny from CU/CIRES are leading a UFS R2O project that is aiming to produce a 30 year reanalysis of the coupled atmosphere, ocean, ice, and land reanalysis in FY23-24. This coupled reanalysis will be used to calibrate and test GEFS and SFS forecasting systems. Future versions of this reanalysis product will need to incorporate the aerosol assimilation. Therefore, this project will develop the necessary technology and the observational datasets that will enable inclusion of the aerosols into the future versions of the fully coupled UFS reanalysis. Finally, science and techniques resulting from this project will further contribute to aerosol-related development and applications in JEDI at JCSDA, validation of VIIRS AOD retrieval products (e.g. VIIRS AOD retrieval bias and error) at NOAA/NESDIS, and evaluation and enhancement of UFS-Aerosols at NCEP/EMC and NOAA/OAR/GSL.
EMC
Cory Martin (EMC)
CIRES/University of Colorado
NA22OAR4590219
Relevant to All Hazards
Colorado
Testbeds
August 2022 - July 2026
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting, Parameterization, Satellite & Remote Sensing Technologies
development of a global aerosol reanalysis at noaa in support of climate monitoring and prediction, to produce a long term global aerosol reanalysis product at noaa in support of climate monitoring and prediction, atmospheric aerosols become increasingly important in weather and climate systems due to their impact on solar radiation and clouds as a major contribution to regional air pollution they affect public health and the economy accurate monitoring of global aerosols thus has significant societal and environmental benefits to improve global aerosol representation an ensemble based global aerosol data assimilation da system has been developed at noa by "optimally" combining information from aerosol observations and model outputs due to large aerosol model uncertainty and limited temporal and or spatial coverage of aerosol observations continuous global aerosol reanalysis produced through da plays an essential role in understanding aerosol characteristics and its impact on weather climate and air quality here we propose to produce a five year global aerosol reanalysis for t2018 2022 as an extension of our currently funded project of "joint noaa nasa development of a data assimilation system for aerosol reanalysis and forecasting" na18oar4310281 2018 2021 specifically the global aerosol reanalysis datasets will be produced by a using the noa united forecast system ufs aerosols coupled with nasa's second generation goddard chemistry aerosol radiation and transport gocart parameterization that includes nitrate aerosols b leveraging three dimensional ensemble variational 3denvar application with the local ensemble transform kalman filter letkf for ensemble update in the joint efforts for data assimilation jedi for aerosol assimilation c assimilating aerosol optical depth aod retrievals at 550 nm derived from the visible infrared imaging radiometer suite viirs instruments onboard noaa's satellites and d verifying against independent ground and satellite based aerosol observations and aerosol reanalysis following ncep emc's metrics
Environmental Modeling Center (EMC)
CIRES/University of Colorado
To produce a long-term global aerosol reanalysis product at NOAA in support of climate monitoring and prediction.
0
Transitioning NMME-based seasonal predictions of atmospheric river activity into an operational forecast product
Xiang
Testbeds
search_activity
2022
Level 3
To develop an NMME-based seasonal AR guidance for NOAA CPC that would be ready for transition into operations. This product would complement the experimental
subseasonal AR forecast product that CPC recently developed, increasing NOAA's ability to provide seamless forecasts of AR activity across subseasonal to seasonal timescales.
Baoqiang Xiang, Nathaniel Johnson, Daniel Harnos, Laura Ciasto
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting, Verification & Validation
CTB
North American Multi-Model Ensemble (NMME), Seamless System for Prediction and EArth System Research (SPEAR), Unified Forecast System (UFS)
Atmospheric rivers (ARs), narrow corridors of intense moisture transport and heavy precipitation, provide both significant benefits and destructive losses to the western U.S., as they contribute
substantially to the western U.S. water supply but also cause damaging floods. Therefore, a seasonal outlook for AR activity would benefit a wide range of stakeholders, but the NOAA Climate Prediction Center (CPC) currently does not provide seasonal outlooks explicitly tied to the extreme conditions produced by ARs. A recent study led by Collaborator Tseng and supervised by Co-PI Johnson (Tseng et al. 2021) demonstrates that the Seamless System for Prediction and EArth System Research (SPEAR), the latest seasonal prediction model developed at GFDL and a contributor to the North American Multi-Model Ensemble (NMME), produces skillful seasonal (three-month) forecasts of AR activity out to at least 9 months in some regions of the western U.S. Therefore, these research developments indicate a promising opportunity to develop a new operational seasonal AR forecast
product for NOAA CPC.
The proposed project aims to develop an NMME-based seasonal AR guidance for NOAA CPC that would be ready for transition into operations. This product would complement the experimental
subseasonal AR forecast product that CPC recently developed, increasing NOAA's ability to provide seamless forecasts of AR activity across subseasonal to seasonal timescales. The initial focus will be placed on the refinement of the SPEAR-based seasonal AR outlook, with subsequent efforts devoted
to a multi-model NMME seasonal AR product. Forecast performance will be evaluated with established verification metrics in both retrospective forecasts and in a real-time experimental test
environment in coordination with the Climate Testbed. The expected outcomes are (1) a prototype seasonal AR forecast product over the U.S., including probabilistic forecasts of AR activity and associated extreme precipitation, that is utilized internally by CPC forecasters; (2) a comprehensive
diagnosis of the expected forecast performance of this product; and (3) an evaluation of model postprocessing methods, including those of model calibration and optimal model weighting, that
potentially can be applied in other research-to-operations efforts.
The primary project outputs include an NMME-based seasonal AR forecast tool for CPC forecasters, publicly available seasonal AR forecast guidance, journal article publications, and presentations in webinars and conferences, as described below.
CPC
Wassila Thiaw
National Center for Atmospheric Research
NOAA/OAR/GFDL
NOAA/NWS/CPC
NA22OAR4590217
Relevant to All Hazards
Colorado, Maryland
Testbeds
August 2022 - July 2026
Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting, Verification & Validation
transitioning nmme based seasonal predictions of atmospheric river activity into an operational forecast product, to develop an nmme based seasonal ar guidance for noaa cpc that would be ready for transition into operations this product would complement the experimental subseasonal ar forecast product that cpc recently developed increasing noaa's ability to provide seamless forecasts of ar activity across subseasonal to seasonal timescales, atmospheric rivers ars narrow corridors of intense moisture transport and heavy precipitation provide both significant benefits and destructive losses to the western u s as they contribute substantially to the western u s water supply but also cause damaging floods therefore a seasonal outlook for ar activity would benefit a wide range of stakeholders but the noaa climate prediction center cpc currently does not provide seasonal outlooks explicitly tied to the extreme conditions produced by ars a recent study led by collaborator tseng and supervised by co pi johnson tseng et al 2021 demonstrates that the seamless system for prediction and earth system research spear the latest seasonal prediction model developed at gfdl and a contributor to the north american multi model ensemble nmme produces skillful seasonal three month forecasts of ar activity out to at least 9 months in some regions of the western u s therefore these research developments indicate a promising opportunity to develop a new operational seasonal ar forecast product for noaa cpc the proposed project aims to develop an nmme based seasonal ar guidance for noaa cpc that would be ready for transition into operations this product would complement the experimental subseasonal ar forecast product that cpc recently developed increasing noaa's ability to provide seamless forecasts of ar activity across subseasonal to seasonal timescales the initial focus will be placed on the refinement of the spear based seasonal ar outlook with subsequent efforts devoted to a multi model nmme seasonal ar product forecast performance will be evaluated with established verification metrics in both retrospective forecasts and in a real time experimental test environment in coordination with the climate testbed the expected outcomes are 1 a prototype seasonal ar forecast product over the u s including probabilistic forecasts of ar activity and associated extreme precipitation that is utilized internally by cpc forecasters 2 a comprehensive diagnosis of the expected forecast performance of this product and 3 an evaluation of model postprocessing methods including those of model calibration and optimal model weighting that potentially can be applied in other research to operations efforts
Climate Prediction Center (CPC)
National Center for Atmospheric Research, NOAA/OAR/GFDL, NOAA/NWS/CPC
To develop an NMME-based seasonal AR guidance for NOAA CPC that would be ready for transition into operations. This product would complement the experimental subseasonal AR forecast product that CPC recently developed, increasing NOAA's ability
1
Advancing S2S precipitation outlooks through the Weather Regime-Gaussian Mixture Model Framework
Zhang
Testbeds
search_activity
2024
Level 3
To comprehensively test and verify a newly developed method that directly applies dynamically forecasted weather regimes for developing S2S precipitation outlooks.
Wei Zhang
Active
Thu Aug 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Dynamics and Nesting
CTB
Coupled Forecast System (CFSv2), Global Ensemble Forecast System (GEFS)
To extend the above works to more products currently used by NOAA's Climate Prediction Center (CPC) for weeks 3-4, monthly and seasonal precipitation outlooks. Specifically, we will use the dynamical forecast outputs of ECMWF, GEFSv12, ECCC, JMA, and CFSv2 for week 3-4 and monthly outlooks (CPC's end-of-month update). We will also use daily outputs from CFSv2 for seasonal forecasts. The objectives of this project are to 1) Evaluate the prediction skill of weather regimes and precipitation in ECMWF, GEFSv12, ECCC, JMA, and CFSv2; 2) Develop a web-based interface for disseminating precipitation outlook and evaluation; and 3) Test and evaluate the product in CPC's environment on NCEP's developmental servers and finalize readiness to transition to the operational compute farm pending a positive review by NCEP-WPO. Our study will provide useful insights and tools to help operational forecasters improve prediction skills.
This project is expected to provide a web-based tool along with a tested statistical framework for
generating S2S precipitation outlooks at 3-4 weeks, monthly and seasonal scales. In addition, this project will train a graduate student and a postdoc at Utah State University, who will be co-
supervised by the PI and co-PI. The postdoc and the student will visit CPC (hosted by Co-PI Cory Baggett) during the implementation of this project. This project will produce peer-reviewed
articles (1-2; The publication cost will be covered by Utah Agricultural Experiment Station) and
2-4 presentations in national conferences/meetings/workshops. Co-PI Cory Baggett will be responsible for supervising the transition of this product onto CPC's developmental servers for experimental evaluation, verification, and use by the forecasters, benefiting the operational
communities, decision makers, and the public.
Utah State University
NA24OARX459C0014
Water Extremes
Utah
Testbeds
August 2024 - July 2026
Artificial Intelligence (AI) / Machine Learning (ML), Dynamics and Nesting
advancing s2s precipitation outlooks through the weather regime gaussian mixture model framework, to comprehensively test and verify a newly developed method that directly applies dynamically forecasted weather regimes for developing s2s precipitation outlooks, to extend the above works to more products currently used by noaa's climate prediction center cpc for weeks 3 4 monthly and seasonal precipitation outlooks specifically we will use the dynamical forecast outputs of ecmwf gefsv12 eccc jma and cfsv2 for week 3 4 and monthly outlooks cpc's end of month update we will also use daily outputs from cfsv2 for seasonal forecasts the objectives of this project are to 1 evaluate the prediction skill of weather regimes and precipitation in ecmwf gefsv12 eccc jma and cfsv2 2 develop a web based interface for disseminating precipitation outlook and evaluation and 3 test and evaluate the product in cpc's environment on ncep's developmental servers and finalize readiness to transition to the operational compute farm pending a positive review by ncep wpo our study will provide useful insights and tools to help operational forecasters improve prediction skills
Utah State University
To comprehensively test and verify a newly developed method that directly applies dynamically forecasted weather regimes for developing S2S precipitation outlooks.
0
Improving GLobal Ocean Reanalysis (GLORe) to advance CPC ocean monitoring and sea ice outlook
Huang
Testbeds
search_activity
2024
Level 3
To improve the GLobal Ocean Reanalysis (GLORe) system in support of the NCEP Climate Prediction Center (CPC) ocean monitoring and forecasting operation.
Bohua Huang
Active
Thu Aug 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Satellite & Remote Sensing Technologies, Surface Observing Networks
CTB
In this project, we plan to make two well-tested
optimizations to the GLORe system: (1) to widen the data insertion window for subsurface
observations below 50 meters and (2) to implement the incremental analysis update (IAU)
technique. The former will allow more persistent influence of the subsurface observations on the
analysis, which will be most beneficial in areas of sparse measurements. The latter will reduce the
model imbalance induced by data insertion, which will improve the analysis of equatorial currents.
Encouraged from the results of assimilating sea ice concentration (SIC) in GLORe, we plan to
further assimilate the satellite-measured sea ice thickness (SIT), as well as a widely used SIT
analysis product. It is expected that this effort will enhance the GLORe sea ice analysis for both
the monitoring and forecast purposes. New versions of the GLORe analyses will be produced after
the optimizations and the SIT assimilation are tested respectively.
This proposal is responsive to the Climate Testbed competition about "CTB-1:
Improvements to data assimilation (DA) systems that support short-range climate monitoring and
prediction", with a focus on ocean and sea ice data assimilation using JEDI, and about "CTB-2:
Developmental activities to accelerate the Seasonal Forecast System (SFS)" with a focus on its
initialization part. The project outcomes will not only advance CPC ocean monitoring and seasonal
ocean forecasts and sea ice outlooks but also be references for the coupled global data assimilation
system (GDAS) and the seasonal forecast system (SFS) under developments. The collaboration
between CPC and GMU will play a crucial role in carrying out the proposed work successfully
and accelerating the transition of its products/results to operational use at CPC. The team has first-
hand experience in dynamical seasonal ocean forecasting, ocean monitoring and data assimilation.
We also have extensive expertise on statistical and diagnostic techniques that will be applied to
this project. In addition, the CPC co-PI Zhu has regular communications with the EMC Marine-
GDAS team that is actively developing weakly coupled data assimilation and reanalysis for future
SFS, and the communications benefit each other's developments including this proposal.
George Mason University
NA24OARX459C0015
Water Extremes
Virginia
Testbeds
August 2024 - July 2026
Data Assimilation and Ensembles, Satellite & Remote Sensing Technologies, Surface Observing Networks
improving global ocean reanalysis glore to advance cpc ocean monitoring and sea ice outlook, to improve the global ocean reanalysis glore system in support of the ncep climate prediction center cpc ocean monitoring and forecasting operation, in this project we plan to make two well tested optimizations to the glore system 1 to widen the data insertion window for subsurface observations below 50 meters and 2 to implement the incremental analysis update iau technique the former will allow more persistent influence of the subsurface observations on the analysis which will be most beneficial in areas of sparse measurements the latter will reduce the model imbalance induced by data insertion which will improve the analysis of equatorial currents encouraged from the results of assimilating sea ice concentration sic in glore we plan to further assimilate the satellite measured sea ice thickness sit as well as a widely used sit analysis product it is expected that this effort will enhance the glore sea ice analysis for both the monitoring and forecast purposes new versions of the glore analyses will be produced after the optimizations and the sit assimilation are tested respectively
George Mason University
To improve the GLobal Ocean Reanalysis (GLORe) system in support of the NCEP Climate Prediction Center (CPC) ocean monitoring and forecasting operation.
0
Improving Week 3-4 precipitation forecasts by leveraging forecasts-of-opportunity identified via explainable machine learning
Arcodia
Testbeds
search_activity
2025
Level 3
The aim of this work is to extend methodology to improve real-time US week 3-4 precipitation forecasts with uncertainty quantification
Marybeth C Arcodia
Active
Sat Feb 01 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Sun Jan 31 2027 03:00:00 GMT-0500 (Eastern Standard Time)
Accurate and reliable forecasts of precipitation are critical to providing preparation time for water resource management officials, energy and agriculture sectors, and citizens. Recent work by Arcodia, Barnes, et al. (2023) found that neural networks are capable of identifying predictable states of the climate system within climate models that are useful for making confident, accurate subseasonal precipitation predictions in the real world. The aim of the proposed work is to extend this methodology to improve real-time U.S. Week 3-4 precipitation forecasts with uncertainty quantification. We will leverage explainable machine learning approaches to identify forecasts-of-opportunity (e.g. regions and climate states that are more predictable) and large-scale sources of predictability.
The Lead PI and co-PI's research has designed and implemented eXplainable Artificial Intelligence (XAI) approaches for advancing data-driven subseasonal-to-seasonal climate predictions, including assessing prediction uncertainty. Of note is the focus on leveraging machine learning to identify forecasts-of-opportunity and applying XAI techniques to understand the physical sources of predictability. This approach will be applied to produce a real-time forecasting tool to identify when, where, and why a Week 3-4 precipitation forecast can be considered a forecast-of-opportunity, ultimately assessing skill and confidence in forecasts. The goals of the proposed work are threefold: (1) refine and extend the methodology of Arcodia, Barnes, et al. (2023) to quantify skill and evaluate predictability of Week 3-4 U.S. precipitation forecasts by applying machine learning approaches to identify forecasts-of-opportunity in model forecasts, (2) identify regional state-dependence of predictability and its sources across climate models and datasets, and (3) develop a machine learning-based tool to be used by CPC Week 3-4 forecasters to identify forecasts-of-opportunity and climate state-dependent predictability sources in real-time. The activities proposed will be performed by scientists at the Climate Prediction Center (CPC) and Colorado State University (CSU), who have extensive experience working together and transitioning similar empirical tools into CPC operations.
This project aims to improve the NOAA Week 3-4 (experimental) Precipitation Outlooks. Identification of intermittent signals and their sources of predictability in model forecasts will enable NOAA forecasters to better determine areas of enhanced probability of precipitation events and when, where, and why a forecast can be considered a forecast-of-opportunity.
improving week 3 4 precipitation forecasts by leveraging forecasts of opportunity identified via explainable machine learning, the aim of this work is to extend methodology to improve real time us week 3 4 precipitation forecasts with uncertainty quantification, accurate and reliable forecasts of precipitation are critical to providing preparation time for water resource management officials energy and agriculture sectors and citizens recent work by arcodia barnes et al 2023 found that neural networks are capable of identifying predictable states of the climate system within climate models that are useful for making confident accurate subseasonal precipitation predictions in the real world the aim of the proposed work is to extend this methodology to improve real time u s week 3 4 precipitation forecasts with uncertainty quantification we will leverage explainable machine learning approaches to identify forecasts of opportunity e g regions and climate states that are more predictable and large scale sources of predictability the lead pi and co pi's research has designed and implemented explainable artificial intelligence xai approaches for advancing data driven subseasonal to seasonal climate predictions including assessing prediction uncertainty of note is the focus on leveraging machine learning to identify forecasts of opportunity and applying xai techniques to understand the physical sources of predictability this approach will be applied to produce a real time forecasting tool to identify when where and why a week 3 4 precipitation forecast can be considered a forecast of opportunity ultimately assessing skill and confidence in forecasts the goals of the proposed work are threefold 1 refine and extend the methodology of arcodia barnes et al 2023 to quantify skill and evaluate predictability of week 3 4 u s precipitation forecasts by applying machine learning approaches to identify forecasts of opportunity in model forecasts 2 identify regional state dependence of predictability and its sources across climate models and datasets and 3 develop a machine learning based tool to be used by cpc week 3 4 forecasters to identify forecasts of opportunity and climate state dependent predictability sources in real time the activities proposed will be performed by scientists at the climate prediction center cpc and colorado state university csu who have extensive experience working together and transitioning similar empirical tools into cpc operations
Colorado State University
The aim of this work is to extend methodology to improve real-time US week 3-4 precipitation forecasts with uncertainty quantification
0
Developing a Prototype Framework for Model Development, Diagnostics, and Experimental Real-Time Forecast Application Effort for Seasonal Forecast System (SFS)
Zhang
Testbeds
search_activity
2025
Level 3
The project aims to develop and demonstrate the feasibility of a cost-effective framework for implementing the future versions of seasonal forecast systems to (a) replace the current version that was implemented in 2011 (Climate Forecast System version 2 - CFSv2), and (b) provide updates on a periodic basis.
Tao Zhang
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Forecast Skill Metrics, Visualizations
CTB
Climate Forecast System (CFS), Seasonal Forecast System (SFS)
This project aims to create a cost-effective framework for updating and evaluating seasonal forecast systems, specifically to replace the existing Climate Forecast System version 2 (CFSv2) implemented in 2011, and to allow for regular future updates.
The outcomes of this project, by demonstrating a systematic approach for testing and evaluating a seasonal forecast system, will benefit in accelerating the development process of SFS in NOAA.
CPC
University of Maryland
NA25OARX459C0150-T1-01
Relevant to All Hazards
Maryland
Testbeds
August 2025 - July 2028
Data Assimilation and Ensembles, Forecast Skill Metrics, Visualizations
developing a prototype framework for model development diagnostics and experimental real time forecast application effort for seasonal forecast system sfs, the project aims to develop and demonstrate the feasibility of a cost effective framework for implementing the future versions of seasonal forecast systems to a replace the current version that was implemented in 2011 climate forecast system version 2 cfsv2 and b provide updates on a periodic basis, this project aims to create a cost effective framework for updating and evaluating seasonal forecast systems specifically to replace the existing climate forecast system version 2 cfsv2 implemented in 2011 and to allow for regular future updates
Climate Prediction Center (CPC)
University of Maryland
The project aims to develop and demonstrate the feasibility of a cost-effective framework for implementing the future versions of seasonal forecast systems to (a) replace the current version that was implemented in 2011 (Climate Forecast
0
Probability of What? Understanding and Conveying Uncertainty through Probabilistic Hazard Services
Gerard
Testbeds
check_circle
2015
Level 3
This project aimed to build an end-to-end capability for probabilistic forecasting as part of the Forecasting a Continuum of Environmental Threats (FACETs) paradigm. Human factors evaluation and analysis is integrated throughout the process, to create the most effective forecaster tools.
Alan Gerard
Complete
Fri May 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Probabilistic Weather Forecasting, "Social, Behavioral, and Economic Sciences (SBES)"
USWRP R2O
FACETS (Forecasting a Continuum of Environmental Threats) is a holistic, science-based paradigm designed to move the NWS's 1960s-era teletype (yes/no) product-centric methodologies toward a system delivering a continuous stream of high-resolution, probabilistic information about weather hazards information, such as tornadoes, large hail, damaging winds, flash floods, snowstorms, and heavy rainfall. This project will build an end-to-end capability for the NWS to deliver high-frequency, probabilistic weather information to decision makers.
The activities undertaken in this project will improve accuracy of high-impact weather forecasts through the application of calibrated probabilistic guidance for end users. The value (and cost-effectiveness) of this new information will be determined through the evaluation of social science results found in sub-projects C1 and C2. Metrics of improved value, response and cost-savings (perceived and actual) will be established and measured.
OSTI
Hendrik Tolman
NOAA/OAR/NSSL
OWAQ15-R2O-I-1
Relevant to All Hazards
Oklahoma
Testbeds
May 2015 - April 2018
Dynamics and Nesting, Probabilistic Weather Forecasting, "Social Science (SBES)"
probability of what? understanding and conveying uncertainty through probabilistic hazard services, this project aimed to build an end to end capability for probabilistic forecasting as part of the forecasting a continuum of environmental threats facets paradigm human factors evaluation and analysis is integrated throughout the process to create the most effective forecaster tools, facets forecasting a continuum of environmental threats is a holistic science based paradigm designed to move the nws's 1960s era teletype yes no product centric methodologies toward a system delivering a continuous stream of high resolution probabilistic information about weather hazards information such as tornadoes large hail damaging winds flash floods snowstorms and heavy rainfall this project will build an end to end capability for the nws to deliver high frequency probabilistic weather information to decision makers
NWS Office of Science and Technology Integration (OSTI)
NOAA/OAR/NSSL
This project aimed to build an end-to-end capability for probabilistic forecasting as part of the Forecasting a Continuum of Environmental Threats (FACETs) paradigm. Human factors evaluation and analysis is integrated throughout the process, to create
10
Refinement and Evaluation of Automated High-Resolution Ensemble-Based Hazard Detection Guidance Tools for Transition to NWS Operations
Benjamin
Testbeds
check_circle
2015
Level 3
to transition a well-tested system for generation of ensemble post-processed hazard guidance products to operational status within the National Weather Service (product generation within NCEP and dissemination of hazard grids to NWS operational forecasters).
Stan Benjamin, Curtis Alexander, David Novak, Stephen Weygandt, Trevor Alcott, tan Benjamin, Isidora Jankov, Jacob Carley, Josh Hacker, Tara Jensen, Julie Demuth
Complete
Fri May 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting, Verification & Validation
USWRP R2O
High Resolution Ensemble Forecast (HREF), High-Resolution Rapid Refresh (HRRR)
This proposal describes work to expand a prototype ensemble post-processing system leading to an automated feature detection system to provide concise hazard guidance information for extreme precipitation and other significant weather events. This work will lead to the creation of forecaster tools that will facilitate the rapid interrogation of the myriad of available deterministic and ensemble model guidance. Thus, this work addresses FACETs components: 2 (guidance), 3 (forecaster), and 4 (tools) to enable probabilistic forecast services. The work will be performed through a collaborative effort involving scientists from ESRL/GSD and NCEP/EMC, who will refine the existing prototype system and create products, scientists from NCAR, who will apply novel feature-based methods to product verification and potentially product generation, social scientists from NCAR, who will evaluate the communication, interpretation, and use of the products, and NWS meteorologists, who will use and evaluate prototype versions of the guidance tools.
The overarching goal of this work is to transition a well-tested system for generation of ensemble post-processed hazard guidance products to operational status within the National Weather Service (product generation within NCEP and dissemination of hazard grids to NWS operational forecasters). A direct outcome of the project will be improved ensemble hazard guidance tools for operational forecasters that will reduce the ensemble information overload problem and enable a more efficient and accurate characterization of forecast uncertainty. Ultimately, the quality and usefulness of the weather guidance information provided by the NWS to the public will increase. Success in this project will also enable follow-up work to significantly expand the scope of ensemble hazard guidance product generation.
MDL
Michael R. Farrar
NOAA/OAR/GSL
OWAQ15-R2O-I-2
Relevant to All Hazards
Colorado
Testbeds
May 2015 - April 2018
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting, Verification & Validation
refinement and evaluation of automated high resolution ensemble based hazard detection guidance tools for transition to nws operations, to transition a well tested system for generation of ensemble post processed hazard guidance products to operational status within the national weather service product generation within ncep and dissemination of hazard grids to nws operational forecasters, this proposal describes work to expand a prototype ensemble post processing system leading to an automated feature detection system to provide concise hazard guidance information for extreme precipitation and other significant weather events this work will lead to the creation of forecaster tools that will facilitate the rapid interrogation of the myriad of available deterministic and ensemble model guidance thus this work addresses facets components 2 guidance 3 forecaster and 4 tools to enable probabilistic forecast services the work will be performed through a collaborative effort involving scientists from esrl gsd and ncep emc who will refine the existing prototype system and create products scientists from ncar who will apply novel feature based methods to product verification and potentially product generation social scientists from ncar who will evaluate the communication interpretation and use of the products and nws meteorologists who will use and evaluate prototype versions of the guidance tools
NWS Meteorological Development Laboratory (MDL)
NOAA/OAR/GSL
to transition a well-tested system for generation of ensemble post-processed hazard guidance products to operational status within the National Weather Service (product generation within NCEP and dissemination of hazard grids to NWS operational forecasters).
1
Convection-Permitting Ensemble Forecast System for Prediction of Extreme Weather
Romine
Testbeds
check_circle
2015
Level 3
The key objective of this project was to demonstrate the capabilities of a formally designed ensemble analysis and prediction system, particularly during the NOAA Hazardous Weather Testbed for application to high-impact weather prediction.
Fri Aug 31 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Verification & Validation
HWT
The proposed work will: i) operate a convection-permitting ensemble prediction system; ii) provide forecast products to the HWT for evaluation; iii) develop training materials and provide a team of ensemble system experts to the HWT to facilitate forecast system evaluation; iv) generate documentation for the design and implementation of the system; and v) develop novel storm surrogate verification tools and generate verification statistics for traditional meteorological fields, precipitation and storm hazards for comparison with other convection-permitting prediction systems.
Develop novel storm surrogate verification tools and generate verification statistics for traditional meteorological fields, precipitation and storm hazards for comparison with other convection-permitting prediction systems.
EMC
UNK
University Corporation for Atmospheric Research;
Cooperative Institute for Mesoscale Meteorological Studies
NA15OAR4590191
NA15OAR4590189
Severe Weather
Colorado, Oklahoma
Testbeds
September 2015 - August 2018
Data Assimilation and Ensembles, Verification & Validation
convection permitting ensemble forecast system for prediction of extreme weather, the key objective of this project was to demonstrate the capabilities of a formally designed ensemble analysis and prediction system particularly during the noaa hazardous weather testbed for application to high impact weather prediction, the proposed work will i operate a convection permitting ensemble prediction system ii provide forecast products to the hwt for evaluation iii develop training materials and provide a team of ensemble system experts to the hwt to facilitate forecast system evaluation iv generate documentation for the design and implementation of the system and v develop novel storm surrogate verification tools and generate verification statistics for traditional meteorological fields precipitation and storm hazards for comparison with other convection permitting prediction systems
Environmental Modeling Center (EMC)
University Corporation for Atmospheric Research;, Cooperative Institute for Mesoscale Meteorological Studies
The key objective of this project was to demonstrate the capabilities of a formally designed ensemble analysis and prediction system, particularly during the NOAA Hazardous Weather Testbed for application to high-impact weather prediction.
9
Developing and Evaluating GSI-based EnKF-Variational Hybrid Data Assimilation for NCEP NAMRR to Improve Convection-Allowing Hazardouos Weather Forecast
Wang
Testbeds
check_circle
2015
Level 3
The primary goal of this project is to extend GSI-based EnKF/Variational/hybrid system with NMMB and test it within the NAMRR context as well as evaluating the hypothesized advantages of the newly extended hybrid DA system for NAMRR by retrospective cases and by running and evaluating the newly extended system in real time during the HWT Spring Forecasting Experiment (SFE) period.
Sat Aug 31 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Radar
HWT
Gridpoint Statistical Interpolation (GSI)
To extend GSI-based EnKF/Variational/hybrid system with NMMB and test it within the NAMRR context. Such system will have the capability of assimilating multi-scale observations including the direct assimilation of radar data. In addition, the EnKF ensemble analyses for NMMB produced during DA will automatically initialize convection allowing ensemble forecasts. Another primary goal is to evaluate the hypothesized advantages of the newly extended hybrid DA system for NAMRR by retrospective cases and by running and evaluating the newly extended system in real time during the HWT Spring Forecasting Experiment (SFE) period.
Evaluate the hypothesized advantages of the newly extended hybrid DA system for NAMRR by retrospective cases and by running and evaluating the newly extended system in real time during the HWT Spring Forecasting Experiment (SFE) period.
EMC
UNK
University of Oklahoma
NA15OAR4590193
Severe Weather
Oklahoma
Testbeds
September 2015 - August 2019
Data Assimilation and Ensembles, Radar
developing and evaluating gsi based enkf variational hybrid data assimilation for ncep namrr to improve convection allowing hazardouos weather forecast, the primary goal of this project is to extend gsi based enkf variational hybrid system with nmmb and test it within the namrr context as well as evaluating the hypothesized advantages of the newly extended hybrid da system for namrr by retrospective cases and by running and evaluating the newly extended system in real time during the hwt spring forecasting experiment sfe period, to extend gsi based enkf variational hybrid system with nmmb and test it within the namrr context such system will have the capability of assimilating multi scale observations including the direct assimilation of radar data in addition the enkf ensemble analyses for nmmb produced during da will automatically initialize convection allowing ensemble forecasts another primary goal is to evaluate the hypothesized advantages of the newly extended hybrid da system for namrr by retrospective cases and by running and evaluating the newly extended system in real time during the hwt spring forecasting experiment sfe period
Environmental Modeling Center (EMC)
University of Oklahoma
The primary goal of this project is to extend GSI-based EnKF/Variational/hybrid system with NMMB and test it within the NAMRR context as well as evaluating the hypothesized advantages of the newly extended hybrid DA system
5
Improving Initial Conditions and their Perturbations through Ensemble-Based Data Assimilation for Optimized Storm-Scale Ensemble Prediction in Support of HWT Severe Weather Forecasting
Xue
Testbeds
check_circle
2015
Level 3
The goal of this project is to establish and test full EnKF radar DA capabilities that interface with GSI and utilize the operational GSI data streams, and to run the system in realtime for evaluation at the HWT in 2016-2017.
The proposed project will: 1) Establish, test and optimize a 3-km grid spacing CONUS-domain EnKF and En3DVar hybrid DA systems capable of assimilating all operational data sets processed by the RAP/HRRR GSI system plus full-volume WSR-88D radial velocity and reflectivity data. 2) Run the EnKF/En3DVar hybrid system in real-time, providing ensemble initial conditions for the CAPS SSEF system during the HWT Spring Forecast Experiment. The ensemble forecasts will have up to 50 ensemble members and run out to 60 hours, and will focus on severe weather deterministic and probabilistic products of direct interest to the HWT. Forecasts will be produced for 7-10 weeks from mid-April through June in 2016 and 2017, and forecasts products and data will be sent directly to the HWT for evaluation. 3) Directly participate in the HWT experiments, and work with forecasters and researchers in evaluating system performance and designing new forecast products. Joint journal publications will result from this collaboration with HWT scientists.
Evaluate the hypothesized advantages of the newly extended hybrid DA system for NAMRR by retrospective cases and by running and evaluating the newly extended system in real time during the HWT Spring Forecasting Experiment (SFE) period.
EMC
UNK
University of Oklahoma
NA15OAR4590186
Severe Weather
Oklahoma
Testbeds
September 2015 - August 2018
Data Assimilation and Ensembles, Dynamics and Nesting, Probabilistic Weather Forecasting, Radar
improving initial conditions and their perturbations through ensemble based data assimilation for optimized storm scale ensemble prediction in support of hwt severe weather forecasting, the goal of this project is to establish and test full enkf radar da capabilities that interface with gsi and utilize the operational gsi data streams and to run the system in realtime for evaluation at the hwt in 2016 2017, the proposed project will 1 establish test and optimize a 3 km grid spacing conus domain enkf and en3dvar hybrid da systems capable of assimilating all operational data sets processed by the rap hrrr gsi system plus full volume wsr 88d radial velocity and reflectivity data 2 run the enkf en3dvar hybrid system in real time providing ensemble initial conditions for the caps ssef system during the hwt spring forecast experiment the ensemble forecasts will have up to 50 ensemble members and run out to 60 hours and will focus on severe weather deterministic and probabilistic products of direct interest to the hwt forecasts will be produced for 7 10 weeks from mid april through june in 2016 and 2017 and forecasts products and data will be sent directly to the hwt for evaluation 3 directly participate in the hwt experiments and work with forecasters and researchers in evaluating system performance and designing new forecast products joint journal publications will result from this collaboration with hwt scientists
Environmental Modeling Center (EMC)
University of Oklahoma
The goal of this project is to establish and test full EnKF radar DA capabilities that interface with GSI and utilize the operational GSI data streams, and to run the system in realtime for evaluation
6
Information Extraction and Verification of Convection-Allowing Models for Severe Hail Forecasting
Jirak
Testbeds
check_circle
2015
Level 3
The goal of this project is to improve severe hail forecasting through targeted information extraction from CAMs and objective verification using radar-derived hail size estimates.
Fri Aug 31 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Radar, Verification & Validation
HWT
Convection Allowing Models (CAM)
This proposal aims to improve severe hail forecasting through targeted information extraction from CAMs and objective verification using radar-derived hail size estimates. Verification is a key first step in evaluating the current skill of existing forecasts and allowing for an objective measure of forecast improvement. Verification techniques and methods that have been explored and tested in the HWT will be refined to establish scale-appropriate metrics for evaluating CAM forecasts relevant to severe hail occurrence.
Improve severe hail forecasting through targeted information extraction from CAMs and objective verification using radar-derived hail size estimates.
EMC
UNK
NOAA/NWS/SPC
NA15OAR4590192
Severe Weather, Winter Weather
Oklahoma
Testbeds
September 2015 - August 2018
Radar, Verification & Validation
information extraction and verification of convection allowing models for severe hail forecasting, the goal of this project is to improve severe hail forecasting through targeted information extraction from cams and objective verification using radar derived hail size estimates, this proposal aims to improve severe hail forecasting through targeted information extraction from cams and objective verification using radar derived hail size estimates verification is a key first step in evaluating the current skill of existing forecasts and allowing for an objective measure of forecast improvement verification techniques and methods that have been explored and tested in the hwt will be refined to establish scale appropriate metrics for evaluating cam forecasts relevant to severe hail occurrence
Environmental Modeling Center (EMC)
NOAA/NWS/SPC
The goal of this project is to improve severe hail forecasting through targeted information extraction from CAMs and objective verification using radar-derived hail size estimates.
1
Integration and Evaluation of ProbSevere within the Probabilistic Hazard Information (PHI) Tool in the Hazardous Weather Testbed
Calhoun
Testbeds
check_circle
2015
Level 3
The goal of this project is to integrate the NOAA/CIMSS ProbSevere model, which utilizes satellite, multi-radar, lightning (future capability) and environmental data to determine the probability that a given storm will produce severe weather in the next 60 minutes, into the National Severe Storms Laboratory (NSSL) Probabilistic Hazard Information (PHI) tool for HWT evaluation.
Kristin Calhoun, Wayne Feltz, Lans Rothfusz, Michael Pavolonis
To integrate the NOAA/CIMSS ProbSevere model, which utilizes satellite, multi-radar, lightning (future capability) and environmental data to determine the probability that a given storm will produce severe weather in the next 60 minutes, into the National Severe Storms Laboratory (NSSL) Probabilistic Hazard Information (PHI) tool for HWT evaluation.
Evolve the National Weather Service (NWS) from product-centric watches and warnings to PHI
NSSL
UNK
University of Oklahoma
CIMSS/University of Wisconsin-Madison
NOAA/NSSL
NOAA/NESDIS
integration and evaluation of probsevere within the probabilistic hazard information phi tool in the hazardous weather testbed, the goal of this project is to integrate the noaa cimss probsevere model which utilizes satellite multi radar lightning future capability and environmental data to determine the probability that a given storm will produce severe weather in the next 60 minutes into the national severe storms laboratory nssl probabilistic hazard information phi tool for hwt evaluation, to integrate the noaa cimss probsevere model which utilizes satellite multi radar lightning future capability and environmental data to determine the probability that a given storm will produce severe weather in the next 60 minutes into the national severe storms laboratory nssl probabilistic hazard information phi tool for hwt evaluation
University of Oklahoma, CIMSS/University of Wisconsin-Madison, NOAA/NSSL, NOAA/NESDIS
The goal of this project is to integrate the NOAA/CIMSS ProbSevere model, which utilizes satellite, multi-radar, lightning (future capability) and environmental data to determine the probability that a given storm will produce severe weather in
5
Application and Evaluation of the Finite-Volume Cubed Sphere Model (FV3) at Convection-Allowing Scales for Severe Weather Forecasting
Clark
Testbeds
check_circle
2017
Level 3
The goal of this project is to assess the performance and accelerate the development of FV3 at convection-allowing scales. To accomplish these goals, the National Severe Storms Laboratory will run a global configuration of FV3, which includes grid refinement to 3-km over the CONUS. These 0000 UTC initialized runs will be run daily in real-time and use GFS analyses for initial conditions with forecast lengths of 36 h (Day 1 period). After adequate testing, the forecasts will be extended to 60 h (Day 2 period). These runs will act as a starting point for a more permanent experimental modeling framework to provide FV3-based storm-scale guidance to the Storm Prediction Center (SPC), serve as a testing ground for new FV3-based guidance products, and lead to a database that can be used for long-term FV3 forecast verification.
Adam Clark
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jun 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Model & Forecast Guidance, Verification & Validation
USWRP
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Forecast System (GFS), Unified Forecast System (UFS)
The goal of this project is to assess the performance and accelerate the development of FV3 at convection-allowing scales. To accomplish these goals, the National Severe Storms Laboratory will run a global configuration of FV3, which includes grid refinement to 3-km over the CONUS. These 0000 UTC initialized runs will be run daily in real-time and use GFS analyses for initial conditions with forecast lengths of 36 h (Day 1 period). After adequate testing, the forecasts will be extended to 60 h (Day 2 period). These runs will act as a starting point for a more permanent experimental modeling framework to provide FV3-based storm-scale guidance to the Storm Prediction Center (SPC), serve as a testing ground for new FV3-based guidance products, and lead to a database that can be used for long-term FV3 forecast verification.
Develop a stand-alone regional, convection-allowing version of FV3 suitable for operational implementation in HREF and Warn-on-Forecast, or provide scientific justification for an alternative course of action.
SPC
NOAA/OAR/NSSL
CIMMS/University of Oklahoma
NOAA/NWS/SPC
UNK
Severe Weather
Oklahoma
Testbeds
July 2017 - June 2018
Dynamics and Nesting, Model & Forecast Guidance, Verification & Validation
application and evaluation of the finite volume cubed sphere model fv3 at convection allowing scales for severe weather forecasting, the goal of this project is to assess the performance and accelerate the development of fv3 at convection allowing scales to accomplish these goals the national severe storms laboratory will run a global configuration of fv3 which includes grid refinement to 3 km over the conus these 0000 utc initialized runs will be run daily in real time and use gfs analyses for initial conditions with forecast lengths of 36 h day 1 period after adequate testing the forecasts will be extended to 60 h day 2 period these runs will act as a starting point for a more permanent experimental modeling framework to provide fv3 based storm scale guidance to the storm prediction center spc serve as a testing ground for new fv3 based guidance products and lead to a database that can be used for long term fv3 forecast verification, the goal of this project is to assess the performance and accelerate the development of fv3 at convection allowing scales to accomplish these goals the national severe storms laboratory will run a global configuration of fv3 which includes grid refinement to 3 km over the conus these 0000 utc initialized runs will be run daily in real time and use gfs analyses for initial conditions with forecast lengths of 36 h day 1 period after adequate testing the forecasts will be extended to 60 h day 2 period these runs will act as a starting point for a more permanent experimental modeling framework to provide fv3 based storm scale guidance to the storm prediction center spc serve as a testing ground for new fv3 based guidance products and lead to a database that can be used for long term fv3 forecast verification
Storm Prediction Center (SPC)
NOAA/OAR/NSSL, CIMMS/University of Oklahoma, NOAA/NWS/SPC
The goal of this project is to assess the performance and accelerate the development of FV3 at convection-allowing scales. To accomplish these goals, the National Severe Storms Laboratory will run a global configuration of FV3,
0
Demonstration of a Rapid Update Convection-Permitting Ensemble Forecast System to Improve Hazardous Weather Prediction
Romine
Testbeds
check_circle
2017
Level 3
The proposed work will contribute toward improved short-term guidance on the predictability of high-impact phenomena by developing specific guidance for advancing the High-Resolution Rapid Refresh (HRRR) analysis and forecast system toward an ensemble analysis and prediction system.
Glen Romine
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting
Demonstrate the value of probabilistic hazard guidance drawn from CPE forecasts. A continuously cycled ensemble analysis on a convection permitting grid will be used to initialize CPE forecasts at frequent intervals.
Probabilistic forecast products to provide useful probabilistic guidance, particularly in the 6-12 h forecast window for severe storms that will lead to improved decision-making.
EMC
Jacob Carley
National Center for Atmospheric Research
NA17OAR4590114
Severe Weather
Colorado
Testbeds
July 2017 - June 2020
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting
demonstration of a rapid update convection permitting ensemble forecast system to improve hazardous weather prediction, the proposed work will contribute toward improved short term guidance on the predictability of high impact phenomena by developing specific guidance for advancing the high resolution rapid refresh hrrr analysis and forecast system toward an ensemble analysis and prediction system, demonstrate the value of probabilistic hazard guidance drawn from cpe forecasts a continuously cycled ensemble analysis on a convection permitting grid will be used to initialize cpe forecasts at frequent intervals
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
The proposed work will contribute toward improved short-term guidance on the predictability of high-impact phenomena by developing specific guidance for advancing the High-Resolution Rapid Refresh (HRRR) analysis and forecast system toward an ensemble analysis and
5
Integration of Multi-Radar Multi-Sensor Azimuthal Shear into a CONUS Conditional Probability of Tornado Intensity Product in the Hazardous Weather Testbed
Mahalik
Testbeds
check_circle
2017
Level 3
This product's goal is to help enhance forecaster ability to discriminate in real-time severe storms that have a higher likelihood to produce more intense, damaging tornadoes from those that do not by transitioning the Conditional Probability of Tornado Intensity (CPTI) product.
Matthew Mahalik, Brandon Smith, Alan Gerard
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting, Radar
HWT
Complete development of a real-time conditional probability of tornado intensity (CPTI) gridded product and evaluate it within the Hazardous Weather Testbed (HWT).
Produce a fine-tuned, storm-scale CPTI gridded product with proposed use within the MRMS suite of products available at NWS Weather Forecast Offices nationwide.
NCO
Eric Howieson
CIMMS/University of Oklahoma
NA17OAR4590115
Severe Weather
Oklahoma
Testbeds
July 2017 - June 2020
Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting, Radar
integration of multi radar multi sensor azimuthal shear into a conus conditional probability of tornado intensity product in the hazardous weather testbed, this product's goal is to help enhance forecaster ability to discriminate in real time severe storms that have a higher likelihood to produce more intense damaging tornadoes from those that do not by transitioning the conditional probability of tornado intensity cpti product, complete development of a real time conditional probability of tornado intensity cpti gridded product and evaluate it within the hazardous weather testbed hwt
NCEP Central Operations (NCO)
CIMMS/University of Oklahoma
This product's goal is to help enhance forecaster ability to discriminate in real-time severe storms that have a higher likelihood to produce more intense, damaging tornadoes from those that do not by transitioning the Conditional
The primary goal of this project is to advance National Weather Service convective scale ensemble design through implementing and evaluating the multiscale IC perturbation methods in the near operational settings where operational model and DA system will be used.
Xuguang Wang
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles
HWT
Gridpoint Statistical Interpolation (GSI)
To advance NWS (National Weather Service) convective scale ensemble design through implementing and evaluating the multiscale IC perturbation methods in the near operational settings where operational model and data assimilation (DA) system will be used. The proposed work will be built upon the GSI based hybrid DA and ensemble forecast system which was further developed for convective scale weather analysis and forecast by the PIs.
Evaluate the impact of multiscale IC perturbations relative to downscaled SREF and GEFS perturbations on convection allowing ensemble forecasts.
EMC
Jack Kain
CIMMS/University of Oklahoma
NA17OAR4590116
Severe Weather
Oklahoma
Testbeds
July 2017 - June 2021
Data Assimilation and Ensembles
improving nws convection allowing hazardous weather ensemble forecasts through optimizing multi scale initial condition ic perturbations, the primary goal of this project is to advance national weather service convective scale ensemble design through implementing and evaluating the multiscale ic perturbation methods in the near operational settings where operational model and da system will be used, to advance nws national weather service convective scale ensemble design through implementing and evaluating the multiscale ic perturbation methods in the near operational settings where operational model and data assimilation da system will be used the proposed work will be built upon the gsi based hybrid da and ensemble forecast system which was further developed for convective scale weather analysis and forecast by the pis
Environmental Modeling Center (EMC)
CIMMS/University of Oklahoma
The primary goal of this project is to advance National Weather Service convective scale ensemble design through implementing and evaluating the multiscale IC perturbation methods in the near operational settings where operational model and DA
6
Evaluating stochastic physics approaches within select Convection Allowing Model (CAM) members included in the Community Leveraged Unified Ensemble (CLUE) during the Hazardous Weather Testbed (HWT) Spring Experiment
Wolff
Testbeds
check_circle
2017
Level 3
This project's goal is targeted at contributing a set of CAM configurations, based on stochastic perturbations, and quantifying the effects of skill within the context of the CLUE to inform decisions-makers on the design of future operational CAM ensembles.
Jamie Wolff, Isidora Jankov
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles
HWT
Convection Allowing Models (CAM)
This proposed project is targeted at contributing a set of CAM configurations, based on stochastic perturbations, and quantifying the effects of skill within the context of the CLUE. Following each HWT SFE during the project, a comprehensive objective evaluation utilizing a meaningful subset of CLUE members from the full 5-week time period will be conducted to investigate stochastic ensemble performance.
Evidence-based results from this project will inform decisions-makers on the design of future operational CAM ensembles.
EMC
Jack Kain
National Center for Atmospheric Research;
Colorado State University-Cooperative Institute for Research in the Atmosphere
NA17OAR4590117
NA17OAR4590118
Severe Weather
Colorado
Testbeds
July 2017 - June 2020
Atmospheric Physics, Data Assimilation and Ensembles
evaluating stochastic physics approaches within select convection allowing model cam members included in the community leveraged unified ensemble clue during the hazardous weather testbed hwt spring experiment, this project's goal is targeted at contributing a set of cam configurations based on stochastic perturbations and quantifying the effects of skill within the context of the clue to inform decisions makers on the design of future operational cam ensembles, this proposed project is targeted at contributing a set of cam configurations based on stochastic perturbations and quantifying the effects of skill within the context of the clue following each hwt sfe during the project a comprehensive objective evaluation utilizing a meaningful subset of clue members from the full 5 week time period will be conducted to investigate stochastic ensemble performance
Environmental Modeling Center (EMC)
National Center for Atmospheric Research;, Colorado State University-Cooperative Institute for Research in the Atmosphere
This project's goal is targeted at contributing a set of CAM configurations, based on stochastic perturbations, and quantifying the effects of skill within the context of the CLUE to inform decisions-makers on the design of
0
Developing an objective evaluation scorecard for storm scale prediction
Jensen
Testbeds
check_circle
2017
Level 3
The overall goal of this project is to improve predictions from convection allowing models (CAM) and develop scorecards to help assess which models provide skill. Specifically, this project will work to (a) identify accepted measures that should be integrated into METplus, (b) explore the applicability to CAMs of a new feature-relative methodology being transitioned to HWT this year (c) explore the use of forecast consistency measures that are being added to METplus in winter 2017, (d) develop a flexible scorecard to allow the community to define a useful one for CAM systems and (e) work with HWT to evaluate CAMs retrospectively to demonstrate the usefulness of the metrics.
Tara Jensen
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Verification & Validation
HWT
Convection Allowing Models (CAM), Model Evaluation Tools Plus (METPlus)
The research team will engage the operational and research communities to identify useful measures to evaluate CAMs and ensembles and add them to the unified MET+ framework for inclusion in a scorecard. Specifically, the team will work iteratively with the HWT, primarily with the Forecast program, but also with the Warning program where appropriate.
Identify accepted measures that should be integrated into MET+, explore the applicability to CAMs of a new feature-relative methodology being transitioned to HMT this year, explore the use of forecast consistency measures that are being added to MET+ in winter 2017, develop a flexible scorecard to allow the community to define a useful one for CAM systems and work with HWT to evaluate CAMs retrospectively to demonstrate the usefulness of the metrics.
EMC
Geoffrey Manikin
National Center for Atmospheric Research
NA17OAR4590119
Severe Weather
Colorado
Testbeds
July 2017 - June 2020
Data Assimilation and Ensembles, Verification & Validation
developing an objective evaluation scorecard for storm scale prediction, the overall goal of this project is to improve predictions from convection allowing models cam and develop scorecards to help assess which models provide skill specifically this project will work to a identify accepted measures that should be integrated into metplus b explore the applicability to cams of a new feature relative methodology being transitioned to hwt this year c explore the use of forecast consistency measures that are being added to metplus in winter 2017 d develop a flexible scorecard to allow the community to define a useful one for cam systems and e work with hwt to evaluate cams retrospectively to demonstrate the usefulness of the metrics, the research team will engage the operational and research communities to identify useful measures to evaluate cams and ensembles and add them to the unified met+ framework for inclusion in a scorecard specifically the team will work iteratively with the hwt primarily with the forecast program but also with the warning program where appropriate
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
The overall goal of this project is to improve predictions from convection allowing models (CAM) and develop scorecards to help assess which models provide skill. Specifically, this project will work to (a) identify accepted measures
2
Development and Optimization of Radar-Assimilating Ensemble-Based Data Assimilation for Storm-Scale Ensemble Prediction in Support of HWT Spring Experiments
Xue
Testbeds
check_circle
2017
Level 3
The goal of this project is to extend collaborations with SPC, NSSL, and EMC to test and improve 3DVar, EnKF, and En3DVar hybrid radar DA capabilities within the operational GSI framework that utilize the operational GSI data streams, and to run the system in realtime for evaluation
Ming Xue
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Jun 30 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
To extend the collaborations with SPC, NSSL and EMC, to further develop GSI-based EnKF and ensemble-variational (EnVar) DA capabilities to effectively and efficiently assimilate the full operational RAP/HRRR data streams plus WSR-88D radial velocity and reflectivity data, and to run the enhanced systems in realtime for evaluation at the HWT in 2018-2019.
Enable the use of much more advanced DA and ensemble IC generation techniques, and will help accelerate the development and adoption of GSIbased ensemble DA capabilities at the storm-scale including radar data for operational RAP, HRRR, and the planned HRRRE and FV3 at NCEP.
EMC
Jacob Carley
University of Oklahoma
NA17OAR4590186
Severe Weather
Oklahoma
Testbeds
July 2017 - June 2019
Data Assimilation and Ensembles, Radar
development and optimization of radar assimilating ensemble based data assimilation for storm scale ensemble prediction in support of hwt spring experiments, the goal of this project is to extend collaborations with spc nssl and emc to test and improve 3dvar enkf and en3dvar hybrid radar da capabilities within the operational gsi framework that utilize the operational gsi data streams and to run the system in realtime for evaluation, to extend the collaborations with spc nssl and emc to further develop gsi based enkf and ensemble variational envar da capabilities to effectively and efficiently assimilate the full operational rap hrrr data streams plus wsr 88d radial velocity and reflectivity data and to run the enhanced systems in realtime for evaluation at the hwt in 2018 2019
Environmental Modeling Center (EMC)
University of Oklahoma
The goal of this project is to extend collaborations with SPC, NSSL, and EMC to test and improve 3DVar, EnKF, and En3DVar hybrid radar DA capabilities within the operational GSI framework that utilize the operational
3
Improving the design and utility to severe weather forecasters of convection permitting ensembles through application of a probabilistic object-based post-processing and verification technique
Johnson
Testbeds
check_circle
2017
Level 3
The goal of the project is to implement the object-based techniques in a probabilistic context, simplify the techniques and determine robust and optimal parameter values, use the technique to diagnose and improve convection-permitting ensembles, and to demonstrate the impact on the severe weather forecast process that can be achieved by utilizing the further-developed techniques.
Aaron Johnson
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Post-processing, Probabilistic Weather Forecasting, Verification & Validation
HWT
Unified Forecast System (UFS)
To optimize the object-based postprocessing software and methodology, which has already been demonstrated in several research studies, for user-friendly operational use at the SPC and Norman WFO. This will be accomplished by extending the object based verification tools to include multi-variable attributes and to use appropriate object matching functions for the specific application of severe weather forecasting.
improve the quality and/or efficiency of operational severe weather forecasters
EMC
Israel Jirak
University of Oklahoma
NA17OAR4590187
Severe Weather
Oklahoma
Testbeds
July 2017 - June 2021
Data Assimilation and Ensembles, Post-processing, Probabilistic Weather Forecasting, Verification & Validation
improving the design and utility to severe weather forecasters of convection permitting ensembles through application of a probabilistic object based post processing and verification technique, the goal of the project is to implement the object based techniques in a probabilistic context simplify the techniques and determine robust and optimal parameter values use the technique to diagnose and improve convection permitting ensembles and to demonstrate the impact on the severe weather forecast process that can be achieved by utilizing the further developed techniques, to optimize the object based postprocessing software and methodology which has already been demonstrated in several research studies for user friendly operational use at the spc and norman wfo this will be accomplished by extending the object based verification tools to include multi variable attributes and to use appropriate object matching functions for the specific application of severe weather forecasting
Environmental Modeling Center (EMC)
University of Oklahoma
The goal of the project is to implement the object-based techniques in a probabilistic context, simplify the techniques and determine robust and optimal parameter values, use the technique to diagnose and improve convection-permitting ensembles, and
3
Using convective mode information for hazard prediction with convection-allowing models
Sobash
Testbeds
check_circle
2019
Level 3
The goal of this project is to improve the prediction of convective mode using two objective algorithms, along with associated convective weather hazards, using convection-allowing models (CAMs).
This project looks to improve convective hazard guidence using convective mode information derived from convection-allowing models. It explores the use of machine learning
Greater skilled and more reliable probabilistic thunderstorm and severe hazard threat guidance
using convective mode information for hazard prediction with convection allowing models, the goal of this project is to improve the prediction of convective mode using two objective algorithms along with associated convective weather hazards using convection allowing models cams, this project looks to improve convective hazard guidence using convective mode information derived from convection allowing models it explores the use of machine learning
Storm Prediction Center (SPC)
National Center for Atmospheric Research
The goal of this project is to improve the prediction of convective mode using two objective algorithms, along with associated convective weather hazards, using convection-allowing models (CAMs).
6
Implementation and testing of stochastic perturbations within a stand-alone regional (SAR) FV3 ensemble using the Common Community Physics Package (CCPP)
Beck
Testbeds
check_circle
2019
Level 3
The goal of this project is to adapt the tendency-based stochastic physics options used in the Global Ensemble Forecast System (GEFS), and the global FV3, including Stochastic Kinetic Energy Backscatter (SKEB), Stochastically Perturbed Parameterization Tendencies (SPPT), and Specific HUMidity (SHUM), for use in the Limited Area Model (LAM) version of the FV3 (the FV3-LAM) as well as to implement the Stochastically Perturbed Parameterizations (SPP) used in the WRF-ARW, to the FV3 code base.
Fri Jun 30 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Parameterization
HWT
Common Community Physics Package (CCPP), Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS), Weather Research and Forecasting Model (WRF)
This project will test and evaluate stochastic physics in the stand-alone regional (SAR) FV3, with an emphasis on convective-allowing resolutions
Less resource intensive model for predicting convective-scale phenomena, such as thunderstorms, at high resolutions.
EMC
Jacob Carley
CIRA/Colorado State University
National Center for Atmospheric Research
NA19OAR4590130
NA19OAR4590131
Severe Weather
Colorado
Testbeds
July 2019 - June 2023
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Parameterization
implementation and testing of stochastic perturbations within a stand alone regional sar fv3 ensemble using the common community physics package ccpp, the goal of this project is to adapt the tendency based stochastic physics options used in the global ensemble forecast system gefs and the global fv3 including stochastic kinetic energy backscatter skeb stochastically perturbed parameterization tendencies sppt and specific humidity shum for use in the limited area model lam version of the fv3 the fv3 lam as well as to implement the stochastically perturbed parameterizations spp used in the wrf arw to the fv3 code base, this project will test and evaluate stochastic physics in the stand alone regional sar fv3 with an emphasis on convective allowing resolutions
Environmental Modeling Center (EMC)
CIRA/Colorado State University, National Center for Atmospheric Research
The goal of this project is to adapt the tendency-based stochastic physics options used in the Global Ensemble Forecast System (GEFS), and the global FV3, including Stochastic Kinetic Energy Backscatter (SKEB), Stochastically Perturbed Parameterization Tendencies
0
Improved Diagnosis of Severe Wind Occurrence through Machine Learning
Gallus
Testbeds
check_circle
2019
Level 3
The goal of this project is to create a machine-learning (ML) tool, developed using sophisticated clustering, spatial statistics, and ML methodologies, that will assign probabilities to each thunderstorm wind damage report where the winds caused damage of severe strength (50 knots or greater).
The goal of this project is to create a machine-learning based tool/algorithm that could be applied to thunderstorm wind damage reports to provide a probability that the damage was truly due to winds of severe or significant severe strength.
Improving verification of severe convective winds to allow improved forecasting
improved diagnosis of severe wind occurrence through machine learning, the goal of this project is to create a machine learning ml tool developed using sophisticated clustering spatial statistics and ml methodologies that will assign probabilities to each thunderstorm wind damage report where the winds caused damage of severe strength 50 knots or greater, the goal of this project is to create a machine learning based tool algorithm that could be applied to thunderstorm wind damage reports to provide a probability that the damage was truly due to winds of severe or significant severe strength
Storm Prediction Center (SPC)
Iowa State University
The goal of this project is to create a machine-learning (ML) tool, developed using sophisticated clustering, spatial statistics, and ML methodologies, that will assign probabilities to each thunderstorm wind damage report where the winds caused
1
Improving National Weather Service Convection Allowing Hazardous Weather Prediction by Using a Cost-Effective Large Background Ensemble in a Regional FV3 Hybrid EnVar Data Assimilation System
Wang
Testbeds
check_circle
2019
Level 3
The goal of this project is to implement and optimize the cost-effective valid time shifting (VTS) method to further advance the hybrid EnVar for convective scale data assimilation (DA) and predictionand transition this new VTS capability into the next generation convective scale DA (JEDI) and modeling system (FV3-SAR) for future operational implementation.
Sun Jun 30 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles
HWT
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Unified Forecast System (UFS)
Implement and optimize the cost-effective Valid Time Shifting method to further advance the hybrid EnVar for convective scale data assimilation and prediction, and transition this new VTS capability into the next generation convective scale prediction system.
Improved forecasts of convective scale weather phenomena, such as thunderstorms.
EMC
Jacob Carley
University of Oklahoma
NA19OAR4590138
Severe Weather
Oklahoma
Testbeds
July 2019 - June 2024
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles
improving national weather service convection allowing hazardous weather prediction by using a cost effective large background ensemble in a regional fv3 hybrid envar data assimilation system, the goal of this project is to implement and optimize the cost effective valid time shifting vts method to further advance the hybrid envar for convective scale data assimilation da and predictionand transition this new vts capability into the next generation convective scale da jedi and modeling system fv3 sar for future operational implementation, implement and optimize the cost effective valid time shifting method to further advance the hybrid envar for convective scale data assimilation and prediction and transition this new vts capability into the next generation convective scale prediction system
Environmental Modeling Center (EMC)
University of Oklahoma
The goal of this project is to implement and optimize the cost-effective valid time shifting (VTS) method to further advance the hybrid EnVar for convective scale data assimilation (DA) and predictionand transition this new VTS
5
Assessing Increased Vulnerability Knowledge and High Temporal Resolution Guidance on Forecaster and Emergency Manager Decision Making
LaDue
Testbeds
check_circle
2019
Level 3
The goal of this project was to assess NWS WFO forecasters' and end users' abilities to assess, understand, and respond effectively to short term convective forecasts and the Brief Vulnerability Overview Tool.
Sun Jun 30 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Model & Forecast Guidance, Decision-Making, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
HWT
This three-year project applies and integrates relevant social and behavioral science methodologies to assesses WFO forecasters' and end-users' abilities to assess, understand, and respond effectively to very short-term through Day 1 forecasts for convective weather hazards as well as a tool that will enhance their awareness of vulnerabilities within their County Warning Area (CWA).
The results of this research project will benefit the general public by permitting NWS forecasters and NWS deep core partners (especially Emergency Managers) to improve communication and messaging - by allowing forecasters to account for their partners' vulnerability concerns earlier in the process of creating briefings, messaging, etc. - and to provide better lead time and more temporal and spatial hi-resolution guidance for forecasters.
SPC
Chris Karstens
University of Oklahoma
NA19OAR4590139
Severe Weather
Oklahoma
Testbeds
July 2019 - June 2024
Dynamics and Nesting, Model & Forecast Guidance, Decision-Making, Social Vulnerability, "Social Science (SBES)"
assessing increased vulnerability knowledge and high temporal resolution guidance on forecaster and emergency manager decision making, the goal of this project was to assess nws wfo forecasters' and end users' abilities to assess understand and respond effectively to short term convective forecasts and the brief vulnerability overview tool, this three year project applies and integrates relevant social and behavioral science methodologies to assesses wfo forecasters' and end users' abilities to assess understand and respond effectively to very short term through day 1 forecasts for convective weather hazards as well as a tool that will enhance their awareness of vulnerabilities within their county warning area cwa
Storm Prediction Center (SPC)
University of Oklahoma
The goal of this project was to assess NWS WFO forecasters' and end users' abilities to assess, understand, and respond effectively to short term convective forecasts and the Brief Vulnerability Overview Tool.
2
Informing UFS-based Rapid Refresh Forecast System (RRFS) ensemble development through evaluation of analysis uncertainty representation methods
Vargas
Testbeds
search_activity
2022
Level 3
The goal of this project is to advance the development of RRFS-based convection-permitting ensemble forecasts through improved forecast spread, skill, and probabilistic forecasts.
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Probabilistic Weather Forecasting, Verification & Validation
HWT
Finite-Volume Cubed-Sphere Dynamical Core (FV3), High Resolution Ensemble Forecast (HREF), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
With the fast-approaching move toward the Unified Forecast System
(UFS)-based Rapid Refresh Forecast System (RRFS), NOAA will soon be replacing a number of
operational deterministic and ensemble regional modeling systems. Based on the FV3 dynamical
core, the RRFS will need to provide sufficient spread to ensure forecast quality equal to or beyond
the performance of the High-Resolution Ensemble Forecast (HREF) system. To meet this goal,
efforts are underway within NOAA and partner institutions to account for model physics
uncertainty through stochastic physics. Yet, analysis uncertainty representation, which is arguably
just as important as ensemble spread, deserves more attention and further investigation to help
improve the next generation of convection-allowing ensembles and the RRFS in particular, given
the lack of initial condition (IC) uncertainty testing with the FV3.
Improved forecasts of convective-scale weather phenomena, such as thunderstorms, through the development of a high-resolution convection-allowing ensemble model system
EMC
Shun Liu (EMC)
CIRA/Colorado State University
National Center for Atmospheric Research
University of Oklahoma
NA22OAR4590526
Severe Weather
Colorado, Oklahoma
Testbeds
August 2022 - July 2026
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Probabilistic Weather Forecasting, Verification & Validation
informing ufs based rapid refresh forecast system rrfs ensemble development through evaluation of analysis uncertainty representation methods, the goal of this project is to advance the development of rrfs based convection permitting ensemble forecasts through improved forecast spread skill and probabilistic forecasts, with the fast approaching move toward the unified forecast system ufs based rapid refresh forecast system rrfs noaa will soon be replacing a number of operational deterministic and ensemble regional modeling systems based on the fv3 dynamical core the rrfs will need to provide sufficient spread to ensure forecast quality equal to or beyond the performance of the high resolution ensemble forecast href system to meet this goal efforts are underway within noaa and partner institutions to account for model physics uncertainty through stochastic physics yet analysis uncertainty representation which is arguably just as important as ensemble spread deserves more attention and further investigation to help improve the next generation of convection allowing ensembles and the rrfs in particular given the lack of initial condition ic uncertainty testing with the fv3
Environmental Modeling Center (EMC)
CIRA/Colorado State University, National Center for Atmospheric Research, University of Oklahoma
The goal of this project is to advance the development of RRFS-based convection-permitting ensemble forecasts through improved forecast spread, skill, and probabilistic forecasts.
1
Bridging Watch-to-Warning Forecast Operations with Refined Probabilistic Guidance
Loken
Testbeds
search_activity
2022
Level 3
The goal of this project is to develop skillful, rapidly-updating severe weather spatial probability forecasts focused on lead times between 30 minutes and 3 hours to provide guidance on the severe weather threat in the times between traditional weather watches and warnings.
Eric Loken, Katie Wilson, Kristin Calhoun, Thea Sandmael
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
The goal of the Forecasting a Continuum of Environmental Threats (FACETs) framework
is to provide users with rapidly-updating probabilistic hazard information (PHI) across the
lifecycle of a weather event. An important tool for accomplishing this goal is skillful automated
PHI, which increases forecasters' situational awareness and aids their ability to provide timely
severe weather threat guidance to the public. Scientists have developed observation-based PHI at
the warning scale (i.e., ~30 min. prior to hazardous weather) and high-resolution numerical
weather prediction (NWP) PHI at the watch scale (i.e., several hours prior to hazardous weather).
However, blended PHI that specifically targets the prediction of severe weather between watch
and warning scales using both observational and NWP information has not yet been created. To
that end, this project proposes to develop, evaluate, and iteratively improve automated
severe weather PHI that provides guidance from 3 hours to 30 minutes ahead of a severe
weather event using observational and NWP data.
This project will result in the development of real-time PHI based on combined information
from the PHI Tool (including ProbSevere and PHItor) and WoFS. The combined severe weather
PHI will aid in the production of more continuous and rapidly-updating severe weather hazard
probabilities, which will support NOAA in meeting its goal of providing more timely and effective
severe weather warnings. An important output of this project will be the real-time availability of
combined severe weather PHI on a publicly-accessible website for viewing by NWS forecasters
and other interested users. Documentation of the ML-derived combined severe weather PHI, its
objective and subjective performance, best practices for operational use, and application to short-
term severe weather predictability challenges will be provided in two peer-reviewed journal
articles and shared at professional conferences. If the COVID-19 pandemic prevents in-person
travel to these conferences, attending PIs/Co-PIs will participate virtually. Relevant codes and
guidance will be made available to NOAA forecasters and researchers, with real-time guidance
accessible on public websites.
bridging watch to warning forecast operations with refined probabilistic guidance, the goal of this project is to develop skillful rapidly updating severe weather spatial probability forecasts focused on lead times between 30 minutes and 3 hours to provide guidance on the severe weather threat in the times between traditional weather watches and warnings, the goal of the forecasting a continuum of environmental threats facets framework is to provide users with rapidly updating probabilistic hazard information phi across the lifecycle of a weather event an important tool for accomplishing this goal is skillful automated phi which increases forecasters' situational awareness and aids their ability to provide timely severe weather threat guidance to the public scientists have developed observation based phi at the warning scale i e ~30 min prior to hazardous weather and high resolution numerical weather prediction nwp phi at the watch scale i e several hours prior to hazardous weather however blended phi that specifically targets the prediction of severe weather between watch and warning scales using both observational and nwp information has not yet been created to that end this project proposes to develop evaluate and iteratively improve automated severe weather phi that provides guidance from 3 hours to 30 minutes ahead of a severe weather event using observational and nwp data
Storm Prediction Center (SPC)
CIWRO/University of Oklahoma
The goal of this project is to develop skillful, rapidly-updating severe weather spatial probability forecasts focused on lead times between 30 minutes and 3 hours to provide guidance on the severe weather threat in the
5
Probabilistic medium-range hazards guidance with an FV3-based convection-allowing ensemble and machine learning
Schwartz
Testbeds
search_activity
2022
Level 3
This project aims to produce and validate medium-range convective forecast guidance provided by both a CAM ensemble and ML algorithms, focusing on convective mode and severe weather hazards.
Craig Schwartz, Ryan Sobash, Lucas Harris
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Given the potential for convection-allowing model (CAM) ensembles and ML to improve medium-range severe
weather forecasts compared to existing methods, this proposal aims to produce and validate
medium-range convective forecast guidance provided by both a CAM ensemble and ML
algorithms, focusing on convective mode and severe weather hazards. Specifically, the proposed
work will 1) produce real-time 8-day, 10-member CAM ensemble forecasts with 3-km horizontal
grid spacing over the conterminous United States using a variable-resolution global NWP model
based on the Finite-Volume Cubed-Sphere (FV3) dynamic core; 2) apply ML to improve raw
CAM forecasts of convective mode and hazards through the entire 8-day forecast period; 3)
explore forecasts from time-lagged CAM ensembles; 4) objectively validate forecasts to quantify
how skill and reliability of severe weather forecasts change with spatial scale and lead-time; and
5) provide real-time forecasts to NOAA's 2023-2025 Hazardous Weather Tested (HWT) Spring
Forecasting Experiments (SFEs), representing the first time that medium-range CAM ensemble
forecasts are subjectively evaluated by HWT SFE participants.
Several outputs and products will be designed, tested, and transferred during the project.
Specifically, the model configurations, variable-resolution global grid, and workflow used to
produce the FV3-based CAM ensemble forecasts will be implemented into the Unified Forecast
System (UFS) so NOAA and other scientists can run similar forecasts. Additionally, the final
ML-based convective hazards prediction system will be available for transition to NOAA's SPC.
SPC
Israel Jirak - israel.jirak@noaa.gov
National Center for Atmospheric Research;
NOAA/OAR/ESRL
NA22OAR4590533
Severe Weather
Colorado
Testbeds
August 2022 - July 2026
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Numerical Weather Prediction (NWP), Probabilistic Weather Forecasting
probabilistic medium range hazards guidance with an fv3 based convection allowing ensemble and machine learning, this project aims to produce and validate medium range convective forecast guidance provided by both a cam ensemble and ml algorithms focusing on convective mode and severe weather hazards, given the potential for convection allowing model cam ensembles and ml to improve medium range severe weather forecasts compared to existing methods this proposal aims to produce and validate medium range convective forecast guidance provided by both a cam ensemble and ml algorithms focusing on convective mode and severe weather hazards specifically the proposed work will 1 produce real time 8 day 10 member cam ensemble forecasts with 3 km horizontal grid spacing over the conterminous united states using a variable resolution global nwp model based on the finite volume cubed sphere fv3 dynamic core 2 apply ml to improve raw cam forecasts of convective mode and hazards through the entire 8 day forecast period 3 explore forecasts from time lagged cam ensembles 4 objectively validate forecasts to quantify how skill and reliability of severe weather forecasts change with spatial scale and lead time and 5 provide real time forecasts to noaa's 2023-2025 hazardous weather tested hwt spring forecasting experiments sfes representing the first time that medium range cam ensemble forecasts are subjectively evaluated by hwt sfe participants
Storm Prediction Center (SPC)
National Center for Atmospheric Research;, NOAA/OAR/ESRL
This project aims to produce and validate medium-range convective forecast guidance provided by both a CAM ensemble and ML algorithms, focusing on convective mode and severe weather hazards.
5
Demonstration of Advanced Ensemble Prediction Services for NWS Hydrometeorological Forecast Operations
Gochis
Testbeds
check_circle
2015
Level 3
Demonstration of Advanced Ensemble Prediction Services for NWS Hydrometeorological Forecast Operations
Thu Aug 31 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Verification & Validation
HMT
This proposal extends on-going research and development efforts within the NOAA Hydrometeorology Testbed (HMT), NCAR, and the new NWS National Water Center to involve existing NWS hydrological services staff responsible for integrating new technologies into seamless flood and water supply forecast and warning operations.
Produce improved combined hydrometeorological forecasts and help communicate the associated risks to forecasters and end-users
NWC
N/A
National Center for Atmospheric Research;
NOAA/OAR/ESRL
NA15OAR4590157
Water Extremes, Winter Weather
Colorado
Testbeds
September 2015 - August 2017
Atmospheric Physics, Data Assimilation and Ensembles, Verification & Validation
demonstration of advanced ensemble prediction services for nws hydrometeorological forecast operations, demonstration of advanced ensemble prediction services for nws hydrometeorological forecast operations, this proposal extends on going research and development efforts within the noaa hydrometeorology testbed hmt ncar and the new nws national water center to involve existing nws hydrological services staff responsible for integrating new technologies into seamless flood and water supply forecast and warning operations
National Water Center (NWC)
National Center for Atmospheric Research;, NOAA/OAR/ESRL
Demonstration of Advanced Ensemble Prediction Services for NWS Hydrometeorological Forecast Operations
Multi-Radar/Multi-Sensor System (MRMS), Unified Forecast System (UFS)
This proposal aims to capitalize on the outcomes from the first year of HMT-Hydro testbed experiment and to facilitate the transition of FLASH products to NWS operations at the National Center for Environmental Prediction (NCEP) Central Operations (NCO) as part of the MRMS transition presently underway.
Identify and validate new models for flash flood forecasting, identify and validate new methods for ensemble hydrometeorological prediction, and improve forecasters' ability to depict hazards.
multi radar multi sensor mrms hmt hydro experiment, multi radar multi sensor mrms hmt hydro experiment, this proposal aims to capitalize on the outcomes from the first year of hmt hydro testbed experiment and to facilitate the transition of flash products to nws operations at the national center for environmental prediction ncep central operations nco as part of the mrms transition presently underway
Thu Aug 31 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Verification & Validation
HMT
To build direct collaborations with the Weather Prediction Center (WPC) and evolve current collaborations with the National Severe Storms Laboratory (NSSL), and the Environmental Modeling Center (EMC). This project will continue to improve the CAPS realtime storm-scale ensemble forecast (SSEF) system utilizing advanced data assimilation including Ensemble Kalman Filter (EnKF) and 3DVAR/cloud analysis techniques, and conduct real-time forecasts in support of the Hydrometeorological Testbed (HMT) Flash Flood and Intense Rainfall (FFaIR) summer experiments.
Development and optimization of a 3-km grid spacing CONUS-domain EnKF DA system capable of assimilating all operational data sets used by the RR/GSI system plus full-volume WSR-88D radial velocity and reflectivity data.
EMC
N/A
University of Oklahoma
NA15OAR4590159
Water Extremes
Oklahoma
Testbeds
September 2015 - August 2017
Data Assimilation and Ensembles, Verification & Validation
storm scale ensemble prediction optimized for heavy precipitation forecasting in support of the hydrometeorology testbed hmt, storm scale ensemble prediction optimized for heavy precipitation forecasting in support of the hydrometeorology testbed hmt, to build direct collaborations with the weather prediction center wpc and evolve current collaborations with the national severe storms laboratory nssl and the environmental modeling center emc this project will continue to improve the caps realtime storm scale ensemble forecast ssef system utilizing advanced data assimilation including ensemble kalman filter enkf and 3dvar cloud analysis techniques and conduct real time forecasts in support of the hydrometeorological testbed hmt flash flood and intense rainfall ffair summer experiments
Environmental Modeling Center (EMC)
University of Oklahoma
Storm-Scale Ensemble Prediction Optimized for Heavy Precipitation Forecasting in Support of the Hydrometeorology Testbed (HMT)
1
Validation and Improvement of Microphysical Parameterizations for Better Orographic Precipitation Forecasts
Kingsmill
Testbeds
check_circle
2015
Level 3
Validation and Improvement of Microphysical Parameterizations for Better Orographic Precipitation Forecasts
This project will focus on retrospective simulations of a landfalling winter storm impacting the coastal orography of northern California where an array of observing systems has been deployed over the last ~10 years in support of the western Hydrometeorology Testbed (HMT-West). The NMMB model running the Ferrier-Aligo, WSM6 and Thompson microphysics scheme will be employed to simulate this storm. Profiling radar, GPS-Met and surface meteorology observations from HMT-West and Stage-IV horizontal precipitation maps will be used to validate the simulations.
Modifications to the Ferrier-Aligo scheme leading to improved forecasts
validation and improvement of microphysical parameterizations for better orographic precipitation forecasts, validation and improvement of microphysical parameterizations for better orographic precipitation forecasts, this project will focus on retrospective simulations of a landfalling winter storm impacting the coastal orography of northern california where an array of observing systems has been deployed over the last ~10 years in support of the western hydrometeorology testbed hmt west the nmmb model running the ferrier aligo wsm6 and thompson microphysics scheme will be employed to simulate this storm profiling radar gps met and surface meteorology observations from hmt west and stage iv horizontal precipitation maps will be used to validate the simulations
Environmental Modeling Center (EMC)
CIMMS/University of Oklahoma
Validation and Improvement of Microphysical Parameterizations for Better Orographic Precipitation Forecasts
1
Demonstration of a Rapid Update Convection-Permitting Ensemble Forecast System to Improve Flash Flood and Winter Weather Prediction
Romine
Testbeds
check_circle
2017
Level 3
The proposed work will contribute toward improved short-term guidance on the predictability of high-impact phenomena by developing specific guidance for advancing the High-Resolution Rapid Refresh (HRRR) analysis and forecast system toward an ensemble analysis and prediction system.
Glen Romine
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting, Verification & Validation
HMT
High-Resolution Rapid Refresh (HRRR)
To aid the NOAA/Hydrometeorology Testbed (HMT) objectives to improve flash flood and winter weather hazard forecasting, this project will demonstrate the value of probabilistic hazard guidance drawn from CPE forecasts.
Test new approaches to improve short-range prediction of high impact weather
EMC
James Nelson
National Center for Atmospheric Research
NA17OAR4590122
Water Extremes, Winter Weather
Colorado
Testbeds
July 2017 - June 2020
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting, Verification & Validation
demonstration of a rapid update convection permitting ensemble forecast system to improve flash flood and winter weather prediction, the proposed work will contribute toward improved short term guidance on the predictability of high impact phenomena by developing specific guidance for advancing the high resolution rapid refresh hrrr analysis and forecast system toward an ensemble analysis and prediction system, to aid the noaa hydrometeorology testbed hmt objectives to improve flash flood and winter weather hazard forecasting this project will demonstrate the value of probabilistic hazard guidance drawn from cpe forecasts
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
The proposed work will contribute toward improved short-term guidance on the predictability of high-impact phenomena by developing specific guidance for advancing the High-Resolution Rapid Refresh (HRRR) analysis and forecast system toward an ensemble analysis and
2
Convection-Allowing Ensemble Prediction for Heavy Precipitation in Support of the Hydrometeorology Testbed (HMT): New QPF Products, Data Assimilation Techniques and Prediction Model
Xue
Testbeds
check_circle
2017
Level 3
The primary goal of this project includes the design, testing, and realtime of CONUS-domain 3-km CAM ensemble forecasts for quantitative precipitation forecasting (QPF), and evaluating them via the HMT FFaIR Experiments.
Ming Xue
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Jun 30 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Verification & Validation
HMT
Convection Allowing Models (CAM), High Resolution Ensemble Forecast (HREF), Unified Forecast System (UFS)
Work for the Hydrometeorology Testbed (HMT) is proposed in seven areas, the first four involve development of techniques or processes to upgrade the CAPS storm-scale ensemble forecasting (SSEF) system that had been run for the Hydrometeorological Testbed (HMT) 2016 Flash Flood and Intense Rainfall Experiment (FFaIR) for the 2018-2019 experiments. The last three include the actual running of the ensemble forecast system, in-person participation in FFaIR and evaluation of ensemble forecast products.
Production of the ensemble forecasts and forecast products and the verification of the forecast product results.
EMC
James Nelson
University of Oklahoma
NA17OAR4590120
Water Extremes
Oklahoma
Testbeds
July 2017 - June 2019
Data Assimilation and Ensembles, Verification & Validation
convection allowing ensemble prediction for heavy precipitation in support of the hydrometeorology testbed hmt new qpf products data assimilation techniques and prediction model, the primary goal of this project includes the design testing and realtime of conus domain 3 km cam ensemble forecasts for quantitative precipitation forecasting qpf and evaluating them via the hmt ffair experiments, work for the hydrometeorology testbed hmt is proposed in seven areas the first four involve development of techniques or processes to upgrade the caps storm scale ensemble forecasting ssef system that had been run for the hydrometeorological testbed hmt 2016 flash flood and intense rainfall experiment ffair for the 2018 2019 experiments the last three include the actual running of the ensemble forecast system in person participation in ffair and evaluation of ensemble forecast products
Environmental Modeling Center (EMC)
University of Oklahoma
The primary goal of this project includes the design, testing, and realtime of CONUS-domain 3-km CAM ensemble forecasts for quantitative precipitation forecasting (QPF), and evaluating them via the HMT FFaIR Experiments.
1
Enabling Effective Use of Deterministic-to-Probabilistic Precipitation Forecasts for Heavy and Extreme Events
Jensen
Testbeds
check_circle
2017
Level 3
The overall goals of this project are to improve heavy and extreme quantitative precipitation forecasts (QPF) by integrating verification research with social science research conducted with NWS forecasters to develop verification metrics and tools that improve forecasters' interpretation and use of deterministic and ensemble-based model guidance, with a focus on convection-allowing model (CAM) guidance.
Tara Jensen
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance, Probabilistic Weather Forecasting, Verification & Validation, "Social, Behavioral, and Economic Sciences (SBES)"
HMT
With a goal of improving heavy and extreme quantitative precipitation forecasts (QPF), this project will integrate verification research with social science research to develop verification metrics and tools that improve forecasters' adoption, interpretation, and use of deterministic and probabilistic model guidance with a focus on convection-allowing guidance.
Determine a set of objective measures that may be employed by HMT, and likely by other testbeds such as Hazardous Weather Testbed, that leverages new technology in ways that meet forecasters' needs.
EMC
James Nelson
National Center for Atmospheric Research
NA17OAR4590128
Water Extremes
Colorado
Testbeds
July 2017 - June 2020
Data Assimilation and Ensembles, Model & Forecast Guidance, Probabilistic Weather Forecasting, Verification & Validation, "Social Science (SBES)"
enabling effective use of deterministic to probabilistic precipitation forecasts for heavy and extreme events, the overall goals of this project are to improve heavy and extreme quantitative precipitation forecasts qpf by integrating verification research with social science research conducted with nws forecasters to develop verification metrics and tools that improve forecasters' interpretation and use of deterministic and ensemble based model guidance with a focus on convection allowing model cam guidance, with a goal of improving heavy and extreme quantitative precipitation forecasts qpf this project will integrate verification research with social science research to develop verification metrics and tools that improve forecasters' adoption interpretation and use of deterministic and probabilistic model guidance with a focus on convection allowing guidance
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
The overall goals of this project are to improve heavy and extreme quantitative precipitation forecasts (QPF) by integrating verification research with social science research conducted with NWS forecasters to develop verification metrics and tools that
0
Improving Lake-Effect Snow and Ice Forecasting for the Great Lakes Region
Chu
Testbeds
check_circle
2017
Level 3
The goal of this project is to fill critical gaps between operational marine forecast products (e.g. Great Lakes Operational Forecast System; GLOFS) and short-term weather forecast models (e.g. HRRR) through linkage and exchange of dynamic lake surface conditions, and provide WFO operational forecasters with improved lake-effect snowfall, precipitation, and ice forecasts guidance and ultimately protect safety of the citizens in the Great Lakes region.
Philip Chu
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Jun 30 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Verification & Validation
HMT
This project aims to provide Great Lakes NWS/WFO forecasters with improved lake effect snow and pce predictions by addressing the above requirements and leveraging existing model development and transition to operations efforts at GLERL
Provide improved forecasts of lake-ice conditions, latent and sensible heat fluxes from the lake surface, and improvement to lake-effect snowfall and visibility/cloud forecasts.
EMC
James Nelson
NOAA/OAR/GLERL
NOAA-OAR-OWAQ-2017-2005004
Winter Weather
Michigan
Testbeds
July 2017 - June 2019
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Verification & Validation
improving lake effect snow and ice forecasting for the great lakes region, the goal of this project is to fill critical gaps between operational marine forecast products e g great lakes operational forecast system glofs and short term weather forecast models e g hrrr through linkage and exchange of dynamic lake surface conditions and provide wfo operational forecasters with improved lake effect snowfall precipitation and ice forecasts guidance and ultimately protect safety of the citizens in the great lakes region, this project aims to provide great lakes nws wfo forecasters with improved lake effect snow and pce predictions by addressing the above requirements and leveraging existing model development and transition to operations efforts at glerl
Environmental Modeling Center (EMC)
NOAA/OAR/GLERL
The goal of this project is to fill critical gaps between operational marine forecast products (e.g. Great Lakes Operational Forecast System; GLOFS) and short-term weather forecast models (e.g. HRRR) through linkage and exchange of dynamic
0
Assessment of Hydrologic Forecasts Generated Using Multi-Model and Multi-Precipitation Product Forcing
Krajewski
Testbeds
check_circle
2017
Level 3
The project's objective is to provide guidance for NWS forecasters on understanding forecast errors and skills associated with model (National Water Model and multiple high-resolution precipitation forcing products) structures and components, precipitation forcing, and relevant hydrologic scales.
Witold Krajewski
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Verification & Validation
HMT
To understand forecast errors, as well as skills, associated with the relevant scales, model structure and components, and precipitation forcing with the ultimate goal of improving streamflow forecasting for the nation.
Improving streamflow forecasting for the nation.
EMC
Mike DeWeese
University of Iowa
NA17OAR4590131
Water Extremes, Winter Weather
Iowa
Testbeds
July 2017 - July 2019
Model & Forecast Guidance, Verification & Validation
assessment of hydrologic forecasts generated using multi model and multi precipitation product forcing, the project's objective is to provide guidance for nws forecasters on understanding forecast errors and skills associated with model national water model and multiple high resolution precipitation forcing products structures and components precipitation forcing and relevant hydrologic scales, to understand forecast errors as well as skills associated with the relevant scales model structure and components and precipitation forcing with the ultimate goal of improving streamflow forecasting for the nation
Environmental Modeling Center (EMC)
University of Iowa
The project's objective is to provide guidance for NWS forecasters on understanding forecast errors and skills associated with model (National Water Model and multiple high-resolution precipitation forcing products) structures and components, precipitation forcing, and relevant
3
Comparison of Model versus Observationally-Driven Water Vapor Profiles for Forecasting Heavy Precipitation Events
Forsythe
Testbeds
check_circle
2017
Level 3
The purpose of the project is to evaluate a tool for forecasters to assess forecast model water vapor initial fields, especially over ocean by testing whether comparisons of model versus satellite-derived layered water vapor profiles are useful tools for assessing short-term precipitation forecasts.
John Forsythe
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
To develop the ability to difference GFS model moisture fields against the blended LPW product and produce a variety of diagnostics, in all seasons and synoptic conditions over land and ocean.
New knowledge can be used to assess or adjust model quantitative precipitation fields in the 0-6 hour timeframe and identify model physics deficiencies.
comparison of model versus observationally driven water vapor profiles for forecasting heavy precipitation events, the purpose of the project is to evaluate a tool for forecasters to assess forecast model water vapor initial fields especially over ocean by testing whether comparisons of model versus satellite derived layered water vapor profiles are useful tools for assessing short term precipitation forecasts, to develop the ability to difference gfs model moisture fields against the blended lpw product and produce a variety of diagnostics in all seasons and synoptic conditions over land and ocean
Environmental Modeling Center (EMC)
CIRA/Colorado State University
The purpose of the project is to evaluate a tool for forecasters to assess forecast model water vapor initial fields, especially over ocean by testing whether comparisons of model versus satellite-derived layered water vapor profiles
1
Probabilistic Warn-on-Forecast System for Heavy Rainfall and Flash Flooding
Martinaitis
Testbeds
check_circle
2017
Level 3
The objective of this project is to evaluate new products focused on improving the predictability of flash flooding on the warning time-scale using probabilistic information and storm-scale quantitative precipitation forecasts (QPFs).
Steven Martinaitis, Jonathan Gourley
Complete
Sun Oct 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
This project proposes to continue the Multi-Radar Multi-Sensor (MRMS) HMT-Hydro Testbed Experiment for two additional years in Norman, OK, with the overarching goal of HMT-Hydro being to evaluate new models and methodologies for forecasting heavy rainfall and flash flooding.
Improving NWS forecaster-issued flash flood watches and warnings.
probabilistic warn on forecast system for heavy rainfall and flash flooding, the objective of this project is to evaluate new products focused on improving the predictability of flash flooding on the warning time scale using probabilistic information and storm scale quantitative precipitation forecasts qpfs, this project proposes to continue the multi radar multi sensor mrms hmt hydro testbed experiment for two additional years in norman ok with the overarching goal of hmt hydro being to evaluate new models and methodologies for forecasting heavy rainfall and flash flooding
NCEP Central Operations (NCO)
CIMMS/University of Oklahoma, NOAA/OAR/NSSL
The objective of this project is to evaluate new products focused on improving the predictability of flash flooding on the warning time-scale using probabilistic information and storm-scale quantitative precipitation forecasts (QPFs).
2
Quantifying Observational Requirements for WRF-Hydro Forcing in the West Using Russian River HMT Experience and Data to Inform National Water Center Tools
Ralph
Testbeds
check_circle
2017
Level 3
This project's goal is to utilize the combination of existing NOAA Hydrometeorology Testbed (HMT) and newly installed Scripps/CW3E Forecast-Informed Reservoir Operations (FIRO) Hydrometeorological Network soil moisture, streamflow, and precipitation observations to test the response of the WRF-Hydro model/National Water Model (NWM) to calibrations with and without soil moisture.
Fred Ralph
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
National Water Model (NWM), Weather Research and Forecasting Model (WRF), Weather Research and Forecasting Model - Hydro (WRF-Hydro)
This project aims to integrate the data from a foundational soil moisture observing network in and near the Lake Mendocino watershed, part of the Russian River basin in California, with the state-of-the-art hydrologic modeling capabilities being developed at the National Water Center (NWC) to support River Forecast Centers around the country.
quantifying observational requirements for wrf hydro forcing in the west using russian river hmt experience and data to inform national water center tools, this project's goal is to utilize the combination of existing noaa hydrometeorology testbed hmt and newly installed scripps cw3e forecast informed reservoir operations firo hydrometeorological network soil moisture streamflow and precipitation observations to test the response of the wrf hydro model national water model nwm to calibrations with and without soil moisture, this project aims to integrate the data from a foundational soil moisture observing network in and near the lake mendocino watershed part of the russian river basin in california with the state of the art hydrologic modeling capabilities being developed at the national water center nwc to support river forecast centers around the country
National Water Center (NWC)
CIMEC/University of California San Diego
This project's goal is to utilize the combination of existing NOAA Hydrometeorology Testbed (HMT) and newly installed Scripps/CW3E Forecast-Informed Reservoir Operations (FIRO) Hydrometeorological Network soil moisture, streamflow, and precipitation observations to test the response of
2
Advancing Probabilistic Prediction of High-Impact Winter Storms through Ensemble NWP and Post-Processing
Minder
Testbeds
check_circle
2019
Level 3
The goal of this project is to improve operational forecast techniques and advance ensemble numerical modeling capabilities used by NOAA for short-and medium-range forecasting of high-impact winter precipitation.
Sun Jun 30 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Numerical Weather Prediction (NWP), Parameterization, Post-processing, Probabilistic Weather Forecasting
HMT
This project looks to develop and test new snow-to-liquid-ratio algorithms to improve snowfall forecasts derived from downscaled medium-range ensembles and increase the skill of probabilistic snowfall forecasts as well as evaluate and improve methods for representing physics uncertainty in high-resolution ensemble forecasts of winter storms using stochastic parameterizations. This project leverages artificial intelligence methods.
Improved snowfall and precipitation forecasts
EMC
Hui-Ya Chuang
SUNY Albany
University of Utah
NA19OAR4590136
NA19OAR4590137
Winter Weather
New York, Utah
Testbeds
July 2019 - June 2024
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Numerical Weather Prediction (NWP), Parameterization, Post-processing, Probabilistic Weather Forecasting
advancing probabilistic prediction of high impact winter storms through ensemble nwp and post processing, the goal of this project is to improve operational forecast techniques and advance ensemble numerical modeling capabilities used by noaa for short and medium range forecasting of high impact winter precipitation, this project looks to develop and test new snow to liquid ratio algorithms to improve snowfall forecasts derived from downscaled medium range ensembles and increase the skill of probabilistic snowfall forecasts as well as evaluate and improve methods for representing physics uncertainty in high resolution ensemble forecasts of winter storms using stochastic parameterizations this project leverages artificial intelligence methods
Environmental Modeling Center (EMC)
SUNY Albany, University of Utah
The goal of this project is to improve operational forecast techniques and advance ensemble numerical modeling capabilities used by NOAA for short-and medium-range forecasting of high-impact winter precipitation.
5
Improving Lake-Effect Snow Forecasting Capabilities via Advanced Coupling Techniques in NOAA's Unified Forecast System (UFS)
Jablonowski
Testbeds
check_circle
2019
Level 3
The goal of this project is to improve lake-effect snow and ice forecasting capabilities via advanced lake-ice-atmosphere coupling techniques for the Unified Forecast System (UFS).
Fri Jun 30 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques
HMT
Unified Forecast System (UFS)
The goal of this project is to improve lake-effect snow and ice forecasting capabilities via advanced lake-ice-atmosphere coupling techniques for the Unified Forecast System
Improve lake-effect snow and ice forecasting capabilities
EMC
Hui-Ya Chuang
CIGLR/University of Michigan
NOAA/OAR/GLERL
NA19OAR4590140
Winter Weather
Michigan
Testbeds
July 2019 - June 2023
Atmospheric Physics, Coupled Models & Techniques
improving lake effect snow forecasting capabilities via advanced coupling techniques in noaa's unified forecast system ufs, the goal of this project is to improve lake effect snow and ice forecasting capabilities via advanced lake ice atmosphere coupling techniques for the unified forecast system ufs, the goal of this project is to improve lake effect snow and ice forecasting capabilities via advanced lake ice atmosphere coupling techniques for the unified forecast system
Environmental Modeling Center (EMC)
CIGLR/University of Michigan, NOAA/OAR/GLERL
The goal of this project is to improve lake-effect snow and ice forecasting capabilities via advanced lake-ice-atmosphere coupling techniques for the Unified Forecast System (UFS).
4
Enhancing CAM Ensemble Forecast System and Improving Ensemble Forecast Products in Support of HMT Winter Weather and Heavy Precipitation Forecasting
Brewster
Testbeds
check_circle
2019
Level 3
The goal of this project is to develop and test Stand-Alone Region FV3 (SAR-FV3) high resolution ensemble forecasts and new ensemble products for improving numerical weather prediction guidance for prediction of rainfall and flood hazard in the summer as well as snowfall and winter weather hazards in the winter.
Develop and test Stand-Alone Region FV3 (SAR-FV3) high resolution ensemble forecasts and new ensemble products for improving numerical weather prediction guidance for prediction of rainfall and flood hazard in the summer as well as snowfall and winter weather hazards in the winter. This project explores the use of machine learning.
Improved numerical weather prediction guidance for prediction of rainfall and flood hazard in the summer as well as snowfall and winter weather hazards in the winter
EMC
Hui-Ya Chuang
University of Oklahoma
NA19OAR4590141
Water Extremes, Winter Weather
Oklahoma
Testbeds
July 2019 - June 2023
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Numerical Weather Prediction (NWP)
enhancing cam ensemble forecast system and improving ensemble forecast products in support of hmt winter weather and heavy precipitation forecasting, the goal of this project is to develop and test stand alone region fv3 sar fv3 high resolution ensemble forecasts and new ensemble products for improving numerical weather prediction guidance for prediction of rainfall and flood hazard in the summer as well as snowfall and winter weather hazards in the winter, develop and test stand alone region fv3 sar fv3 high resolution ensemble forecasts and new ensemble products for improving numerical weather prediction guidance for prediction of rainfall and flood hazard in the summer as well as snowfall and winter weather hazards in the winter this project explores the use of machine learning
Environmental Modeling Center (EMC)
University of Oklahoma
The goal of this project is to develop and test Stand-Alone Region FV3 (SAR-FV3) high resolution ensemble forecasts and new ensemble products for improving numerical weather prediction guidance for prediction of rainfall and flood hazard
6
Advancing Probabilistic Prediction of Snow-to-Liquid Ratio and Snowfall during High-Impact Winter Storms
Steenburgh
Testbeds
search_activity
2022
Level 3
The goal of this project is to advance NWS probabilistic snowfall prediction through better specification of SLR and improved knowledge of the contributions of SLR and precipitation amount errors to probabilistic snowfall forecast errors produced with convection-allowing ensembles.
James Steenburgh, Peter Veals
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting
HMT
Rapid Refresh Forecast System (RRFS), Unified Post Processor (UPP)
The proposed research extends our ongoing collaboration with the National Weather Service
Weather Prediction Center (WPC) to advance snowfall prediction through better specification of
snow-to-liquid ratio (SLR) and other model post-processing techniques. Specifically, we will
work with the WPC to to improve the probalistic snow-to-liquid ratio (SLR) and snowfall guidance
from convection-allowing ensemble modeling systems, including the Rapid Refresh Forecast
System (RRFS) ensemble scheduled for operational implementation in late 2023. The proposed
research involves the development of new SLR ratio algorithms at the University of Utah, testing
during the WPC Winter Weather Experiment (WWE), and the transfer of successfully
demonstarted algorithms to the WPC and the Unified Post Processor (UPP) for operational use.
Such algorithms will also be provided to other NOAA/NWS groups, as well as the broader weather
enterprise, through an internet hosting platform such as GitHub. The research will also identify
the contributions of SLR and precipitation amount forecast errors to snowfall errors in probabilistic
forecasts derived from the RRFS ensemble. These research activities are strongly aligned with the
hydrometeorology testbed priorities HMT-1, HMT-3, and HMT-4 by developing new methods
and models to improve probabilistic winter precipitation forecasts of snowfall amounts, enhancing
forecaster use of short- and medium-range probabilistic precipitation forecasts, and improving
forecasts of extreme precipitation events.
We will provide probabalistic snowfall forecasts to WPC for the WWE each cool season as part
of a research and development loop involving the development of new algorithms, testing and
evaluation in a quasi-operational environment (i.e., the WWE), refinement, and then testing
again. For the WWE the primary output will be probabalistic quantified snowfall and SLR fore-
casts, as well as other products requested by the WPC. These products will be provided in
GRIB2, netCDF, or alternate format. Typically we work with WPC each fall to provide what-
ever products that they require for the WWE for real-time or retrospective application.
WPC
Kirstin Harnos - kirstin.harnos@noaa.gov
University of Utah
NA22OAR4590531
Winter Weather
Utah
Testbeds
August 2022 - July 2026
Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting
advancing probabilistic prediction of snow to liquid ratio and snowfall during high impact winter storms, the goal of this project is to advance nws probabilistic snowfall prediction through better specification of slr and improved knowledge of the contributions of slr and precipitation amount errors to probabilistic snowfall forecast errors produced with convection allowing ensembles, the proposed research extends our ongoing collaboration with the national weather service weather prediction center wpc to advance snowfall prediction through better specification of snow to liquid ratio slr and other model post processing techniques specifically we will work with the wpc to to improve the probalistic snow to liquid ratio slr and snowfall guidance from convection allowing ensemble modeling systems including the rapid refresh forecast system rrfs ensemble scheduled for operational implementation in late 2023 the proposed research involves the development of new slr ratio algorithms at the university of utah testing during the wpc winter weather experiment wwe and the transfer of successfully demonstarted algorithms to the wpc and the unified post processor upp for operational use such algorithms will also be provided to other noaa nws groups as well as the broader weather enterprise through an internet hosting platform such as github the research will also identify the contributions of slr and precipitation amount forecast errors to snowfall errors in probabilistic forecasts derived from the rrfs ensemble these research activities are strongly aligned with the hydrometeorology testbed priorities hmt 1 hmt 3 and hmt 4 by developing new methods and models to improve probabilistic winter precipitation forecasts of snowfall amounts enhancing forecaster use of short and medium range probabilistic precipitation forecasts and improving forecasts of extreme precipitation events
Weather Prediction Center (WPC)
University of Utah
The goal of this project is to advance NWS probabilistic snowfall prediction through better specification of SLR and improved knowledge of the contributions of SLR and precipitation amount errors to probabilistic snowfall forecast errors produced
4
Developing the "Next-Generation" Winter Weather Experiment Testbed: Integrating Road Hazards
Tobin
Testbeds
search_activity
2022
Level 3
The goal of this project is to identify what probabilistic forecast information is relevant to end-user decision-makers when it comes to improving safety on roadways, and determine how this information should be presented to those end users.
Dana Tobin, Heather Reeves, Kim Klockow-McCain, James Correia, Kirstin Harnos
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Probabilistic Weather Forecasting, Decision-Making, Integrated Warning Team (IWT), Risk Communication, "Social, Behavioral, and Economic Sciences (SBES)"
HMT
This project comprises three tasks: 1) Perform an audit of inter-agency and public-facing road-
hazard messaging during impactful winter-weather events. 2) Develop new WWE activities related
to what, how, when, and to whom road hazards are communicated in response to available
probabilistic forecast guidance. 3) Assemble integrated warning teams (IWTs) within select WFO
service areas that include forecasters, transportation officials, emergency managers, and other
partners. IWTs will assess communication strategies currently employed during winter weather,
including identifying limitations and discussing strategies for improvement. Discussions will focus
on both inter-agency communication and public-facing messaging. IWTs will also evaluate
whether probabilistic guidance is valuable to stakeholders in their respective decision-making
processes, what information is desired, and how that information is best communicated.
The planned impacts/outcomes/benefits for this project are to provide improve inter-agency
communication of probabilistic winter weather information for the purposes of improving surface-
transportation-related decisions and safety. This project will provide forecaster recommendations
for inter-agency communication and messaging, and will also properly set the stage for the "Next-
Generation" testbed engagement within WWE for end-users. This "Next-Generation" testbed
engagement within WWE will begin to fold these external partners into testbed activities, yet it is
important to first understand what their needs are for probabilistic weather information, such as
what products would be useful and when, and how to best communicate weather forecasts and
anticipated impacts for decision-making support. Improved inter-agency communication and
messaging of forecasted surface-transportation impacts will lead to improved public-facing
messaging and agency decisions that affect motorist safety.
WPC
Jim Nelson - james.a.nelson@noaa.gov
CIWRO/University of Oklahoma
CIRES/University of Colorado Boulder
NA22OAR4590529
Winter Weather
Oklahoma
Testbeds
August 2022 - July 2026
Model & Forecast Guidance, Probabilistic Weather Forecasting, Decision-Making, Integrated Warning Team (IWT), Risk Communication, "Social Science (SBES)"
developing the "next generation" winter weather experiment testbed integrating road hazards, the goal of this project is to identify what probabilistic forecast information is relevant to end user decision makers when it comes to improving safety on roadways and determine how this information should be presented to those end users, this project comprises three tasks 1 perform an audit of inter agency and public facing road hazard messaging during impactful winter weather events 2 develop new wwe activities related to what how when and to whom road hazards are communicated in response to available probabilistic forecast guidance 3 assemble integrated warning teams iwts within select wfo service areas that include forecasters transportation officials emergency managers and other partners iwts will assess communication strategies currently employed during winter weather including identifying limitations and discussing strategies for improvement discussions will focus on both inter agency communication and public facing messaging iwts will also evaluate whether probabilistic guidance is valuable to stakeholders in their respective decision making processes what information is desired and how that information is best communicated
Weather Prediction Center (WPC)
CIWRO/University of Oklahoma, CIRES/University of Colorado Boulder
The goal of this project is to identify what probabilistic forecast information is relevant to end-user decision-makers when it comes to improving safety on roadways, and determine how this information should be presented to those
0
FV3-LAM CAM Ensemble Forecast System and Improving Ensemble Probabilistic and Consensus Forecast Products in Support of HMT Winter Weather and Heavy Precipitation Forecasting
Brewster
Testbeds
search_activity
2022
Level 3
The goal of this project is to improve the application of CAM ensembles to provide guidance for forecasts of snowfall in the winter and rainfall in the warm season through developingv nd testing two methods for finding consensus and probabilistic forecasts from a set of CAM forecasts.
Keith Brewster, Nathan Snook, Timothy Supinie, Ming Xue
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
To continue producing CAM ensembles in real-time for the Hydrometeorology Testbed (HMT) Winter Weather Experiment (WWE) and Flash Flood and Intense Rainfall (FFaIR) experiments, utilizing the FV3-LAM regional model while expanding the number and complexity of machine learning post-processing products for ensemble-based quantitative precipitation forecast (QPF) and snowfall guidance.
This project directly addresses several NOAA testbed priorities, namely 1) HMT-1, "Improve probabilistic winter precipitation forecasts...", by validating CAM ensemble and individual FV3- LAM members run out to Day 3 in winter, and developing and testing novel ensemble post-processing techniques such as SAM and two ML techniques for snowfall and WSSI forecasts; 2) HMT-2, "Im- prove flash flood monitoring and forecasting..." by validating CAM ensemble and individual FV3- LAM members run out to Day 3 in flash flood season, and developing and testing novel ensemble post-processing techniques such as SAM and two ML techniques for short-duration QPF; and 3) HMT-3: "Enhance forecaster use of probabilistic information ... and assessing and communicating human impacts" through use of probabilistic forecasts from CAM ensembles and by applying ML to forecast the WSSI.
WPC
Kirstin Harnos - kirstin.harnos@noaa.gov
Center for Analysis and Prediction of Storms (CAPS)/University of Oklahoma
NA22OAR4590522
Water Extremes, Winter Weather
Oklahoma
Testbeds
August 2022 - July 2026
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting
fv3 lam cam ensemble forecast system and improving ensemble probabilistic and consensus forecast products in support of hmt winter weather and heavy precipitation forecasting, the goal of this project is to improve the application of cam ensembles to provide guidance for forecasts of snowfall in the winter and rainfall in the warm season through developingv nd testing two methods for finding consensus and probabilistic forecasts from a set of cam forecasts, to continue producing cam ensembles in real time for the hydrometeorology testbed hmt winter weather experiment wwe and flash flood and intense rainfall ffair experiments utilizing the fv3 lam regional model while expanding the number and complexity of machine learning post processing products for ensemble based quantitative precipitation forecast qpf and snowfall guidance
Weather Prediction Center (WPC)
Center for Analysis and Prediction of Storms (CAPS)/University of Oklahoma
The goal of this project is to improve the application of CAM ensembles to provide guidance for forecasts of snowfall in the winter and rainfall in the warm season through developingv nd testing two methods
5
Enhancing probabilistic machine-learned guidance and verification of excessive rainfall and flash floods
Hill
Testbeds
search_activity
2025
Level 3
This project will enhance a machine learning (ML)-based excessive rainfall
Aaron Hill, Russ Schumacher, Antonia G. Sebastian
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Decision-Making, Decision Support
HMT
Global Ensemble Forecast System (GEFS)
This project aims to improve a machine learning (ML)-based excessive rainfall forecast system by enhancing historical datasets for training and verification, and incorporating new predictors related to atmospheric and antecedent conditions that influence flood risk. The goal is to generate more skillful and reliable forecasts, which will be evaluated in a NOAA testbed, ultimately benefiting forecasters at the Weather Prediction Center and other NOAA entities.
The integration of impact-based datasets into an
WPC
University of Oklahoma
Colorado State U.
University of North Carolina at Chapel Hill
Artificial Intelligence (AI) / Machine Learning (ML), Decision-Making, Decision Support
enhancing probabilistic machine learned guidance and verification of excessive rainfall and flash floods, this project will enhance a machine learning ml based excessive rainfall, this project aims to improve a machine learning ml based excessive rainfall forecast system by enhancing historical datasets for training and verification and incorporating new predictors related to atmospheric and antecedent conditions that influence flood risk the goal is to generate more skillful and reliable forecasts which will be evaluated in a noaa testbed ultimately benefiting forecasters at the weather prediction center and other noaa entities
Weather Prediction Center (WPC)
University of Oklahoma, Colorado State U., University of North Carolina at Chapel Hill
This project will enhance a machine learning (ML)-based excessive rainfall
0
Probabilistic prediction of tropical cyclone rapid intensification using satellite passive microwave imagery
Rozoff
Testbeds
check_circle
2015
Level 3
environment and antecedent conditions that contribute to enhanced flood risk.
A suite of probabilistic RI models [including an updated version of the Statistical Hurricane Intensity Prediction System (SHIPS) Rapid Intensification Index (RII)] will be employed to examine potential environmental and storm structure predictors based on satellite passive microwave imagery (MI). This project builds on recently completed research funded through the JHT that demonstrates objectively determined MI predictors increase the skill of probabilistic RI prediction. Our proposed study will focus on testing these findings in an operational environment and innovating on RI models to better account for TC structure.
damage information will better characterize vulnerability and enhance our understanding of
probabilistic prediction of tropical cyclone rapid intensification using satellite passive microwave imagery, environment and antecedent conditions that contribute to enhanced flood risk, a suite of probabilistic ri models [including an updated version of the statistical hurricane intensity prediction system ships rapid intensification index rii ] will be employed to examine potential environmental and storm structure predictors based on satellite passive microwave imagery mi this project builds on recently completed research funded through the jht that demonstrates objectively determined mi predictors increase the skill of probabilistic ri prediction our proposed study will focus on testing these findings in an operational environment and innovating on ri models to better account for tc structure
National Hurricane Center (NHC)
CIMSS/University of Wisconsin
environment and antecedent conditions that contribute to enhanced flood risk.
0
Improvements in Operational Statistical Tropical Cyclone Forecast Models
Chirokova
Testbeds
check_circle
2015
Level 3
Improvements in Operational Statistical Tropical Cyclone Forecast Models
Fri Aug 31 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting
JHT
This proposal seeks to complete a number of upgrades to SHIPS, LGEM, and the RII, many of which are based on NHC feedback over the past few hurricane seasons. Three tasks are proposed. These include 1) replacing weekly SSTs with 1 resolution with daily SST with 0.25 resolution, 2) adding a physical mechanism to account for storm-induces SST cooling, and 3) adding forecasts of TC structure (wind radii and MSLP) to SHIPS/LGEM.
conditions that have led to past flood impacts.
NHC
N/A
CIRA/Colorado State University
NA15OAR4590204
Tropical Cyclones
Colorado
Testbeds
September 2015 - August 2018
Atmospheric Physics, Dynamics and Nesting
improvements in operational statistical tropical cyclone forecast models, improvements in operational statistical tropical cyclone forecast models, this proposal seeks to complete a number of upgrades to ships lgem and the rii many of which are based on nhc feedback over the past few hurricane seasons three tasks are proposed these include 1 replacing weekly ssts with 1 resolution with daily sst with 0 25 resolution 2 adding a physical mechanism to account for storm induces sst cooling and 3 adding forecasts of tc structure wind radii and mslp to ships lgem
National Hurricane Center (NHC)
CIRA/Colorado State University
Improvements in Operational Statistical Tropical Cyclone Forecast Models
0
Improved Eyewall Replacement Cycle Forecasting Using a Modified Microwave-Based Algorithm (ARCHER)
Wimmers
Testbeds
check_circle
2015
Level 3
Improved Eyewall Replacement Cycle Forecasting Using a Modified Microwave-Based Algorithm (ARCHER)
Fri Aug 31 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Verification & Validation
JHT
A simple modification to the eyewall-finding component of this algorithm can produce statistics on the presence of multiple eyewalls, and even pre-eyewall patterns. This information has great potential to complement the existing pERC (probability of ERC) model, which presently runs within the SHIPS operational model. By utilizing the objective identification of multiple eyewalls in microwave imagery, this project will enable more accurate forecasting of hurricane intensity during an ERC.
Develop new models, based on microwave satellite imagery, that will provide diagnostic and forecast guidance prior to and during hurricane eyewall replacement cycles.
NHC
N/A
CIMSS/University of Wisconsin
NA15OAR4590197
Tropical Cyclones
Wisconsin
Testbeds
September 2015 - August 2018
Verification & Validation
improved eyewall replacement cycle forecasting using a modified microwave based algorithm archer, improved eyewall replacement cycle forecasting using a modified microwave based algorithm archer, a simple modification to the eyewall finding component of this algorithm can produce statistics on the presence of multiple eyewalls and even pre eyewall patterns this information has great potential to complement the existing perc probability of erc model which presently runs within the ships operational model by utilizing the objective identification of multiple eyewalls in microwave imagery this project will enable more accurate forecasting of hurricane intensity during an erc
National Hurricane Center (NHC)
CIMSS/University of Wisconsin
Improved Eyewall Replacement Cycle Forecasting Using a Modified Microwave-Based Algorithm (ARCHER)
1
Improvement and Implementation of the Probability-based Microwave Ring Rapid Intensification Index for NHC/JTWC Forecast Basins
Jiang
Testbeds
check_circle
2015
Level 3
Improvement and Implementation of the Probability-based Microwave Ring Rapid Intensification Index for NHC/JTWC Forecast Basins
To provide guidance for tropical cyclone (TC) rapid intensification (RI) forecasts, an automatic 37 GHz ring RI index has been developed and tested under the PI's previous FY-11 and FY-13 JHT projects. This satellite-based RI index mainly addresses the internal effect by precipitation and convection in the inner core region and is implemented on top of the environmental based Statistical Hurricane Prediction Scheme RI Index (SHIPS RII). The major product of our FY-11 JHT project was a "yes" and "no" type of RI forecast only for 30 kt/24 hr intensity increase. During FY-13 funding years, the product was upgraded to a probability-based RI index by adding three 85 GHz predictors. This product was developed and tested in real-time at the National Hurricane Center (NHC) for Atlantic (ATL) and Eastern & Central North Pacific (EPA) basins. A further improvement is proposed here based on the results of Jiang et al. who demonstrated a more in-depth quantification of different colors (green/cyan/pink) on the Navy Research Lab (NRL) 37 GHz color images.
This new enhancement improves the ring detection algorithm.
NHC
N/A
Florida International University
CIRA/Colorado State University
improvement and implementation of the probability based microwave ring rapid intensification index for nhc jtwc forecast basins, improvement and implementation of the probability based microwave ring rapid intensification index for nhc jtwc forecast basins, to provide guidance for tropical cyclone tc rapid intensification ri forecasts an automatic 37 ghz ring ri index has been developed and tested under the pi's previous fy 11 and fy 13 jht projects this satellite based ri index mainly addresses the internal effect by precipitation and convection in the inner core region and is implemented on top of the environmental based statistical hurricane prediction scheme ri index ships rii the major product of our fy 11 jht project was a "yes" and "no" type of ri forecast only for 30 kt 24 hr intensity increase during fy 13 funding years the product was upgraded to a probability based ri index by adding three 85 ghz predictors this product was developed and tested in real time at the national hurricane center nhc for atlantic atl and eastern central north pacific epa basins a further improvement is proposed here based on the results of jiang et al who demonstrated a more in depth quantification of different colors green cyan pink on the navy research lab nrl 37 ghz color images
National Hurricane Center (NHC)
Florida International University, CIRA/Colorado State University
Improvement and Implementation of the Probability-based Microwave Ring Rapid Intensification Index for NHC/JTWC Forecast Basins
10
Guidance on Observational Undersampling over the Tropical Cyclone Lifecycle
Nolan
Testbeds
check_circle
2015
Level 3
Guidance on Observational Undersampling over the Tropical Cyclone Lifecycle
Fri Aug 31 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Model & Forecast Guidance, Satellite & Remote Sensing Technologies, Surface Observing Networks
JHT
The goal of this study will be to compute systematic underestimates of hurricane intensity as measured by airborne SFMR instruments, surface observations (such as ships or buoys), and scatterometers. The underlying data sets will be three very high-resolution, high-quality simulations, the realisms of which have already been well documented.
Guidance for forecasters and for postseason analysts as to how to interpret SFMR, scatterometer, and point measurements of surface winds and pressure for differing classes of tropical storms and hurricanes.
NHC
N/A
University of Maryland
NA15OAR4590203
Tropical Cyclones
Maryland
Testbeds
September 2015 - August 2018
Dynamics and Nesting, Model & Forecast Guidance, Satellite & Remote Sensing Technologies, Surface Observing Networks
guidance on observational undersampling over the tropical cyclone lifecycle, guidance on observational undersampling over the tropical cyclone lifecycle, the goal of this study will be to compute systematic underestimates of hurricane intensity as measured by airborne sfmr instruments surface observations such as ships or buoys and scatterometers the underlying data sets will be three very high resolution high quality simulations the realisms of which have already been well documented
National Hurricane Center (NHC)
University of Maryland
Guidance on Observational Undersampling over the Tropical Cyclone Lifecycle
1
Passive Microwave Data Exploitation via the NRL Tropical Cyclone Webpage
Bankert
Testbeds
check_circle
2015
Level 3
Passive Microwave Data Exploitation via the NRL Tropical Cyclone Webpage
Sat Sep 30 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Verification & Validation
JHT
Since many of the NRL TC web products were first developed, the goals and needs of the operational TC community has evolved. This proposal provides for these upgraded needs by suggesting multiple upgrades to the passive microwave imagery products on the NRL TC webpage.
Provide enhancements to the real-time tropical cyclone (TC) satellite products on the Naval Research Laboratory (NRL) TC webpage.
NHC
N/A
Naval Research Laboratory
NOAA-OAR-OWAQ-2015-2004200
Tropical Cyclones
DC
Testbeds
September 2015 - September 2017
Verification & Validation
passive microwave data exploitation via the nrl tropical cyclone webpage, passive microwave data exploitation via the nrl tropical cyclone webpage, since many of the nrl tc web products were first developed the goals and needs of the operational tc community has evolved this proposal provides for these upgraded needs by suggesting multiple upgrades to the passive microwave imagery products on the nrl tc webpage
National Hurricane Center (NHC)
Naval Research Laboratory
Passive Microwave Data Exploitation via the NRL Tropical Cyclone Webpage
0
Improvement to the Tropical Cyclone Genesis Index (TCGI)
Dunion
Testbeds
check_circle
2015
Level 3
Improvement to the Tropical Cyclone Genesis Index (TCGI)
Jason Dunion, Andrea Schumacher, John Kaplan, Josh Cossuth
Fri Aug 31 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Verification & Validation
JHT
This current project proposes several new elements with goals to expand and improve the current TCGI model that include extending the TCGI into the eastern and central North Pacific (EPAC and CPAC) basins, refining (testing) current (new) TCGI predictors, and developing a graphical version of TCGI to compliment the text version that is currently being produced.
Expand and improve the current TCGI model that include extending the TCGI into the eastern and central North Pacific (EPAC and CPAC) basins, refining (testing) current (new) TCGI predictors, and developing a graphical version of TCGI to compliment the text version that is currently being produced
NHC
N/A
CIMAS/University of Miami
CIRA/Colorado State University
NOAA/OAR/AOML/HRD
Naval Research Laboratory/Monterey
NA15OAR4590201
NA15OAR4590202
Tropical Cyclones
Colorado, Maryland
Testbeds
September 2015 - August 2018
Atmospheric Physics, Verification & Validation
improvement to the tropical cyclone genesis index tcgi, improvement to the tropical cyclone genesis index tcgi, this current project proposes several new elements with goals to expand and improve the current tcgi model that include extending the tcgi into the eastern and central north pacific epac and cpac basins refining testing current new tcgi predictors and developing a graphical version of tcgi to compliment the text version that is currently being produced
National Hurricane Center (NHC)
CIMAS/University of Miami, CIRA/Colorado State University, NOAA/OAR/AOML/HRD, Naval Research Laboratory/Monterey
Improvement to the Tropical Cyclone Genesis Index (TCGI)
0
Transition of the Coastal and Estuarine Storm Tide Model to an Operatonal Model for Forecasting Storm Surges
Zhang
Testbeds
check_circle
2015
Level 3
Transition of the Coastal and Estuarine Storm Tide Model to an Operatonal Model for Forecasting Storm Surges
Thu Feb 28 2019 03:00:00 GMT-0500 (Eastern Standard Time)
Model & Forecast Guidance, Verification & Validation
JHT
We propose to continue to conduct experimental forecasts during 2015 and 2016 hurricane seasons and transition CEST into an operational model by working with NHC's storm surge unit and the Meteorological Development Laboratory (MDL) of the National Weather Service through the JHT program.
Add an alternative model for cross-validation of SLOSH's forecasts and setting a basis for producing ensemble surge forecasts using multiple surge models.
NHC
N/A
Florida International University
NA15OAR4590198
Tropical Cyclones
Florida
Testbeds
September 2015 - February 2019
Model & Forecast Guidance, Verification & Validation
transition of the coastal and estuarine storm tide model to an operatonal model for forecasting storm surges, transition of the coastal and estuarine storm tide model to an operatonal model for forecasting storm surges, we propose to continue to conduct experimental forecasts during 2015 and 2016 hurricane seasons and transition cest into an operational model by working with nhc's storm surge unit and the meteorological development laboratory mdl of the national weather service through the jht program
National Hurricane Center (NHC)
Florida International University
Transition of the Coastal and Estuarine Storm Tide Model to an Operatonal Model for Forecasting Storm Surges
0
Improvements and extensions to an existing probabilistic genesis forecast tool using an ensemble of global models
Hart
Testbeds
check_circle
2017
Level 3
The goal of this project is to transition a statistical-dynamical tropical cyclone (TC) genesis guidance tool that will provide probabilistic TC genesis forecasts from global model output.
Robert Hart
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting
JHT
The PIs recently completed a JHT project in which they developed and tested a statisticaldynamical tropical cyclone (TC) genesis guidance tool based on forecast output from global numerical models. The tool has been used extensively by NHC hurricane specialists as guidance for the Tropical Weather Outlook (TWO) since 2014. Building on this success, the PIs propose to further enhance this previously developed and operationally tested forecast tool, with specific enhancements and extensions.
The resulting updated tool will be an even more calibrated, skillful and compatible tool for NHC to rely upon for TC genesis guidance well in advance (up to 5 days) of TC formation.
NHC
Brian Zachry
Florida State University
NA17OAR4590141
Tropical Cyclones
Florida
Testbeds
July 2017 - June 2020
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting
improvements and extensions to an existing probabilistic genesis forecast tool using an ensemble of global models, the goal of this project is to transition a statistical dynamical tropical cyclone tc genesis guidance tool that will provide probabilistic tc genesis forecasts from global model output, the pis recently completed a jht project in which they developed and tested a statisticaldynamical tropical cyclone tc genesis guidance tool based on forecast output from global numerical models the tool has been used extensively by nhc hurricane specialists as guidance for the tropical weather outlook two since 2014 building on this success the pis propose to further enhance this previously developed and operationally tested forecast tool with specific enhancements and extensions
National Hurricane Center (NHC)
Florida State University
The goal of this project is to transition a statistical-dynamical tropical cyclone (TC) genesis guidance tool that will provide probabilistic TC genesis forecasts from global model output.
2
Improvements to Operational Statistical Tropical Cyclone Intensity Forecast Models using Wind Structure and Eye Predictors
Chirokova
Testbeds
check_circle
2017
Level 3
This project seeks to improve operational Statistical Hurricane Intensity Prediction Scheme (SHIPS), the Logistic Growth Equation Model (LGEM), the multi-lead time probabilistic Rapid Intensification Index (MLTRII), and the global Rapid Intensification Index (GRII) by 1) adding a tropical cyclone wind structure based predictor or combination of predictors to SHIPS/LGEM, 2) adding a predictor or a group of predictors based on the probability of the eye existence and the code to calculate that probability to SHIPS/LGEM, 3) adding wind structure predictors to both operational rapid intensification indices (RIIs), and 4) adding eye-probability predictors to both versions of the RII.
Galina Chirokova, John Kaplan
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
This proposal seeks to complete a number of upgrades to SHIPS/LGEM and both RIIs, many of which are based on NHC feedback over the past few hurricane seasons.
Adding the predictors based on the probability of the eye-existence to SHIPS/LGEM should very significantly improve the short-term intensity forecasts (6 - 18 hours) that have been the most difficult to improve, and add additional skill to the RI forecasts. Collectively these upgrades and new capabilities will result in an improved suite of statistical-dynamical forecast guidance available at NHC/CPHC and JTWC.
improvements to operational statistical tropical cyclone intensity forecast models using wind structure and eye predictors, this project seeks to improve operational statistical hurricane intensity prediction scheme ships the logistic growth equation model lgem the multi lead time probabilistic rapid intensification index mltrii and the global rapid intensification index grii by 1 adding a tropical cyclone wind structure based predictor or combination of predictors to ships lgem 2 adding a predictor or a group of predictors based on the probability of the eye existence and the code to calculate that probability to ships lgem 3 adding wind structure predictors to both operational rapid intensification indices riis and 4 adding eye probability predictors to both versions of the rii, this proposal seeks to complete a number of upgrades to ships lgem and both riis many of which are based on nhc feedback over the past few hurricane seasons
NCEP Central Operations (NCO)
CIRA/Colorado State University
This project seeks to improve operational Statistical Hurricane Intensity Prediction Scheme (SHIPS), the Logistic Growth Equation Model (LGEM), the multi-lead time probabilistic Rapid Intensification Index (MLTRII), and the global Rapid Intensification Index (GRII) by 1)
1
Evolutionary Programming for Probabilistic Tropical Cyclone Intensity Forecasts
Roebber
Testbeds
check_circle
2017
Level 3
The goal of this project is to advance the Evolutionary Programming (EP) application to tropical cyclone intensity from the latter stages of development to and through the demonstration process.
Paul Roebber
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
The model is developed using SHIPS developmental data between 2000-2015 for the Atlantic and East/Central Pacific basins (using separate models for each basin). Model outputs will include (a) deterministic and probabilistic TC intensity forecasts every 12 h to 168 h and (b) probabilistic forecasts of RI and RW every 12 h to 72 h at the Kaplan et al. (2015) thresholds. A secondary hypothesis to be tested is that Bayesian model combination provides consensus deterministic TC intensity (every 12 h to 168 h) and probabilistic RI (every 12 h to 72 h) forecasts with skill equal to or greater than existing consensus approaches. This project explores the use of machine learning.
The approach may be particularly beneficial to tropical cyclone intensity forecasts as a whole given their improving yet still low forecast skill.
evolutionary programming for probabilistic tropical cyclone intensity forecasts, the goal of this project is to advance the evolutionary programming ep application to tropical cyclone intensity from the latter stages of development to and through the demonstration process, the model is developed using ships developmental data between 2000 2015 for the atlantic and east central pacific basins using separate models for each basin model outputs will include a deterministic and probabilistic tc intensity forecasts every 12 h to 168 h and b probabilistic forecasts of ri and rw every 12 h to 72 h at the kaplan et al 2015 thresholds a secondary hypothesis to be tested is that bayesian model combination provides consensus deterministic tc intensity every 12 h to 168 h and probabilistic ri every 12 h to 72 h forecasts with skill equal to or greater than existing consensus approaches this project explores the use of machine learning
National Hurricane Center (NHC)
University of Wisconsin-Milwaukee
The goal of this project is to advance the Evolutionary Programming (EP) application to tropical cyclone intensity from the latter stages of development to and through the demonstration process.
2
Ensemble-based Pre-genesis Watches and Warnings for Atlantic and North Pacific Tropical Cyclones
Elsberry
Testbeds
check_circle
2017
Level 3
The objective of this project is to provide guidance products, based on the NOAA Global Ensemble Forecast System (GEFS) and the European enter for Medium-range Weather Forecasts (ECMWF) ensemble forecasts, for the National Hurricane Center (NHC), Central Pacific Hurricane Center, and the Joint Typhoon Warning Center (JTWC) production of pre-genesis watches and warnings of Atlantic and eastern North Pacific, Central North Pacific, and western North Pacific.
Russell Elsberry
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance
JHT
Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS), UFS-Medium Range Weather Application (MRWA)
To provide guidance products for the National Hurricane Center (NHC), Central Pacific Hurricane Center, and the Joint Typhoon Warning Center (JTWC) production of pre-genesis watches and warnings of Atlantic and eastern North Pacific, Central North Pacific, and western North Pacific, respectively. These guidance products will be based on the NOAA Global Ensemble Forecast System (GEFS) and the European Center for Medium-range Weather Forecasts (ECMWF) ensemble forecasts and will provide the genesis time and location along the Weighted-Mean Vector Motion (WMVM) ensemble storm tracks, and also the Weighted Analog Intensity Atlantic (WAIA) or corresponding Pacific (WAIP) intensity and intensity spread forecasts to seven days.
Forecasters will be able to issue 5 day (or longer) track forecasts and uncertainty measures with the pre-genesis watches and warnings.
NCO
Brian Zachry
University of Colorado-Colorado Springs
NA17OAR4590139
Tropical Cyclones
Colorado
Testbeds
July 2017 - June 2020
Data Assimilation and Ensembles, Model & Forecast Guidance
ensemble based pre genesis watches and warnings for atlantic and north pacific tropical cyclones, the objective of this project is to provide guidance products based on the noaa global ensemble forecast system gefs and the european enter for medium range weather forecasts ecmwf ensemble forecasts for the national hurricane center nhc central pacific hurricane center and the joint typhoon warning center jtwc production of pre genesis watches and warnings of atlantic and eastern north pacific central north pacific and western north pacific, to provide guidance products for the national hurricane center nhc central pacific hurricane center and the joint typhoon warning center jtwc production of pre genesis watches and warnings of atlantic and eastern north pacific central north pacific and western north pacific respectively these guidance products will be based on the noaa global ensemble forecast system gefs and the european center for medium range weather forecasts ecmwf ensemble forecasts and will provide the genesis time and location along the weighted mean vector motion wmvm ensemble storm tracks and also the weighted analog intensity atlantic waia or corresponding pacific waip intensity and intensity spread forecasts to seven days
NCEP Central Operations (NCO)
University of Colorado-Colorado Springs
The objective of this project is to provide guidance products, based on the NOAA Global Ensemble Forecast System (GEFS) and the European enter for Medium-range Weather Forecasts (ECMWF) ensemble forecasts, for the National Hurricane Center
2
Transition of Machine-Learning Based Rapid Intensification Forecasts to Operations
Mercer
Testbeds
check_circle
2017
Level 3
The primary objective of this project is to include the AI ensemble in the 2018 Joint Hurricane Testbed to assess its performance in real-time, and to ultimately transition the product into operations to complement the SHIPS-RII system.
Andrew Mercer
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Verification & Validation
JHT
The primary objective of the proposed research is to include the AI ensemble in the 2018 Joint Hurricane Testbed to assess its performance in realtime, and to ultimately transition the product into operations to complement the SHIPS-RII system. This is a machine learning project.
Predicting onset and duration of rapid intensification events and providing statistical guidance on guidance.
NHC
Brian Zachry
Mississippi State University
NA17OAR4590140
Tropical Cyclones
Mississippi
Testbeds
July 2017 - June 2020
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Verification & Validation
transition of machine learning based rapid intensification forecasts to operations, the primary objective of this project is to include the ai ensemble in the 2018 joint hurricane testbed to assess its performance in real time and to ultimately transition the product into operations to complement the ships rii system, the primary objective of the proposed research is to include the ai ensemble in the 2018 joint hurricane testbed to assess its performance in realtime and to ultimately transition the product into operations to complement the ships rii system this is a machine learning project
National Hurricane Center (NHC)
Mississippi State University
The primary objective of this project is to include the AI ensemble in the 2018 Joint Hurricane Testbed to assess its performance in real-time, and to ultimately transition the product into operations to complement the
2
Estimation of Tropical Cyclone Intensity Using Satellite Passive Microwave Observations
Jiang
Testbeds
check_circle
2017
Level 3
The purpose of this project is to implement a new algorithm for estimating the current intensity of tropical cyclones using most of the current vailable microwave satellite imagers. The new algorithm is expected to provide microwave based tropical cyclone intensity estimates independent of the existing visible and infrared based Dvorak technique at similar or better accuracy.
Haiyan Jiang
Complete
Sat Jul 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
An operational algorithm to estimate the current intensity of TCs using most of the current available microwave satellite sensors will be developed. The technique builds on the results of Cecil and Zipser (and many others) who demonstrated that the storm intensity is very well correlated with variables associated with 85 GHz brightness temperatures and rain rates in the inner core.
Improve the utility of microwave satellite and radar data for tropical cyclone intensity and location analysis
estimation of tropical cyclone intensity using satellite passive microwave observations, the purpose of this project is to implement a new algorithm for estimating the current intensity of tropical cyclones using most of the current vailable microwave satellite imagers the new algorithm is expected to provide microwave based tropical cyclone intensity estimates independent of the existing visible and infrared based dvorak technique at similar or better accuracy, an operational algorithm to estimate the current intensity of tcs using most of the current available microwave satellite sensors will be developed the technique builds on the results of cecil and zipser and many others who demonstrated that the storm intensity is very well correlated with variables associated with 85 ghz brightness temperatures and rain rates in the inner core
National Hurricane Center (NHC)
Florida International University
The purpose of this project is to implement a new algorithm for estimating the current intensity of tropical cyclones using most of the current vailable microwave satellite imagers. The new algorithm is expected to provide
7
Transitioning Ensemble-based TC Track and Intensity Sensitivity to Operations
Torn
Testbeds
check_circle
2019
Level 3
The project involves evaluating the potential value of real-time ensemble-based sensitivity and targeting information for use in determining optimal observation sampling strategies for reducing the uncertainty in TC track forecasts using ECMWF ensemble forecast output.
Thu Jun 30 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles
JHT
The goal of this project is to implement a modern, ensemble-based operational product that National Hurricane Center (NHC) forecasters could use to determine the locations for dropsondes and supplemental rawinsonde profiles over land that could subsequently reduce model uncertainty in forecasts of both TC track and intensity.
Reduce model uncertainty leading to better forecasts
NHC
Wallace Hogsett
SUNY Albany
NOAA/OAR/AOML
University of Miami
NA19OAR4590129
Tropical Cyclones
Florida, New York
Testbeds
July 2019 - June 2022
Data Assimilation and Ensembles
transitioning ensemble based tc track and intensity sensitivity to operations, the project involves evaluating the potential value of real time ensemble based sensitivity and targeting information for use in determining optimal observation sampling strategies for reducing the uncertainty in tc track forecasts using ecmwf ensemble forecast output, the goal of this project is to implement a modern ensemble based operational product that national hurricane center nhc forecasters could use to determine the locations for dropsondes and supplemental rawinsonde profiles over land that could subsequently reduce model uncertainty in forecasts of both tc track and intensity
National Hurricane Center (NHC)
SUNY Albany, NOAA/OAR/AOML, University of Miami
The project involves evaluating the potential value of real-time ensemble-based sensitivity and targeting information for use in determining optimal observation sampling strategies for reducing the uncertainty in TC track forecasts using ECMWF ensemble forecast output.
2
Upgrades to the M-PERC and PERC Models to Improve Short Term Tropical Cyclone Intensity Forecasts
Herndon
Testbeds
check_circle
2019
Level 3
The goal of this project is to improve short-term intensity forecasts of tropical cyclones by further developing the Microwave Probability of Eyewall Replacement Cycle (M-PERC) model to be used in other NOAA ocean basins outside of the Atlantic region.
Use an existing satellite tool that uses satellite microwave 89 GHz imagery to estimate Eyewall Replacement cycle (ERC) onset in tropical cyclones.
Incremental improvements in tropical cyclone intensity forecasts and reduce the errors associated with intensity changes during the eyewall replacement cycle that are problematic for forecasters.
upgrades to the m perc and perc models to improve short term tropical cyclone intensity forecasts, the goal of this project is to improve short term intensity forecasts of tropical cyclones by further developing the microwave probability of eyewall replacement cycle m perc model to be used in other noaa ocean basins outside of the atlantic region, use an existing satellite tool that uses satellite microwave 89 ghz imagery to estimate eyewall replacement cycle erc onset in tropical cyclones
National Hurricane Center (NHC)
CIMSS/University of Wisconsin
The goal of this project is to improve short-term intensity forecasts of tropical cyclones by further developing the Microwave Probability of Eyewall Replacement Cycle (M-PERC) model to be used in other NOAA ocean basins outside
1
Further improvements and extensions to the tropical cyclone logistical guidance for genesis (TCLOGG)
Hart
Testbeds
check_circle
2019
Level 3
To further expand and improve upon the the Tropical Cyclone Logistical Guidance for Genesis (TCLOGG) guidance tool.
Fri Jun 30 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Model & Forecast Guidance, Verification & Validation
JHT
To further expand and improve upon an existing forecast guidance tool for tropical cyclone formation that uses well-calibrated numerical model output
Improved guidence for tropical cyclone formation
NHC
Wallace Hogsett
Florida State University;
Embry-Riddle Aeronautical University
NA19OAR4590134
NA19OAR4590135
Tropical Cyclones
Florida
Testbeds
July 2019 - June 2023
Atmospheric Physics, Model & Forecast Guidance, Verification & Validation
further improvements and extensions to the tropical cyclone logistical guidance for genesis tclogg, to further expand and improve upon the the tropical cyclone logistical guidance for genesis tclogg guidance tool, to further expand and improve upon an existing forecast guidance tool for tropical cyclone formation that uses well calibrated numerical model output
National Hurricane Center (NHC)
Florida State University;, Embry-Riddle Aeronautical University
To further expand and improve upon the the Tropical Cyclone Logistical Guidance for Genesis (TCLOGG) guidance tool.
0
Forecaster Support Products for Analysis of Tropical Cyclone Intensity and Structure from Aircraft Reconnaissance Observations
Vigh
Testbeds
search_activity
2022
Level 3
This project focuses on operationally implementing integrated aircraft reconnaissance analysis products that improve the spatial and temporal analysis of the surface wind field in tropical cyclones (TCs).
Jonathan Vigh, Michael Bell, Jun Zhang, Eric Hendricks, Christopher Rozoff
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
This proposed effort seeks to operationally implement aircraft reconnaissance products to
optimize the spatial and temporal analysis of the surface wind field in TCs. The main project
outcomes will be a real-time suite of observational analysis products that provide high quality
spatial and temporal analysis of the TC wind field from 0-3 km in height, graphical and tabular
outputs of the estimated VMAX, RMW, and wind radii information along with the uncertainty
associated with the estimates. The provided products will allow forecasters to analyze TC wind
structure more quickly and with greater confidence. Through demonstration of the analysis
products in an operational environment and at least two submitted journal articles, the long-term
societal benefits of this project will result in a measurable decrease in NHC forecaster workload
and improvements in forecasters' confidence, as well as improvements to hurricane track,
structure, and intensity forecasts.
Below, we summarize the key project outputs, provide examples of how the products could look, and describe readiness levels of our techniques and codes. Further details concerning the performance and methodologies are given in sections (e) and (f) below. The primary outcomes of this proposed work will include:
1. A real-time suite of observational analysis products will provide high quality spatial and temporal analysis of the TC wind field from the surface up to ~3 km height.
2. Graphical and tabular outputs of the estimated VMAX, RMW, and wind radii information that will allow forecasters to analyze TC intensity and structure more quickly and with greater confidence.
3. This automated analysis system will be demonstrated in an operational environment so that it can be readily incorporated into forecast support systems used by NHC forecasters.
4. Two journal manuscripts will be submitted for peer-reviewed publication: 1) a paper
describing and evaluating the real-time aircraft analyses; 2) a paper describing the unified parameterization for the surface wind reduction.
forecaster support products for analysis of tropical cyclone intensity and structure from aircraft reconnaissance observations, this project focuses on operationally implementing integrated aircraft reconnaissance analysis products that improve the spatial and temporal analysis of the surface wind field in tropical cyclones tcs, this proposed effort seeks to operationally implement aircraft reconnaissance products to optimize the spatial and temporal analysis of the surface wind field in tcs the main project outcomes will be a real time suite of observational analysis products that provide high quality spatial and temporal analysis of the tc wind field from 0 3 km in height graphical and tabular outputs of the estimated vmax rmw and wind radii information along with the uncertainty associated with the estimates the provided products will allow forecasters to analyze tc wind structure more quickly and with greater confidence through demonstration of the analysis products in an operational environment and at least two submitted journal articles the long term societal benefits of this project will result in a measurable decrease in nhc forecaster workload and improvements in forecasters' confidence as well as improvements to hurricane track structure and intensity forecasts
National Hurricane Center (NHC)
National Center for Atmospheric Research, Colorado State University, University of Miami
This project focuses on operationally implementing integrated aircraft reconnaissance analysis products that improve the spatial and temporal analysis of the surface wind field in tropical cyclones (TCs).
23
A Machine Learning Model for Estimating Tropical Cyclone Track and Intensity Forecast Uncertainty
DeMaria
Testbeds
search_activity
2022
Level 3
The primary goal of this research and development project is to use machine learning methods to estimate TC track and intensity forecast uncertainty to improve probabilistic hazard products for the National Hurricane Center (NHC), Central Pacific Hurricane Center (CPHC) and Joint Typhoon Warning Center (JTWC).
Mark DeMaria, Elizabeth Barnes, Galina Chirokova, LIxin Lu
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Probabilistic Weather Forecasting, Verification & Validation
HOT
Tropical cyclone (TC) hazards can occur well outside of the operational track forecast, making it essential to provide uncertainty information with the National Hurricane Center (NHC), Central Pacific Hurricane Center (CPHC), and Joint Typhoon Warning Center (JTWC) deterministic TC forecasts to assist emergency managers with mitigation activities. A serious limitation of the current operational uncertainty products is that the underlying probabilities are derived from historical forecast error distributions. We propose to adapt a new
machine learning method developed by Dr. Elizabeth Barnes and collaborators to estimate TC
track and intensity forecast error uncertainty using an abstention neural network technique based
on a multi-model ensemble and TC environmental parameters. A fundamental difference
between this approach, referred to as the Tropical Cyclone Abstention Network (TCAN)
model, and existing operational techniques is that the uncertainty estimates are obtained as
part of the network training, rather than from post-analysis of forecast errors. TCAN builds
on the NHC's operational Neural Network Intensity Consensus (NNIC) model and predicts the
full conditional distributions of track and intensity that depend on the specific forecast situation.
TCAN has been developed for NHC and CPHC, is compatible with the existing NHC's operational
environment, and will be demonstrated in real-time, in close coordination with the Joint
Hurricane Testbed (JHT). TCAN will be also adapted to the western North Pacific for JTWC.
The TCAN guidance on guidance model will produce track and
intensity forecasts and measures of uncertainty based on the conditional distributions predicted by the neural networks. Special emphasis will be placed on testing in JHT all components of the system, including the database development, model training, forecast components, product generation, and validation to maximize the prospects for operational transition if the project is successful. Past criticism of the use of neural networks for forecasting was that they are "black boxes", but methods now exist to help explain the network's decision. The products will also include output from explainable artificial intelligence (AI) methods to provide forecasters with
information about which aspects of the TCAN input were most relevant for its prediction.
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Probabilistic Weather Forecasting, Verification & Validation
a machine learning model for estimating tropical cyclone track and intensity forecast uncertainty, the primary goal of this research and development project is to use machine learning methods to estimate tc track and intensity forecast uncertainty to improve probabilistic hazard products for the national hurricane center nhc central pacific hurricane center cphc and joint typhoon warning center jtwc, tropical cyclone tc hazards can occur well outside of the operational track forecast making it essential to provide uncertainty information with the national hurricane center nhc central pacific hurricane center cphc and joint typhoon warning center jtwc deterministic tc forecasts to assist emergency managers with mitigation activities a serious limitation of the current operational uncertainty products is that the underlying probabilities are derived from historical forecast error distributions we propose to adapt a new machine learning method developed by dr elizabeth barnes and collaborators to estimate tc track and intensity forecast error uncertainty using an abstention neural network technique based on a multi model ensemble and tc environmental parameters a fundamental difference between this approach referred to as the tropical cyclone abstention network tcan model and existing operational techniques is that the uncertainty estimates are obtained as part of the network training rather than from post analysis of forecast errors tcan builds on the nhc's operational neural network intensity consensus nnic model and predicts the full conditional distributions of track and intensity that depend on the specific forecast situation tcan has been developed for nhc and cphc is compatible with the existing nhc's operational environment and will be demonstrated in real time in close coordination with the joint hurricane testbed jht tcan will be also adapted to the western north pacific for jtwc
National Hurricane Center (NHC)
CIRA/Colorado State University
The primary goal of this research and development project is to use machine learning methods to estimate TC track and intensity forecast uncertainty to improve probabilistic hazard products for the National Hurricane Center (NHC), Central
3
Expansion of Ensemble-based Sensitivity to TC Hazard Forecasts
Torn
Testbeds
search_activity
2022
Level 3
The purpose of this project is to expand upon the quasi-operational ensemble-based product that has been employed by NHC forecasters to determine the locations where supplemental observations could reduce model uncertainty in TC track, to intensity, maximum wind and precipitation.
Ryan Torn
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Data Assimilation and Ensembles, Verification & Validation
HOT
For over 20 years, NOAA's National Hurricane Center (NHC) has tasked the Gulfstream
G-IV to perform Synoptic Surveillance missions, primarily aimed to reducing uncertainty in
tropical cyclone (TC) track. The increasing skill and sophistication of numerical models and shift
toward more impacts-based forecasting may require developing additional new techniques that
identify where additional targeted observations, such as from aircraft or extra rawinsondes, could
improve forecasts of other aspects of the TC, such as intensity, wind, and precipitation. Here, we
propose to expand upon the quasi-operational ensemble-based product that has been employed by
NHC forecasters to determine the locations where supplemental observations could reduce model
uncertainty in TC track, to intensity, maximum wind and precipitation, thereby addressing JHT
Priority 2.
The outcome of this work will be an expanded guidance suite that can be employed to
understand the sensitivity of TC forecasts to various aspects of the modeling system and where supplemental observations might reduce the uncertainty in specific forecast metrics. The current guidance suite provides information for TC track; however, the expanded guidance suite would provide sensitivity information for individual forecast metrics, such as track, intensity, wind speed, and precipitation, as well as combinations of these. In turn, this would allow NHC to develop observation strategies that could address uncertainty in forecasts of societal-based impacts, such
was wind and precipitation. Improved forecasts will yield more timely watches and warnings, raising situational awareness of both forecasters and the public of the hazards specific to a storm, which in turn will result in reduced socioeconomic impact and additional time to prepare for potential impacts of landfalling TCs.
Aircraft Reconnaissance, Data Assimilation and Ensembles, Verification & Validation
expansion of ensemble based sensitivity to tc hazard forecasts, the purpose of this project is to expand upon the quasi operational ensemble based product that has been employed by nhc forecasters to determine the locations where supplemental observations could reduce model uncertainty in tc track to intensity maximum wind and precipitation, for over 20 years noaa's national hurricane center nhc has tasked the gulfstream g iv to perform synoptic surveillance missions primarily aimed to reducing uncertainty in tropical cyclone tc track the increasing skill and sophistication of numerical models and shift toward more impacts based forecasting may require developing additional new techniques that identify where additional targeted observations such as from aircraft or extra rawinsondes could improve forecasts of other aspects of the tc such as intensity wind and precipitation here we propose to expand upon the quasi operational ensemble based product that has been employed by nhc forecasters to determine the locations where supplemental observations could reduce model uncertainty in tc track to intensity maximum wind and precipitation thereby addressing jht priority 2
National Hurricane Center (NHC)
SUNY Albany
The purpose of this project is to expand upon the quasi-operational ensemble-based product that has been employed by NHC forecasters to determine the locations where supplemental observations could reduce model uncertainty in TC track, to
8
The Impact of Targeted Synoptic Dropsondes on Tropical Cyclone Forecasts in HAFS
Ditchek
Testbeds
search_activity
2022
Level 3
The goal of this project is to quantify the impact of NOAA's Gulfstream IV-SP (G-IV) on tropical cyclone (TC) forecasts of track, intensity, and significant wind radii by using HAFS to assess 1) the overall G-IV impact as well as 2) the impact of non-standard G-IV flight patterns that were operationally implemented in 2019.
Sarah Ditchek
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Dec 31 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Verification & Validation
HOT
Hurricane Analysis and Forecast System (HAFS), Hurricane Weather Research and Forecasting (HWRF), Unified Forecast System (UFS)
This proposal aims to quantify the impact of these targeted synoptic dropsondes
launched from NOAA's G-IV on TC forecasts of track, intensity, and storm size, and to further
optimize their associated dropsonde-sampling strategy. To quantify their impact, OSEs will be
conducted where targeted synoptic dropsondes from storms between 2019-2021 are included,
modified, or denied in an otherwise operationally-consistent model. This project will use HAFS,
the next generation TC model within the Unified Forecast System (UFS) framework, which will
be replacing the currently operational Hurricane Weather Research and Forecasting (HWRF)
Model. By conducing this research with HAFS, results can be more easily translated to operations
as results will be consistent with the latest operational model implementation.
Currently, the real-time product used by NHC to identify sensitive regions
of the TC's synoptic environment is applied to those storms that will have G-IV flights. However, this strategy does not consider 1) when multiple storms are in the basin, 2) current and anticipated intensity, 3) shear environment, and 4) latitude. With the proposed 2019-2021 sample, stratifications will be taken that identify regimes where the targeted synoptic dropsondes perform best. For example, is there more impact to forecasts when targeted synoptic dropsondes sample storms of a particular intensity? These results, presented and delivered to JHT and NHC, will advance the current application of the sensitivity technique.
the impact of targeted synoptic dropsondes on tropical cyclone forecasts in hafs, the goal of this project is to quantify the impact of noaa's gulfstream iv sp g iv on tropical cyclone tc forecasts of track intensity and significant wind radii by using hafs to assess 1 the overall g iv impact as well as 2 the impact of non standard g iv flight patterns that were operationally implemented in 2019, this proposal aims to quantify the impact of these targeted synoptic dropsondes launched from noaa's g iv on tc forecasts of track intensity and storm size and to further optimize their associated dropsonde sampling strategy to quantify their impact oses will be conducted where targeted synoptic dropsondes from storms between 2019 2021 are included modified or denied in an otherwise operationally consistent model this project will use hafs the next generation tc model within the unified forecast system ufs framework which will be replacing the currently operational hurricane weather research and forecasting hwrf model by conducing this research with hafs results can be more easily translated to operations as results will be consistent with the latest operational model implementation
National Hurricane Center (NHC)
CIMAS/University of Miami
The goal of this project is to quantify the impact of NOAA's Gulfstream IV-SP (G-IV) on tropical cyclone (TC) forecasts of track, intensity, and significant wind radii by using HAFS to assess 1) the overall
0
Artificial Intelligence Weather Prediction Models for Operational Tropical Cyclone Forecasting
Musgrave
Testbeds
search_activity
2025
Level 3
The goal of this project is to evaluate Artificial Intelligence Weather Prediction (AIWP) models
for use in tropical cyclone (TC) forecasting applications and provide AIWP-based TC guidance
products, including track, intensity, environment, genesis, consensus, and ensemble-based, to the
National Hurricane Center (NHC), and a framework for AIWP model evaluation.
Kate Musgrave
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Post-processing
HOT
Advanced Weather Interactive Processing System (AWIPS)
This project aims to evaluate Artificial Intelligence Weather Prediction (AIWP) models for tropical cyclone (TC) forecasting and provide AIWP-based guidance products, including track, intensity, and genesis forecasts, to the National Hurricane Center (NHC). It will also develop a framework for AIWP model evaluation and improve existing consensus forecasts by incorporating AIWP model outputs.
This project will evaluate the direct use of AIWP models, improve consensus forecasts, use ensembles to estimate forecast uncertainty and provide a framework for evaluation of AIWP models.
NHC
CIRA/Colorado State
University
NA25OARX459C0200-T1-01
Tropical Cyclones
Colorado
Testbeds
August 2025 - July 2028
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Post-processing
artificial intelligence weather prediction models for operational tropical cyclone forecasting, the goal of this project is to evaluate artificial intelligence weather prediction aiwp models for use in tropical cyclone tc forecasting applications and provide aiwp based tc guidance products including track intensity environment genesis consensus and ensemble based to the national hurricane center nhc and a framework for aiwp model evaluation, this project aims to evaluate artificial intelligence weather prediction aiwp models for tropical cyclone tc forecasting and provide aiwp based guidance products including track intensity and genesis forecasts to the national hurricane center nhc it will also develop a framework for aiwp model evaluation and improve existing consensus forecasts by incorporating aiwp model outputs
National Hurricane Center (NHC)
CIRA/Colorado State, University
The goal of this project is to evaluate Artificial Intelligence Weather Prediction (AIWP) models for use in tropical cyclone (TC) forecasting applications and provide AIWP-based TC guidance products, including track, intensity, environment, genesis, consensus, and
0
Modernizing Flight Planning Software for Hurricane Reconnaissance Missions
Dunion
Testbeds
search_activity
2025
Level 3
The primary goal of this project is to develop next generation aircraft flight
planning software for use by NOAA's National Hurricane Center (NHC) and NOAA laboratories
(e.g., AOML/HRD) that use NOAA aircraft to conduct research.
Jason Dunion, Alan Brammer
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Visualizations, Decision Support
HOT
This project aims to develop next-generation, modernized flight planning software for NOAA's National Hurricane Center (NHC) and other NOAA laboratories (e.g., AOML/HRD) that utilize NOAA aircraft for research and operational hurricane reconnaissance missions. The new software will be a user-friendly, python-based package that addresses current limitations, supports future aircraft types (like the NOAA G-550 and C-130s), streamlines the flight planning process, reduces errors, and improves efficiency during high-impact weather events.
This project will produce new flight planning software that will enhance flight planning capabilities and streamline flight planning for operational and research stakeholders who utilize hurricane reconnaissance aircraft. The improvements to the software will reduce errors and improve efficiency during high-impact
weather events.
NHC
University of Miami/CIMAS
Colorado State University/CIRA
NA25OARX459C0186-T1-01
NA25OARX459C0187-T1-01
Tropical Cyclones
Colorado, Florida
Testbeds
August 2025 - July 2028
Artificial Intelligence (AI) / Machine Learning (ML), Visualizations, Decision Support
modernizing flight planning software for hurricane reconnaissance missions, the primary goal of this project is to develop next generation aircraft flight planning software for use by noaa's national hurricane center nhc and noaa laboratories e g aoml hrd that use noaa aircraft to conduct research, this project aims to develop next generation modernized flight planning software for noaa's national hurricane center nhc and other noaa laboratories e g aoml hrd that utilize noaa aircraft for research and operational hurricane reconnaissance missions the new software will be a user friendly python based package that addresses current limitations supports future aircraft types like the noaa g 550 and c 130s streamlines the flight planning process reduces errors and improves efficiency during high impact weather events
National Hurricane Center (NHC)
University of Miami/CIMAS, Colorado State University/CIRA
The primary goal of this project is to develop next generation aircraft flight
planning software for use by NOAA's National Hurricane Center (NHC) and NOAA laboratories
(e.g., AOML/HRD) that use NOAA aircraft to conduct research.
0
Forecasting and Verification for the Fire Weather Testbed
Hilburn
Testbeds
search_activity
2025
Level 3
The primary goal is to demonstrate and validate WRF-SFIRE forecasts initialized
with Next Generation Fire System (NGFS) ignition points through the Fire Weather Testbed.
This will involve development of a Recommender tool to integrate the forecasts into Hazard
Services, collecting user feedback, and statistical validation.
Kyle Hilburn
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Verification & Validation, Visualizations, Decision Support
FWT
Advanced Weather Interactive Processing System (AWIPS), Weather Research and Forecasting Model - Fire (WRF-Fire)
This project aims to enhance wildfire forecasting capabilities by demonstrating and validating WRF-SFIRE forecasts, initialized with Next Generation Fire System (NGFS) ignition points, within the Fire Weather Testbed. Key activities include integrating forecasts into Hazard Services through a Recommender tool, collecting user feedback, and conducting statistical validation. The project will deliver an operations-ready forecasting system, high-resolution fire spread and smoke forecasts, a forecast verification database, uncertainty quantification, and user feedback.
This project will provide NOAA the opportunity to demonstrate fire forecasting capabilities in the FWT. This will clarify the needs and priorities of operational users, and we expect the fire modeling community will benefit from the lessons learned at the FWT.
GSL
CIRA/Colorado State University
NA25OARX459C0155-T1-01
Fire Weather
Colorado
Testbeds
August 2025 - July 2028
Data Assimilation and Ensembles, Verification & Validation, Visualizations, Decision Support
forecasting and verification for the fire weather testbed, the primary goal is to demonstrate and validate wrf sfire forecasts initialized with next generation fire system ngfs ignition points through the fire weather testbed this will involve development of a recommender tool to integrate the forecasts into hazard services collecting user feedback and statistical validation, this project aims to enhance wildfire forecasting capabilities by demonstrating and validating wrf sfire forecasts initialized with next generation fire system ngfs ignition points within the fire weather testbed key activities include integrating forecasts into hazard services through a recommender tool collecting user feedback and conducting statistical validation the project will deliver an operations ready forecasting system high resolution fire spread and smoke forecasts a forecast verification database uncertainty quantification and user feedback
CIRA/Colorado State University
The primary goal is to demonstrate and validate WRF-SFIRE forecasts initialized with Next Generation Fire System (NGFS) ignition points through the Fire Weather Testbed. This will involve development of a Recommender tool to integrate the
0
A Convection-allowing Ensemble System for Short-term Probabilistic Forecasts of Severe Convective Hazards Based on the MPAS Dynamic Core
Yussouf
Testbeds
search_activity
2025
Level 3
Evaluate a MPAS-based data assimilation (DA) and prediction system in HWT and HMT experiments for short-term probabilistic guidance of severe thunderstorms. Migrate from the Data Assimilation Research Testbed (DART) to Joint Effort for Data assimilation Integration (JEDI) software, which will be the backbone of future operational convective-scale DA systems.
Nusrat Yussouf, Craig Schwartz
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance, Decision Support
HMT/HWT
Model for Prediction Across Scales (MPAS)
This project proposes the development and evaluation of a storm-scale ensemble data assimilation and prediction system based on the MPAS dynamic core and JEDI software for short-term probabilistic forecasts of severe convective hazards. It aims to provide improved guidance for severe thunderstorms and flash flooding by migrating the existing DA system from DART to JEDI, enhancing resolution to 1-km, and rigorous evaluation through Hazardous Weather Testbed (HWT) and Hydrometeorology Testbed (HMT) experiments.
The probabilistic guidance products from the MPAS-based system will be used in NWS operations to increase lead-time and accuracy of watches and warnings for severe convective weather and extreme precipitation that leads to flash flooding. Depending on performance, this system could be a candidate to replace the WRF-based "Warn-on-Forecast System".
EMC
CIWRO/University of Oklahoma
NCAR
NA25OARX459C0215-T1-01 NA25OARX459C0216-T1-01
Severe Weather, Water Extremes
Colorado, Oklahoma
Testbeds
August 2025 - July 2028
Data Assimilation and Ensembles, Model & Forecast Guidance, Decision Support
a convection allowing ensemble system for short term probabilistic forecasts of severe convective hazards based on the mpas dynamic core, evaluate a mpas based data assimilation da and prediction system in hwt and hmt experiments for short term probabilistic guidance of severe thunderstorms migrate from the data assimilation research testbed dart to joint effort for data assimilation integration jedi software which will be the backbone of future operational convective scale da systems, this project proposes the development and evaluation of a storm scale ensemble data assimilation and prediction system based on the mpas dynamic core and jedi software for short term probabilistic forecasts of severe convective hazards it aims to provide improved guidance for severe thunderstorms and flash flooding by migrating the existing da system from dart to jedi enhancing resolution to 1 km and rigorous evaluation through hazardous weather testbed hwt and hydrometeorology testbed hmt experiments
Environmental Modeling Center (EMC)
CIWRO/University of Oklahoma, NCAR
Evaluate a MPAS-based data assimilation (DA) and prediction system in HWT and HMT experiments for short-term probabilistic guidance of severe thunderstorms. Migrate from the Data Assimilation Research Testbed (DART) to Joint Effort for Data assimilation
0
Hazards SEES Type 2: Next Generation, Resilient Warning Systems for Tornados and Flash Floods, Supplement
Philips
Social Science
check_circle
2016
Level 3
This goal of this supplement was to develop an initial methodological approach and strategy for capturing human behavioral understanding and response to weather and warnings for NOAA.
Tue Dec 31 2019 03:00:00 GMT-0500 (Eastern Standard Time)
Community Preparedness & Resilience, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Document Analysis
Policy
N/A
This project evaluates current NWS and NOAA programs that collect some form of social science data, including a) Service Assessments, b) the Customer Satisfaction Survey, and c) the Quick Response Survey. The project will also outline how to use sound social science methodological approaches in these data collection efforts. This report is the result of an inter-agency partnership between the Weather Program Office (WPO) and National Science Foundation. WPO supplemented their NSF proposal to focus on a NOAA specific application of their research.
By recognizing the importance of social science research and data collection, the NWS can make progress towards understanding how their customers utilize their products. Small but wide-spread structured changes to the agency will promote the benefits, importance, and legitimacy of social science research and make the data collection and analysis process a priority.
N/A
N/A
UMass Amherst
University of Delaware
OWAQ16-SSP-E-1
Relevant to All Hazards
Massachusetts
Social Science Program
September 2016 - December 2019
Community Preparedness & Resilience, "Social Science (SBES)", Document Analysis, Policy
hazards sees type 2 next generation resilient warning systems for tornados and flash floods supplement, this goal of this supplement was to develop an initial methodological approach and strategy for capturing human behavioral understanding and response to weather and warnings for noaa, this project evaluates current nws and noaa programs that collect some form of social science data including a service assessments b the customer satisfaction survey and c the quick response survey the project will also outline how to use sound social science methodological approaches in these data collection efforts this report is the result of an inter agency partnership between the weather program office wpo and national science foundation wpo supplemented their nsf proposal to focus on a noaa specific application of their research
Not Applicable (N/A)
UMass Amherst, University of Delaware
This goal of this supplement was to develop an initial methodological approach and strategy for capturing human behavioral understanding and response to weather and warnings for NOAA.
1
An Examination of the State of Knowledge on Risk Perceptions and Understanding Response to Uncertainty Information
Adams
Social Science
check_circle
2017
Level 3
The major goal of this project was to conduct a multi-disciplinary meta-analysis on the public's response to uncertainty information.
Terri Adams
Complete
Tue Aug 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Dec 31 2019 03:00:00 GMT-0500 (Eastern Standard Time)
This project conducts a multi-disciplinary systematic review of the literature on uncertainty. The overarching research question is how does the general public respond to and interpret uncertainty?
The National Oceanic and Atmospheric Administration (NOAA) and its affiliates have invested heavily in improving forecasting measures and making information available to the public. While several advancements have been made in measurement, atmospheric sensing, modeling, and other innovative developments, there is limited understanding about how the public responds to these products. Considerable attention has been given to the idea of changing how weather forecast information is shared with the public, especially regarding how forecast uncertainty is incorporated with additional probabilistic information. In contemplating the ways in which probabilistic information could impact the public, it is important to gain a baseline understanding of how decision-making is shaped by uncertainty.
an examination of the state of knowledge on risk perceptions and understanding response to uncertainty information, the major goal of this project was to conduct a multi disciplinary meta analysis on the public's response to uncertainty information, this project conducts a multi disciplinary systematic review of the literature on uncertainty the overarching research question is how does the general public respond to and interpret uncertainty?
Not Applicable (N/A)
Howard University
The major goal of this project was to conduct a multi-disciplinary meta-analysis on the public's response to uncertainty information.
0
Estimating the Economic Benefits of the Tornado Warning Improvement and Extension Program
Klockow
Social Science
check_circle
2018
Level 3
This project estimates the potential economic benefits of a Tornado Warning Improvement and Extension Program (TWIEP), especially on U.S. businesses and tornado-vulnerable populations.
Kimberly Klockow, Kevin Simmons
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Economic Valuation, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Interviews, Experiments, Economic Modeling
Mixed Methods
Vulnerable Populations, Business Sector
In the recently-passed Weather Research and Forecasting Innovation Act of 2017, Congress calls for improvements in tornado warning lead-times from the current average of 13 minutes (Erickson and Brooks 2006) to an hour (115 th Cong 2017). A program change of this magnitude would expand the behavioral response options of the populations in harm's way, which may be especially beneficial for reducing the mortality and morbidity rate of society's most vulnerable. Vulnerable populations are of particular interest because their injury and fatality rates far exceed those of other populations, and they may perish in many of the tornadoes that occur, not just the rarest and most intense tornadoes (Sutter and Simmons 2010). If vulnerable populations were to see an improved ability to access, understand, and respond to threats under this paradigm, part of the value of the TWIEP can be estimated based on the anticipated reduction in fatalities for these groups. In addition to the potential impacts for vulnerable populations, the TWIEP may also impact the decisions businesses make ahead of potential tornado threats. Specifically, the tornado warning improvements currently under development include the estimation of the likelihood that a storm may produce a given hazard, like a tornado (Rothfusz et al. 2018).
The Weather Research and Forecasting Innovation Act of 2017 requires NOAA to prioritize research to improve weather data, modeling, computing, forecasts, and warnings for the protection of life, property and the enhancement of the national economy. More specifically, the act calls for improvements in tornado warning lead-times from the current average of 13 minutes (Erickson and Brooks 2006) to an hour (115th Cong 2017). NOAA's Office of Oceanic and Atmospheric Research (OAR), Office of Weather and Air Quality is focusing on understanding the economic impact of an improved understanding of forecast capabilities for atmospheric events. This study is one such effort to focus on the value of 1-hour lead time. Since economic valuation is a multi-step process, this study provides foundational analysis in developing methodologies and an initial data set that will allow NOAA to understand the value of 1-hour lead time and the value of the research investments to reach that goal.
N/A
N/A
CIMMS/University of Oklahoma
Austin College
OWAQ18-SSP-E-1
Severe Weather
Oklahoma
Social Science Program
October 2018 - June 2021
Economic Valuation, Social Vulnerability, "Social Science (SBES)", Interviews, Experiments, Economic Modeling, Mixed Methods, Vulnerable Populations, Business Sector
estimating the economic benefits of the tornado warning improvement and extension program, this project estimates the potential economic benefits of a tornado warning improvement and extension program twiep especially on u s businesses and tornado vulnerable populations, in the recently passed weather research and forecasting innovation act of 2017 congress calls for improvements in tornado warning lead times from the current average of 13 minutes erickson and brooks 2006 to an hour 115 th cong 2017 a program change of this magnitude would expand the behavioral response options of the populations in harm's way which may be especially beneficial for reducing the mortality and morbidity rate of society's most vulnerable vulnerable populations are of particular interest because their injury and fatality rates far exceed those of other populations and they may perish in many of the tornadoes that occur not just the rarest and most intense tornadoes sutter and simmons 2010 if vulnerable populations were to see an improved ability to access understand and respond to threats under this paradigm part of the value of the twiep can be estimated based on the anticipated reduction in fatalities for these groups in addition to the potential impacts for vulnerable populations the twiep may also impact the decisions businesses make ahead of potential tornado threats specifically the tornado warning improvements currently under development include the estimation of the likelihood that a storm may produce a given hazard like a tornado rothfusz et al 2018
Not Applicable (N/A)
CIMMS/University of Oklahoma, Austin College
This project estimates the potential economic benefits of a Tornado Warning Improvement and Extension Program (TWIEP), especially on U.S. businesses and tornado-vulnerable populations.
2
A Web Based Survey to Estimate the Economic Value of Improved Hurricane Forecasts
Letson
Social Science
check_circle
2018
Level 3
The goal of this project was to evaluate the public's willingness to pay for more accurate hurricane forecasts through a large-scale choice experiment. Focusing on areas recently hit by hurricanes, this project establishes the value of improvements in storm track, wind speed, and precipitation forecast precision.
David Letson, Renato Molina, Pallab Mozumder
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Economic Valuation, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Surveys, Experiments, Economic Modeling
Quantitative
Public
The economic value of hurricane forecasts is directly related to its ability to affect behavior; namely household evacuation. Members of the public will complete a survey that varies the attributes of a hurricane forecast (e.g., storm surge, landfall location, intensity forecast) and possible mitigation behaviors (e.g., a monetary voucher to evacuate) in order to provide the time and/or expenditure costs of their choices. Overall, this project will estimate an overall value of improving hurricane forecasts.
The Weather Research and Forecasting Innovation Act of 2017 requires NOAA to prioritize research that improves forecasts and warnings for the protection of life, property, and the enhancement of the national economy. More specifically, targets improvements in hurricane forecasts that incorporate risk communication to create more effective watch and warning products (P.Law 115-25, Title 1, 101, 102, 104). As such, this research assesses the economic impact of improving hurricane forecasts and warnings through a public survey regarding how forecasts of varying precision affect human behavior. By manipulating forecast and behavioral attributes, this project will estimate an overall value of improving hurricane forecasts. Research on behavioral economics not only provides a value of improving a forecast, but may also help NOAA prioritize which attribute of an improved forecast will help NOAA meet its mission of protecting lives and property and, in turn, provide guidance on physical science research priorities.
N/A
N/A
CIMAS/University of Miami
Florida International University
OWAQ18-SSP-E-2
Tropical Cyclones
Florida
Social Science Program
October 2018 - September 2020
Model & Forecast Guidance, Economic Valuation, "Social Science (SBES)", Surveys, Experiments, Economic Modeling, Quantitative, Public
a web based survey to estimate the economic value of improved hurricane forecasts, the goal of this project was to evaluate the public's willingness to pay for more accurate hurricane forecasts through a large scale choice experiment focusing on areas recently hit by hurricanes this project establishes the value of improvements in storm track wind speed and precipitation forecast precision, the economic value of hurricane forecasts is directly related to its ability to affect behavior namely household evacuation members of the public will complete a survey that varies the attributes of a hurricane forecast e g storm surge landfall location intensity forecast and possible mitigation behaviors e g a monetary voucher to evacuate in order to provide the time and or expenditure costs of their choices overall this project will estimate an overall value of improving hurricane forecasts
Not Applicable (N/A)
CIMAS/University of Miami, Florida International University
The goal of this project was to evaluate the public's willingness to pay for more accurate hurricane forecasts through a large-scale choice experiment. Focusing on areas recently hit by hurricanes, this project establishes the value
1
Estimating the Value of Economic Resiliency Created By Weather Forecasts
Kurban
Social Science
check_circle
2018
Level 3
This project uses information on preparedness for severe weather events from two national represented survey datasets to study mitigating effects of preparedness on the economic costs of severe weather events.
Haydar Kurban
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
The Weather Research and Forecasting Innovation Act of 2017 requires NOAA to prioritize research that improves forecasts and warnings for the protection of life, property, and the enhancement of the national economy, and (P.Law 115-25, Title 1, 101, 102). As such, this research will develop a methodology for assessing the value of information generated by weather forecasts by measuring the value of resiliency built in the economy via forecast accuracy and mitigation efforts. Specifically, this study will focus on NOAA's forecast predictions from an economic perspective, combining forecast accuracy (3 and & 7 day forecast) data with economic damage and other relevant local area specific variables, such as gross domestic product and frequency of severe weather events. In doing so, this project is developing a mechanism that allows NOAA to assess a type of public response (i.e., impact on GDP) of NOAA's forecast information.
This study advances NOAA's ability to prioritize research that improves forecasts and warnings for the protection of life, property, and the enhancement of the national economy, as required by the Weather Research and Forecasting Innovation Act of 2017. This research will develop a methodology for assessing the economic impact through GDP and the value of forecast accuracy of 3 and 7 day forecasts. In doing so, this project is developing a mechanism that allows NOAA to assess a type of public response (i.e., impact on GDP) of NOAA's forecast information.
estimating the value of economic resiliency created by weather forecasts, this project uses information on preparedness for severe weather events from two national represented survey datasets to study mitigating effects of preparedness on the economic costs of severe weather events, the weather research and forecasting innovation act of 2017 requires noaa to prioritize research that improves forecasts and warnings for the protection of life property and the enhancement of the national economy and p law 115 25 title 1 101 102 as such this research will develop a methodology for assessing the value of information generated by weather forecasts by measuring the value of resiliency built in the economy via forecast accuracy and mitigation efforts specifically this study will focus on noaa's forecast predictions from an economic perspective combining forecast accuracy 3 and 7 day forecast data with economic damage and other relevant local area specific variables such as gross domestic product and frequency of severe weather events in doing so this project is developing a mechanism that allows noaa to assess a type of public response i e impact on gdp of noaa's forecast information
Not Applicable (N/A)
Howard University
This project uses information on preparedness for severe weather events from two national represented survey datasets to study mitigating effects of preparedness on the economic costs of severe weather events.
0
Joint Project: Social Media Message Amplification and Attentional Networks
Sutton
Social Science
check_circle
2018
Level 3
The goal of this NSF-NOAA supplemental research was to identify factors that are predictive of message retransmission (i.e., amplification) of NWS messaging on social media.
Jeannette Sutton, Carter Butts
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Flow of Weather Risk Information, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Social Media Analysis
Quantitative
Public
Social Science Transitions from Research into Operations: a NOAA and NSF Grant project, called the STRONG project for short, is an innovative funding partnership between OWAQ and the National Science Foundation (NSF) that takes advantage of ongoing investments in basic research at NSF to hasten application at NOAA. This STRONG project uses machine learning to code the corpus of 12 NWS WFO tweets from 2013-2016 according to their engagement content (i.e., information, action, and community building) and structure (e.g., hashtags, URLs). These message variables will be used to predict which types of message content and message structure lead to amplification (i.e., passed on via retweeting) of NWS tweets during threat and non-threat periods. In addition, this study will explore the interoffice interactions among the 12 WFOs that also lead to message retransmission.
This study has R2O communication implications by isolating how the NWS should communicate via social media during threat and non-threat periods in order to have their messages obtain the greatest amount of exposure.
N/A
N/A
University of Kentucky
University of California Irvine
OWAQ18-SSP-E-4
Relevant to All Hazards
California, Kentucky
Social Science Program
October 2018 - September 2021
Flow of Weather Risk Information, "Social Science (SBES)", Social Media Analysis, Quantitative, Public
joint project social media message amplification and attentional networks, the goal of this nsf noaa supplemental research was to identify factors that are predictive of message retransmission i e amplification of nws messaging on social media, social science transitions from research into operations a noaa and nsf grant project called the strong project for short is an innovative funding partnership between owaq and the national science foundation nsf that takes advantage of ongoing investments in basic research at nsf to hasten application at noaa this strong project uses machine learning to code the corpus of 12 nws wfo tweets from 2013 2016 according to their engagement content i e information action and community building and structure e g hashtags urls these message variables will be used to predict which types of message content and message structure lead to amplification i e passed on via retweeting of nws tweets during threat and non threat periods in addition this study will explore the interoffice interactions among the 12 wfos that also lead to message retransmission
Not Applicable (N/A)
University of Kentucky, University of California Irvine
The goal of this NSF-NOAA supplemental research was to identify factors that are predictive of message retransmission (i.e., amplification) of NWS messaging on social media.
3
NSF title: Hazards SEES Type 2: Hazard Prediction and Communication Dynamics in the Modern Information Environment
Morss
Social Science
check_circle
2018
Level 3
The goal of this NSF-NOAA supplemental research is to support the improved risk communication of tropical cyclones and hurricanes.
Rebecca Morss
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Communicating Probabilities & Uncertainty, Risk Communication, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
SSP
Focus Groups, Experiments, Social Media Analysis
Mixed Methods
Public
Social Science Transitions from Research into Operations: a NOAA and NSF Grant project, called the STRONG project for short, is an innovative funding partnership between OWAQ and the National Science Foundation (NSF) that takes advantage of ongoing investments in basic research at NSF to hasten application at NOAA. This STRONG project will supplement the application of the NSF grant, "Hazard Prediction and Communication Dynamics in the Modern Information Environment." Specifically, the PIs will enhance ongoing analysis of hurricane social media, focus group, and experimental data that has implications for a) improving hurricane forecast products, communication, and storm surge visualization tools, b) developing a hurricane risk communication strategy on social media, and c) helping NOAA to generate and convey improved probabilistic storm surge forecasts. The PIs will identify the most fruitful transition opportunities for these findings via interaction and collaboration between the researchers and NOAA, OAR, OWAQ, NHC, the NWS Houston/Galveston WFO, and a WFO in Florida (either Miami, Jacksonville, and/or Tampa).
Section 104 of the Weather Research and Forecasting Innovation Act of 2017 prioritizes the development of more accurate hurricane forecasting, with the goal of incorporating risk communication research into this endeavor. This STRONG project builds upon a pre-existing hurricane risk communication NSF grant that has the potential to a) improve hurricane forecast products, communication, and storm surge visualization tools, b) develop a hurricane risk communication strategy on social media, and c) help NOAA to generate and convey improved probabilistic storm surge forecasts. The project PIs will identify the most fruitful transition opportunities for their findings through iteration with OWAQ and NWS/NHC
N/A
N/A
National Center for Atmospheric Research
OWAQ18-SSP-E-5
Tropical Cyclones
Colorado
Social Science Program
October 2018 - September 2021
Communicating Probabilities & Uncertainty, Risk Communication, "Social Science (SBES)", Visual Risk Communication, Focus Groups, Experiments, Social Media Analysis, Mixed Methods, Public
nsf title hazards sees type 2 hazard prediction and communication dynamics in the modern information environment, the goal of this nsf noaa supplemental research is to support the improved risk communication of tropical cyclones and hurricanes, social science transitions from research into operations a noaa and nsf grant project called the strong project for short is an innovative funding partnership between owaq and the national science foundation nsf that takes advantage of ongoing investments in basic research at nsf to hasten application at noaa this strong project will supplement the application of the nsf grant "hazard prediction and communication dynamics in the modern information environment " specifically the pis will enhance ongoing analysis of hurricane social media focus group and experimental data that has implications for a improving hurricane forecast products communication and storm surge visualization tools b developing a hurricane risk communication strategy on social media and c helping noaa to generate and convey improved probabilistic storm surge forecasts the pis will identify the most fruitful transition opportunities for these findings via interaction and collaboration between the researchers and noaa oar owaq nhc the nws houston galveston wfo and a wfo in florida either miami jacksonville and or tampa
Not Applicable (N/A)
National Center for Atmospheric Research
The goal of this NSF-NOAA supplemental research is to support the improved risk communication of tropical cyclones and hurricanes.
0
NSF title: RAPID: Responding to the Risk of Hurricanes Harvey and Irma: Choices and Adjustment Over Time
Silver
Social Science
check_circle
2019
Level 3
The goal of this NSF-NOAA supplemental research was to use data collected prior to and following Hurricane Irma to guide risk communications during future hurricane seasons in the United States.
Roxane Silver
Complete
Fri Feb 01 2019 03:00:00 GMT-0500 (Eastern Standard Time)
Mon Feb 01 2021 03:00:00 GMT-0500 (Eastern Standard Time)
This project builds on 2 NSF grants that sampled approximately 1,600 Florida residents prior to and following Hurricane Irma. Here, the researchers seek to determine which individual factors (e.g., risk perceptions and emotional responses) help explain who evacuates as a hurricane approaches. Weather Program Office funds will help facilitate detailed data analysis of how risk perceptions changed with the physical forecast and ultimately how the forecast can impact real-time evacuation decisions.
Project recommendations will help refine communications for at-risk individuals and communities, will examine how to revise communications as storms' paths shift and/or defy earlier predictions, and will discuss recommendations for debriefing communities and broadcasters after a storm has bypassed a community.
nsf title rapid responding to the risk of hurricanes harvey and irma choices and adjustment over time, the goal of this nsf noaa supplemental research was to use data collected prior to and following hurricane irma to guide risk communications during future hurricane seasons in the united states, this project builds on 2 nsf grants that sampled approximately 1 600 florida residents prior to and following hurricane irma here the researchers seek to determine which individual factors e g risk perceptions and emotional responses help explain who evacuates as a hurricane approaches weather program office funds will help facilitate detailed data analysis of how risk perceptions changed with the physical forecast and ultimately how the forecast can impact real time evacuation decisions
Not Applicable (N/A)
University of California Irvine
The goal of this NSF-NOAA supplemental research was to use data collected prior to and following Hurricane Irma to guide risk communications during future hurricane seasons in the United States.
4
Wait, that forecast changed? Assessing how publics consume and process changing tropical cyclone forecasts over time
Wong-Parodi
Social Science
check_circle
2019
Level 3
The goal of this project was to develop a proof-of-concept methodology that could measure, in real time, how various publics consume and process changing tropical cyclone forecasts over time.
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Flow of Weather Risk Information, Information Seeking & Processing, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
SSP-IFAA
Surveys
Quantitative
Public
This project will choose methodologies that will uncover how the public consumes and processes tropical cyclone risk information. Questions to consider include: What tropical cyclone information does the public hear from different channels? For example, does the information received from social media, TV stations, and websites look fundamentally different or similar? How does the public incorporate uncertainty into their decision process? How does the public(s) anchor to the hurricane risk and associated uncertainty? How are they shifting their risk perceptions over the duration of the hurricane event? How do emergency managers compare to the broader public in terms of their risk perceptions? The NWS has had a growing interest in shifting risk preferences. Although the focus would remain on tropical, the results may have broader theoretical applications to hazards throughout the NWS/OAR.
While this project functions independently, together the 4 the social science Hurricane Supplemental projects position the NWS and NHC to make significant progress toward modernizing NWS products to better convey risk and uncertainty, enable enhanced risk assessment, provide timely preparedness, and allow more effective action on the part of users, partners and stakeholders to reduce loss of life and property. Collectively, these projects ensure that technological enhancements also meet the needs of NWS stakeholders and the American public while simultaneously improving our warning system.
AFS
Jessica Schauer
NWS Tropical Services Program
jessica.schauer@noaa.gov
National Center for Atmospheric Research
Stanford University
University of Colorado
NA19OAR0220095
NA19OAR0220103
NA19OAR0220119
Tropical Cyclones
California, Colorado
Social Science Program
July 2019 - June 2021
Flow of Weather Risk Information, Information Seeking & Processing, Risk Perception & Response, "Social Science (SBES)", Surveys, Quantitative, Public
wait that forecast changed? assessing how publics consume and process changing tropical cyclone forecasts over time, the goal of this project was to develop a proof of concept methodology that could measure in real time how various publics consume and process changing tropical cyclone forecasts over time, this project will choose methodologies that will uncover how the public consumes and processes tropical cyclone risk information questions to consider include what tropical cyclone information does the public hear from different channels? for example does the information received from social media tv stations and websites look fundamentally different or similar? how does the public incorporate uncertainty into their decision process? how does the public s anchor to the hurricane risk and associated uncertainty? how are they shifting their risk perceptions over the duration of the hurricane event? how do emergency managers compare to the broader public in terms of their risk perceptions? the nws has had a growing interest in shifting risk preferences although the focus would remain on tropical the results may have broader theoretical applications to hazards throughout the nws oar
NWS Analyze, Forecast, and Support Office (AFS)
National Center for Atmospheric Research, Stanford University, University of Colorado
The goal of this project was to develop a proof-of-concept methodology that could measure, in real time, how various publics consume and process changing tropical cyclone forecasts over time.
14
Optimizing Tropical Cyclone Information: An NOAA Hurricane Website User Experience Study from a Public Perspective
Miles
Social Science
check_circle
2019
Level 3
The goal of this project was to evaluate the usability of National Hurricane Center's (NHC) webpage and help NOAA identify various design opportunities to modernize the NHC's web presence.
This project will focus on a user-centered interaction design method to conduct a usability test of NOAA's hurricane information. This project will utilize a combination of approaches that will - Understand the public and NWS core partner information needs relative to website tropical cyclone information - Conduct a scripted scenario in which to have participants walk through current NWS hurricane related websites to determine current functionality - Provide guidance on how to optimize the design of a one-NOAA hurricane information website as well as for tablet/phone use. - Provide guidance on how to optimize NWS social media presence. Depending on data collected, optimization may include improving product titles, incorporating more lay language, and providing ideas for redesigning website layout.
While this project functions independently, together the 4 the social science Hurricane Supplemental projects position the NWS and NHC to make significant progress toward modernizing NWS products to better convey risk and uncertainty, enable enhanced risk assessment, provide timely preparedness, and allow more effective action on the part of users, partners and stakeholders to reduce loss of life and property. Collectively, these projects ensure that technological enhancements also meet the needs of NWS stakeholders and the American public while simultaneously improving our warning system.
AFS
Jessica Schauer
NWS Tropical Services Program
jessica.schauer@noaa.gov
optimizing tropical cyclone information an noaa hurricane website user experience study from a public perspective, the goal of this project was to evaluate the usability of national hurricane center's nhc webpage and help noaa identify various design opportunities to modernize the nhc's web presence, this project will focus on a user centered interaction design method to conduct a usability test of noaa's hurricane information this project will utilize a combination of approaches that will understand the public and nws core partner information needs relative to website tropical cyclone information conduct a scripted scenario in which to have participants walk through current nws hurricane related websites to determine current functionality provide guidance on how to optimize the design of a one noaa hurricane information website as well as for tablet phone use provide guidance on how to optimize nws social media presence depending on data collected optimization may include improving product titles incorporating more lay language and providing ideas for redesigning website layout
NWS Analyze, Forecast, and Support Office (AFS)
University of Washington
The goal of this project was to evaluate the usability of National Hurricane Center's (NHC) webpage and help NOAA identify various design opportunities to modernize the NHC's web presence.
2
There's a Chance of What? Assessing Numeracy Skills of Forecasters, Partners, and Publics to Improve TC Product Uncertainty Communication, IDSS, and Training
Ripberger
Social Science
check_circle
2019
Level 3
The goal of this project was to examine how end-users interpret and comprehend probabilistic tropical cyclone information. This was explored through the use of a concept known as numeracy, or one's ability to use and understand numerical information.
Joseph Ripberger, Carol Silva , Hank Jenkins-Smith , Edward Cokely
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Communicating Probabilities & Uncertainty, Decision Support, Risk Communication, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
SSP-IFAA
Surveys
Quantitative
Forecasters, Emergency Managers, Other Core Partners, Public
This study will conduct a baseline assessment of forecaster, NWS core partner, and public's understanding of probabilities broadly (called numeracy) related to tropical cyclone hazards (including inland flooding, etc.). This effort will advance research on numeracy and related communication framing of probabilistic information as it relates to TC hazards. In particular, the study will focus on ways to express probabilities and its respective reference class to enable forecasters, NWS core partners, and publics to assess their risk.
While this project functions independently, together the 4 the social science Hurricane Supplemental projects position the NWS and NHC to make significant progress toward modernizing NWS products to better convey risk and uncertainty, enable enhanced risk assessment, provide timely preparedness, and allow more effective action on the part of users, partners and stakeholders to reduce loss of life and property. Collectively, these projects ensure that technological enhancements also meet the needs of NWS stakeholders and the American public while simultaneously improving our warning system.
AFS
Jessica Schauer
NWS Tropical Services Program
jessica.schauer@noaa.gov
there's a chance of what? assessing numeracy skills of forecasters partners and publics to improve tc product uncertainty communication idss and training, the goal of this project was to examine how end users interpret and comprehend probabilistic tropical cyclone information this was explored through the use of a concept known as numeracy or one's ability to use and understand numerical information, this study will conduct a baseline assessment of forecaster nws core partner and public's understanding of probabilities broadly called numeracy related to tropical cyclone hazards including inland flooding etc this effort will advance research on numeracy and related communication framing of probabilistic information as it relates to tc hazards in particular the study will focus on ways to express probabilities and its respective reference class to enable forecasters nws core partners and publics to assess their risk
NWS Analyze, Forecast, and Support Office (AFS)
University of Oklahoma
The goal of this project was to examine how end-users interpret and comprehend probabilistic tropical cyclone information. This was explored through the use of a concept known as numeracy, or one's ability to use and
2
Minding the gap: Modernizing the TC product suite by evaluating NWS partner information needs
Morss
Social Science
check_circle
2019
Level 3
By interviewing and surveying NWS partners, this project was designed to help NWS prioritize their efforts to modernize their tropical cyclone product suite and identify gaps needed to enhance NWS partner decision-making.
Rebecca Morss, Ann Bostrom, Julie Demuth, Heather Lazrus
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Forecast Product Usability & Design, Decision-Making, Information Seeking & Processing, Personalization of Weather Information, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
SSP-IFAA
Interviews, Surveys
Mixed Methods
Emergency Managers, Other Core Partners, Broadcast Meteorologists
This project will conduct a baseline study on the tropical cyclone information needs and the utility of the current tropical cyclone product suite in supporting key decision-making by NWS core partners and the public. First, this study will capture what NWS core partners and the public need to know about a tropical cyclone and when without constraint to current product issuance. Cross referencing this with the current product suite will begin to identify needs not currently met. The research team will further identify information gaps, needs, or conflicts that the current product suite is not meeting by focusing on meteorological characteristics that are missing, technological aspects that limit utility, and/or product message enhancements, such as needed lay language descriptions, or the need for consistency between NWS Centers and WFOs. The goal of the project is to help NWS prioritize their efforts to modernize their tropical cyclone product suite and identify gaps needed to enhance NWS partner and public decision-making.
While this project functions independently, together the 4 the social science Hurricane Supplemental projects position the NWS and NHC to make significant progress toward modernizing NWS products to better convey risk and uncertainty, enable enhanced risk assessment, provide timely preparedness, and allow more effective action on the part of users, partners and stakeholders to reduce loss of life and property. Collectively, these projects ensure that technological enhancements also meet the needs of NWS stakeholders and the American public while simultaneously improving our warning system.
AFS
Jessica Schauer
NWS Tropical Services Program
jessica.schauer@noaa.gov
National Center for Atmospheric Research
University of Washington
minding the gap modernizing the tc product suite by evaluating nws partner information needs, by interviewing and surveying nws partners this project was designed to help nws prioritize their efforts to modernize their tropical cyclone product suite and identify gaps needed to enhance nws partner decision making, this project will conduct a baseline study on the tropical cyclone information needs and the utility of the current tropical cyclone product suite in supporting key decision making by nws core partners and the public first this study will capture what nws core partners and the public need to know about a tropical cyclone and when without constraint to current product issuance cross referencing this with the current product suite will begin to identify needs not currently met the research team will further identify information gaps needs or conflicts that the current product suite is not meeting by focusing on meteorological characteristics that are missing technological aspects that limit utility and or product message enhancements such as needed lay language descriptions or the need for consistency between nws centers and wfos the goal of the project is to help nws prioritize their efforts to modernize their tropical cyclone product suite and identify gaps needed to enhance nws partner and public decision making
NWS Analyze, Forecast, and Support Office (AFS)
National Center for Atmospheric Research, University of Washington
By interviewing and surveying NWS partners, this project was designed to help NWS prioritize their efforts to modernize their tropical cyclone product suite and identify gaps needed to enhance NWS partner decision-making.
10
Accelerating Development of the US Extreme Weather and Society Survey Series
Ripberger
Social Science
check_circle
2020
Level 3
The goal of this project was to expand the Extreme Weather and Society surveys in two ways: (1) develop a winter weather survey and (2) translate the most recent WxSurvey to Spanish and implement the survey to Spanish speaking residents in the U.S.
Joseph Ripberger , Carol Silva, Hank Jenkins-Smith, Scott Robinson, Warren Eller, Julie Demuth, John Lipski
Communicating Probabilities & Uncertainty, Decision-Making, Language & Cultural Contexts, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Surveys
Quantitative
Public
In Spring 2012/2013, the University of Oklahoma's Center for Risk and Crisis Management began implementing the Severe Weather and Society Survey (Wx Survey), a data collection capacity that provides generalizable, longitudinal, and experimental data on the extent to which members of the US public receive, understand, and respond to severe weather forecasts and warnings. The Wx Survey is an annual public survey that includes two types of questions: (1) baseline (recurring) questions that measure forecast and warning reception, comprehension, and response; and (2) new (one-time) questions and experiments that address multiple topics, such as the impact of uncertainty and probabilistic information on risk judgements and protective action decision making. This expansion project will develop the foundation necessary to establish a yearly emergency management survey series that will provide systematic information about how emergency managers access, interpret, and use information from the National Weather Service. In addition, this expansion project will also develop a version of the yearly Wx Survey that addresses winter weather threats. Finally, this expansion project will translate the most recent version of the Wx Survey to Spanish and then implement the survey to Spanish speaking US residents
This type of data collection helps establish baselines, track progress, and determine where change is or is not occurring. For example, as NWS adapts and develops new policies, products, and communication practices (e.g., the Hazard Simplification Project), having longitudinal measurement of these concepts across the country, both before and after these policies, products, and communication changes are implemented, allows NWS to evaluate its impact. Therefore, longitudinal studies and their associated data are essential to measure improvements that inform operational decision-making.
OSTI
Ji Sun Lee
NWS Social Science Program
jisun.lee@noaa.gov
CIMMS/University of Oklahoma
John Jay College of Criminal Justice
National Center for Atmospheric Research
Penn State University
WPO20-SSP-E-1
Severe Weather, Winter Weather
Colorado, New York, Oklahoma, Pennsylvania
Social Science Program
July 2020 - September 2021
Communicating Probabilities & Uncertainty, Decision-Making, Language & Cultural Contexts, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Surveys, Quantitative, Public
accelerating development of the us extreme weather and society survey series, the goal of this project was to expand the extreme weather and society surveys in two ways 1 develop a winter weather survey and 2 translate the most recent wxsurvey to spanish and implement the survey to spanish speaking residents in the u s, in spring 2012 2013 the university of oklahoma's center for risk and crisis management began implementing the severe weather and society survey wx survey a data collection capacity that provides generalizable longitudinal and experimental data on the extent to which members of the us public receive understand and respond to severe weather forecasts and warnings the wx survey is an annual public survey that includes two types of questions 1 baseline recurring questions that measure forecast and warning reception comprehension and response and 2 new one time questions and experiments that address multiple topics such as the impact of uncertainty and probabilistic information on risk judgements and protective action decision making this expansion project will develop the foundation necessary to establish a yearly emergency management survey series that will provide systematic information about how emergency managers access interpret and use information from the national weather service in addition this expansion project will also develop a version of the yearly wx survey that addresses winter weather threats finally this expansion project will translate the most recent version of the wx survey to spanish and then implement the survey to spanish speaking us residents
NWS Office of Science and Technology Integration (OSTI)
CIMMS/University of Oklahoma, John Jay College of Criminal Justice, National Center for Atmospheric Research, Penn State University
The goal of this project was to expand the Extreme Weather and Society surveys in two ways: (1) develop a winter weather survey and (2) translate the most recent WxSurvey to Spanish and implement the
4
Examining the public's changing risk assessments and responses to tropical cyclone forecasts using a longitudinal survey methodology
Demuth
Social Science
check_circle
2020
Level 3
The outcomes of this project are to (1) demonstrate a new longitudinal survey methodology for collecting real-time data from at-risk members of the U.S. public during tropical cyclone threats; and (2) utilize this methodology to develop new NOAA-relevant knowledge about how people access, interpret, and respond to changing tropical cyclone forecast and warning information.
Thu Feb 29 2024 03:00:00 GMT-0500 (Eastern Standard Time)
Flow of Weather Risk Information, Information Seeking & Processing, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Surveys
Quantitative
Public
This project will design and implement a longitudinal survey of at-risk members of the public for two Atlantic Basic tropical cyclones that threaten the mainland U.S. For each tropical cyclone, data will be collected in two to three survey waves; the first when the tropical cyclone approaches the U.S. and one post-storm survey. Each predictive wave will ask the same set of questions about topics such as: the forecast information people are currently getting; their risk perceptions and efficacy beliefs for the current TC threat; and the protective actions they have taken. The post-storm wave will ask people about information that was most useful to them during the threat; information they wish they had during the threat; effects they experienced from the tropical cyclone; and their assessment of protective actions taken. Data will be analyzed using longitudinal statistical data analysis techniques to investigate changes within and among individuals over time.
Improved communication of tropical cyclone forecasts helps reduce negative impacts on the public from tropical cyclones. Improved communication can help the public take the necessary protective action for tropical cyclones.
AFS
Jessica Schauer
NWS Tropical Services Program
jessica.schauer@noaa.gov
National Center for Atmospheric Research
NA20OAR4590469
Tropical Cyclones
Colorado
Social Science Program
September 2020 - February 2024
Flow of Weather Risk Information, Information Seeking & Processing, Risk Perception & Response, "Social Science (SBES)", Surveys, Quantitative, Public
examining the public's changing risk assessments and responses to tropical cyclone forecasts using a longitudinal survey methodology, the outcomes of this project are to 1 demonstrate a new longitudinal survey methodology for collecting real time data from at risk members of the u s public during tropical cyclone threats and 2 utilize this methodology to develop new noaa relevant knowledge about how people access interpret and respond to changing tropical cyclone forecast and warning information, this project will design and implement a longitudinal survey of at risk members of the public for two atlantic basic tropical cyclones that threaten the mainland u s for each tropical cyclone data will be collected in two to three survey waves the first when the tropical cyclone approaches the u s and one post storm survey each predictive wave will ask the same set of questions about topics such as the forecast information people are currently getting their risk perceptions and efficacy beliefs for the current tc threat and the protective actions they have taken the post storm wave will ask people about information that was most useful to them during the threat information they wish they had during the threat effects they experienced from the tropical cyclone and their assessment of protective actions taken data will be analyzed using longitudinal statistical data analysis techniques to investigate changes within and among individuals over time
NWS Analyze, Forecast, and Support Office (AFS)
National Center for Atmospheric Research
The outcomes of this project are to (1) demonstrate a new longitudinal survey methodology for collecting real-time data from at-risk members of the U.S. public during tropical cyclone threats; and (2) utilize this methodology to
4
Threats in Motion (Taller TIM) (FACETs)
Murphy
Social Science
check_circle
2020
Level 3
Short term outcomes include strengthening the partnership between GSL, MDL, and SBES. Long term outcomes include improved visualization of hazards, which supports the move from binary warning systems to eventually producing probabilistic hazard information.
Sylvia Murphy
Complete
Thu Oct 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Threats-In-Motion (TIM) evolves the Advanced Weather Information Processing System (AWIPS) Hazard Services enabling the National Weather Service (NWS) to upgrade severe thunderstorm and tornado warnings from static polygons to continuously-updating polygons that move with the storm.
Improved visualization of hazards will reduce deaths associated with severe weather and property damage.
threats in motion taller tim facets, short term outcomes include strengthening the partnership between gsl mdl and sbes long term outcomes include improved visualization of hazards which supports the move from binary warning systems to eventually producing probabilistic hazard information, threats in motion tim evolves the advanced weather information processing system awips hazard services enabling the national weather service nws to upgrade severe thunderstorm and tornado warnings from static polygons to continuously updating polygons that move with the storm
NOAA/OAR/GSL
Short term outcomes include strengthening the partnership between GSL, MDL, and SBES. Long term outcomes include improved visualization of hazards, which supports the move from binary warning systems to eventually producing probabilistic hazard information.
0
Dynamic Communication of Weather Risk: A User-Centered Design Approach
Majumdar
Social Science
search_activity
2021
Level 3
This project is aimed at effective visual communication of forecast risk and uncertainty, with a primary goal of establishing a translatable process for NOAA to harness public feedback to rethink and redesign weather risk graphics, and evaluate new visualizations.
Sharanya Majumdar, Kenneth Broad, Alberto Cairo, Scotney Evans, Barbara Millet, Rebecca Morss
Active
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Emergency Managers, Broadcast Meteorologists, Public
This project is aimed at effective visual communication of forecast risk and uncertainty, with a primary goal of establishing a translatable process for NOAA to harness public feedback to rethink and redesign weather risk graphics, and evaluate new visualizations. The appropriate amount of information on a graphic that can be cognitively processed by forecast users will be examined, as will the question of whether the communication of "expected" versus "reasonable worst case" impacts is more effective. Online behavioral experiments and interviews will develop lessons learned, which will be combined with new ideation and participatory design sessions to develop visualizations for multiple hazards from non-tropical events, evaluated via in-person eye-tracking experiments. Primary deliverables include: improved visual communications of risk and uncertainty, and new user-centered design guidelines for wider NOAA applications. The intended long-term benefit to society is an improved ability for the public to make decisions ahead of approaching weather events with multiple hazards, via graphical products that are easy to use and understand.
The primary deliverables are improved visual communications of risk and uncertainty, and new user-centered design guidelines for wider NOAA applications. The intended long-term benefit to society is an improved ability for the public to make decisions ahead of approaching weather events with multiple hazards, via graphical products that are easy to use and understand.
AFS
Jessica Schauer
NWS Tropical Services Program
jessica.schauer@noaa.gov
CIMAS/University of Miami
National Center for Atmospheric Research
dynamic communication of weather risk a user centered design approach, this project is aimed at effective visual communication of forecast risk and uncertainty with a primary goal of establishing a translatable process for noaa to harness public feedback to rethink and redesign weather risk graphics and evaluate new visualizations, this project is aimed at effective visual communication of forecast risk and uncertainty with a primary goal of establishing a translatable process for noaa to harness public feedback to rethink and redesign weather risk graphics and evaluate new visualizations the appropriate amount of information on a graphic that can be cognitively processed by forecast users will be examined as will the question of whether the communication of "expected" versus "reasonable worst case" impacts is more effective online behavioral experiments and interviews will develop lessons learned which will be combined with new ideation and participatory design sessions to develop visualizations for multiple hazards from non tropical events evaluated via in person eye tracking experiments primary deliverables include improved visual communications of risk and uncertainty and new user centered design guidelines for wider noaa applications the intended long term benefit to society is an improved ability for the public to make decisions ahead of approaching weather events with multiple hazards via graphical products that are easy to use and understand
NWS Analyze, Forecast, and Support Office (AFS)
CIMAS/University of Miami, National Center for Atmospheric Research
This project is aimed at effective visual communication of forecast risk and uncertainty, with a primary goal of establishing a translatable process for NOAA to harness public feedback to rethink and redesign weather risk graphics,
4
Using Quick Response Surveys to Generate a Public Perception and Response Database
Phillips
Social Science
search_activity
2021
Level 3
This project aims to create a scientifically rigorous and operationally feasible survey system for collecting data on the publics' perception and response to four different hazards: tornados, severe thunderstorms, flash floods, and winter weather.
Brenda Phillips, Cedar League, Vankita Brown
Active
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Surveys
Quantitative
Public
This project aims to create a scientifically rigorous and operationally feasible survey system for collecting data on the publics' perception and response to four different hazards: tornados, thunderstorm winds over 70 miles per hour (mph), flash floods, and winter weather. By using consistent questions and methodological approach, these surveys will be the building blocks for a multi-year, cross-sectional database on human perception and response. The survey system will enable individual WFOs to disseminate Quick Response Surveys soon after a hazardous event occurs to collect perishable data on the publics' perceptions and response.This project will address survey instrument design, survey administration, triggers for survey dissemination, population sampling, data aggregation, and analysis of results. The ultimate societal benefit of the proposed project will be improved public response and protective action behavior by the public that could mitigate the impacts of hazardous weather events. The benefits will extend to multiple organizational levels within NOAA and to the research community in general.
This project will aggregate human perception and response data across multiple regions, hazards, and warning types to evaluate higher-level trends to better inform region-wide eduation, warning communications, and/or social and physical science initiatives. Ultimately, the benefit of this roject will be improved public response and protective action behavior by the public, which could mitigate the impacts of hazardous weather events.
OSTI
Ji Sun Lee
NWS Social Science Program
jisun.lee@noaa.gov
University of Massachusetts Amherst
Cedar League
National Weather Service
NA21OAR4590207
Severe Weather, Water Extremes, Winter Weather
Massachusetts, Montana
Social Science Program
August 2021 - July 2025
Risk Perception & Response, "Social Science (SBES)", Surveys, Quantitative, Public
using quick response surveys to generate a public perception and response database, this project aims to create a scientifically rigorous and operationally feasible survey system for collecting data on the publics' perception and response to four different hazards tornados severe thunderstorms flash floods and winter weather, this project aims to create a scientifically rigorous and operationally feasible survey system for collecting data on the publics' perception and response to four different hazards tornados thunderstorm winds over 70 miles per hour mph flash floods and winter weather by using consistent questions and methodological approach these surveys will be the building blocks for a multi year cross sectional database on human perception and response the survey system will enable individual wfos to disseminate quick response surveys soon after a hazardous event occurs to collect perishable data on the publics' perceptions and response this project will address survey instrument design survey administration triggers for survey dissemination population sampling data aggregation and analysis of results the ultimate societal benefit of the proposed project will be improved public response and protective action behavior by the public that could mitigate the impacts of hazardous weather events the benefits will extend to multiple organizational levels within noaa and to the research community in general
NWS Office of Science and Technology Integration (OSTI)
University of Massachusetts Amherst, Cedar League, National Weather Service
This project aims to create a scientifically rigorous and operationally feasible survey system for collecting data on the publics' perception and response to four different hazards: tornados, severe thunderstorms, flash floods, and winter weather.
0
Employing Rhetoric to Improve Probabilistic Forecast Communication
Lambrecht
Social Science
check_circle
2021
Level 3
The goal of this project is to see if community commonplaces can be used to identify risk thresholds for local stakeholder communities, and then to see if these thresholds can be visualized in a way that improves PHI communication and Impact-Based Decision Support (IDSS).
Kathryn Lambrecht, Lynda Olman , Benjamin Hatchett
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri Jan 31 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Communicating Probabilities & Uncertainty, Decision-Making, Decision Support, Risk Communication, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
SSP
Surveys, Experiments
Quantitative
Emergency Managers, Other Core Partners, Public
The proposed research is to identify stakeholder risk thresholds to improve the visualization of PHI forecasts. Researchers will test visualizations of PHI for a wide range of weather hazards by surveying NWS forecast offices core partners and social-media users. We will create and evaluate visualizations of probabilistic forecasts using a range of methods to determine where risk-assessment thresholds lie and use them to create tools NWS staff throughout the U.S. can employ to optimize PHI communication with their own user groups. Central to this project is the use of rhetoric, a subdiscipline of communication studies that models collective decision making in the face of rampant uncertainty. Project outcomes include: (1) Provide operational meteorologists with a best practices guide and adaptable templates for visual and verbal communication techniques of PHI; (2) Supply a simple rhetorical toolkit for recovering risk thresholds from stakeholder surveys to allow more NWS offices to engineer just-in-time communication solutions for their partners; (3) Create educational videos about PHI forecasting to enrich and strengthen NWS-stakeholder communication. The proposed multifaceted approach has a high probability of revolutionizing weather forecast communication and enhancing IDSS, especially during high impact, costly, and deadly weather events.
This project will identify stakeholder risk thresholds and use the information to provide a best practices guide for probabilistic hazard information (PHI). This work will improve the visualization of PHI forecasts.
OSTI
Chad Gravelle
NWS Science & Technology Integration
chad.gravelle@noaa.gov
Arizona State University
University of Nevada Reno
Desert Research Institute
employing rhetoric to improve probabilistic forecast communication, the goal of this project is to see if community commonplaces can be used to identify risk thresholds for local stakeholder communities and then to see if these thresholds can be visualized in a way that improves phi communication and impact based decision support idss, the proposed research is to identify stakeholder risk thresholds to improve the visualization of phi forecasts researchers will test visualizations of phi for a wide range of weather hazards by surveying nws forecast offices core partners and social media users we will create and evaluate visualizations of probabilistic forecasts using a range of methods to determine where risk assessment thresholds lie and use them to create tools nws staff throughout the u s can employ to optimize phi communication with their own user groups central to this project is the use of rhetoric a subdiscipline of communication studies that models collective decision making in the face of rampant uncertainty project outcomes include 1 provide operational meteorologists with a best practices guide and adaptable templates for visual and verbal communication techniques of phi 2 supply a simple rhetorical toolkit for recovering risk thresholds from stakeholder surveys to allow more nws offices to engineer just in time communication solutions for their partners 3 create educational videos about phi forecasting to enrich and strengthen nws-stakeholder communication the proposed multifaceted approach has a high probability of revolutionizing weather forecast communication and enhancing idss especially during high impact costly and deadly weather events
NWS Office of Science and Technology Integration (OSTI)
Arizona State University, University of Nevada Reno, Desert Research Institute
The goal of this project is to see if community commonplaces can be used to identify risk thresholds for local stakeholder communities, and then to see if these thresholds can be visualized in a way
2
Operationalizing Impact-Based Decision Support Services (IDSS) for a Broader Impact Across Cultures and Weather Events
Adams
Social Science
check_circle
2021
Level 3
The major goal of this project is to gain an understanding of how forecasters and emergency managers define and implement Impact-based Decision Support Services (IDSS) in their respective regions across different weather events.
Terri Adams, Vankita Brown, Rosa Fitzgerald, Leticia Williams
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Flow of Weather Risk Information, Decision Support, "Organizational Dimensions (e.g., culture, management, communication)", "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Interviews, Surveys, Focus Groups
Mixed Methods
Forecasters, Emergency Managers, Other Core Partners
This interdisciplinary research project seeks to advance the understanding and execution of Impact-Based Decision Support Services (IDSS) at the National Weather Service (NWS). This project will establish a baseline of IDSS performance to assess implementation across NWS and the inclusion of cultural dimensions in engagement with emergency managers (EMs). The objectives of this project are: (1) define how Meteorologist in Charge, Hydrologist in Charge, Warning Coordination Meteorologist, and Service Coordination Hydrologist operationalize IDSS; (2) assess how IDSS is performed at different NWS organizational levels (e.g., national, regional) and by weather event; and (3) assess how EMs' awareness of the needs of their communities and cultural dimensions guide the provision of IDSS. This project will follow the procedures of a multi-phased study using a sequential explanatory mixed-methods approach that includes the collection of both qualitative and quantitative data. Results of this work will support developing the infrastructure for IDSS, enhancing training materials, and supplementing current IDSS initiatives. Findings will also provide the NWS with best practices to consider cultural dimension in the provision of IDSS. The project also has implications for moving findings from research to NWS management and operations by providing a framework to determine a benchmark for IDSS consistency, identify best practices, and present recommendations to increase the efficiency and uniformity of IDSS.
Results of this work will support developing Impact-Based Decision Support Services (IDSS) infrastructure, enhancing training materials, and supplementing current IDSS initiatives, and external measures that assess IDSS performance. Findings will also provide the NWS with best practices to consider cultural dimension in the provision of IDSS. The project provides a framework to determine a benchmark for IDSS consistency, identify best practices, and present recommendations to increase the efficiency and uniformity of IDSS.
operationalizing impact based decision support services idss for a broader impact across cultures and weather events, the major goal of this project is to gain an understanding of how forecasters and emergency managers define and implement impact based decision support services idss in their respective regions across different weather events, this interdisciplinary research project seeks to advance the understanding and execution of impact based decision support services idss at the national weather service nws this project will establish a baseline of idss performance to assess implementation across nws and the inclusion of cultural dimensions in engagement with emergency managers ems the objectives of this project are 1 define how meteorologist in charge hydrologist in charge warning coordination meteorologist and service coordination hydrologist operationalize idss 2 assess how idss is performed at different nws organizational levels e g national regional and by weather event and 3 assess how ems' awareness of the needs of their communities and cultural dimensions guide the provision of idss this project will follow the procedures of a multi phased study using a sequential explanatory mixed methods approach that includes the collection of both qualitative and quantitative data results of this work will support developing the infrastructure for idss enhancing training materials and supplementing current idss initiatives findings will also provide the nws with best practices to consider cultural dimension in the provision of idss the project also has implications for moving findings from research to nws management and operations by providing a framework to determine a benchmark for idss consistency identify best practices and present recommendations to increase the efficiency and uniformity of idss
NWS Analyze, Forecast, and Support Office (AFS)
Howard University
The major goal of this project is to gain an understanding of how forecasters and emergency managers define and implement Impact-based Decision Support Services (IDSS) in their respective regions across different weather events.
0
Improving the Effectiveness of IDSS For Severe Convective Weather and Flood Events
LaDue
Social Science
check_circle
2021
Level 3
This project seeks to discover how emergency managers' (EM) differing individual and job characteristics influence how effectively they use the National Weather Service's forecasts and Impact-Based Decision Support (IDSS)
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri Feb 28 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Decision Support, Integrated Warning Team (IWT), "Organizational Dimensions (e.g., culture, management, communication)", "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Interviews, Surveys
Mixed Methods
Forecasters, Emergency Managers, Other Core Partners
This researchers of this project propose to (1) explore how organizational and individual differences among city and county emergency managers (EMs) might influence how they use NWS IDSS information and convey it to other officials in their jurisdictions, and (2) further develop the NWS Amarillo Integrated Warning Team (IWT) "Pod" methodology. The IWT Pod is an exercise in which EMs and other key decision-makers work together through a simulated severe weather event. The overall aim of the proposed research is to provide a better understanding of how EMs interact with IDSS information and the means (a method) through which NWS Weather Forecast Offices can discover how to improve their IDSS services. Specifically, the knowledge gained from this research will help WFOs design more effective IDSS products that better meet the decision needs of EMs and other core partners in local jurisdictions. This will help officials make more cost-efficient preparation decisions and lower the overall economic impact of severe weather.
This work will develop the Integrated Warning Team Pod methodology, where emergency managers and other decision-makers work together during severe weather events. The methodology will help identify and shortcomings in the Impact-Based Decision Support Services information, to improve decision services.
AFS
Andy Foster
NWS IDSS Program
andy.foster@noaa.gov
University of Oklahoma
Jackson State University
NOAA/OAR/NSSL
improving the effectiveness of idss for severe convective weather and flood events, this project seeks to discover how emergency managers' em differing individual and job characteristics influence how effectively they use the national weather service's forecasts and impact based decision support idss, this researchers of this project propose to 1 explore how organizational and individual differences among city and county emergency managers ems might influence how they use nws idss information and convey it to other officials in their jurisdictions and 2 further develop the nws amarillo integrated warning team iwt "pod" methodology the iwt pod is an exercise in which ems and other key decision makers work together through a simulated severe weather event the overall aim of the proposed research is to provide a better understanding of how ems interact with idss information and the means a method through which nws weather forecast offices can discover how to improve their idss services specifically the knowledge gained from this research will help wfos design more effective idss products that better meet the decision needs of ems and other core partners in local jurisdictions this will help officials make more cost efficient preparation decisions and lower the overall economic impact of severe weather
NWS Analyze, Forecast, and Support Office (AFS)
University of Oklahoma, Jackson State University, NOAA/OAR/NSSL
This project seeks to discover how emergency managers' (EM) differing individual and job characteristics influence how effectively they use the National Weather Service's forecasts and Impact-Based Decision Support (IDSS)
17
Developing, Testing, and Evaluating Methods for Transitioning the Brief Vulnerability Overview Tool (BVOT) to NWS Weather Forecasting Office Operations
LaDue
Social Science
check_circle
2021
Level 3
The goal of this project is to improve the development, testing, and evaluation of methods that can be used by NWS WFOs to collect operationally actionable vulnerability data to create Brief Vulnerability Overview Tools (BVOT) for their CWAs.
Daphne LaDue, Jack Friedman
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Visualizations, Forecast Product Usability & Design, Decision Support, Personalization of Weather Information, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Interviews, Surveys
Mixed Methods
Forecasters, Emergency Managers, Other Core Partners
This two-year project applies and integrates social and behavioral science research to develop, test, and evaluate methods that can be used by NWS Weather Forecasting Offices (WFOs) to collect operationally actionable vulnerability data in their County Warning Areas. Specifically, we will evaluate a range of methods in different types of WFOs across 4 NWS regions to populate unique Brief Vulnerability Overview Tools (BVOT) for each WFO. The social science-derived methods used to collect these "known vulnerabilities" result in mapped information that alerts meteorologists to critical vulnerabilities without being overly detailed that they might distract meteorologists from their core tasks. Thus, this social science-driven proposal will assess not only the usability and scalability of the BVOT in operations, but, also, will assess the viability of new ways of thinking about what would be involved in transferring social science-derived research from research-to-operations.
This project will assess different methods of collecting and systemizing vulnerability information, with the intent of providing meteorologists increased spatial-situational awareness fo vulnerabilities in their warning area. This project will include vulnerability information in shapefiles for meteorologists and emergency managers to map vulnerability information.
OSTI
Paul Kirkwood
NWS Southern Region
Paul.Kirkwood@noaa.gov
University of Oklahoma
NA21OAR4590212
Relevant to All Hazards
Oklahoma
Social Science Program
August 2021 - July 2024
Visualizations, Forecast Product Usability & Design, Decision Support, Personalization of Weather Information, Social Vulnerability, "Social Science (SBES)", Interviews, Surveys, Mixed Methods, Forecasters, Emergency Managers, Other Core Partners
developing testing and evaluating methods for transitioning the brief vulnerability overview tool bvot to nws weather forecasting office operations, the goal of this project is to improve the development testing and evaluation of methods that can be used by nws wfos to collect operationally actionable vulnerability data to create brief vulnerability overview tools bvot for their cwas, this two year project applies and integrates social and behavioral science research to develop test and evaluate methods that can be used by nws weather forecasting offices wfos to collect operationally actionable vulnerability data in their county warning areas specifically we will evaluate a range of methods in different types of wfos across 4 nws regions to populate unique brief vulnerability overview tools bvot for each wfo the social science derived methods used to collect these "known vulnerabilities" result in mapped information that alerts meteorologists to critical vulnerabilities without being overly detailed that they might distract meteorologists from their core tasks thus this social science driven proposal will assess not only the usability and scalability of the bvot in operations but also will assess the viability of new ways of thinking about what would be involved in transferring social science derived research from research to operations
NWS Office of Science and Technology Integration (OSTI)
University of Oklahoma
The goal of this project is to improve the development, testing, and evaluation of methods that can be used by NWS WFOs to collect operationally actionable vulnerability data to create Brief Vulnerability Overview Tools (BVOT)
3
Establishing the Extreme Weather and Emergency Management Survey
Ripberger
Social Science
check_circle
2021
Level 3
The goal of this project was to expand the Extreme Weather and Society surveys by implementing a recurring survey of the emergency management community to understand how they use NWS products and services when responding to high impact weather events.
Joseph Ripberger, Carol Silva, Hank Jenkins-Smith, Scott Robinson, Andrew Fox, Warren Eller
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will identify, develop, and test new metrics that measure the effectiveness of Impact-Based Decision Support Services (IDSS) with NWS core partners by creating the Extreme Weather and Emergency Management Survey (WxEm Survey). The WxEm Survey will implement a yearly survey of the emergency management community (EMs) to provide longitudinal data on how EMs use NOAA/NWS data, products, information, and decision support when responding to high impact weather events. Data from the WxEm Survey will allow the project team to develop composite metrics that provide direct information to forecasters at WFOs about the most effective way to structure IDSS communication and feedback to administrators in the NWS about how to improve IDSS programs in the future. Additionally, the research team will create an interactive platform the members of the forecast community can use to better understand their respective audiences. The WxEm Survey and interactive platform will function alongside the Extreme Weather and Society (WxSoc) Survey and interactive platform (WxDash), which currently provide longitudinal data on how members of the public use NOAA/NWS data and products when responding to high impact weather events. This project will benefit society by providing systematic information about how EMs use NOAA/NWS data, products, information, and decision support when responding to high impact weather events. This information will provide invaluable feedback that will improve NWS IDSS and, consequently, public and economic security.
This project will benefit society by providing systematic information about how EMs use NOAA/NWS data, products, information, and decision support when responding to high impact weather events. This information will provide invaluable feedback that will improve NWS IDSS and, consequently, public and economic security.
OSTI
Ji Sun Lee
NWS Social Science Program
jisun.lee@noaa.gov
CIMMS/University of Oklahoma
John Jay College of Criminal Justice
establishing the extreme weather and emergency management survey, the goal of this project was to expand the extreme weather and society surveys by implementing a recurring survey of the emergency management community to understand how they use nws products and services when responding to high impact weather events, this project will identify develop and test new metrics that measure the effectiveness of impact based decision support services idss with nws core partners by creating the extreme weather and emergency management survey wxem survey the wxem survey will implement a yearly survey of the emergency management community ems to provide longitudinal data on how ems use noaa nws data products information and decision support when responding to high impact weather events data from the wxem survey will allow the project team to develop composite metrics that provide direct information to forecasters at wfos about the most effective way to structure idss communication and feedback to administrators in the nws about how to improve idss programs in the future additionally the research team will create an interactive platform the members of the forecast community can use to better understand their respective audiences the wxem survey and interactive platform will function alongside the extreme weather and society wxsoc survey and interactive platform wxdash which currently provide longitudinal data on how members of the public use noaa nws data and products when responding to high impact weather events this project will benefit society by providing systematic information about how ems use noaa nws data products information and decision support when responding to high impact weather events this information will provide invaluable feedback that will improve nws idss and consequently public and economic security
NWS Office of Science and Technology Integration (OSTI)
CIMMS/University of Oklahoma, John Jay College of Criminal Justice
The goal of this project was to expand the Extreme Weather and Society surveys by implementing a recurring survey of the emergency management community to understand how they use NWS products and services when responding
3
Improving Knowledge about NWS Forecaster Core Partner Needs for Reducing Vulnerability to Compound Threats in Landfalling Tropical Cyclones Amid Covid-19
Henderson
Social Science
check_circle
2021
Level 3
This project will develop new knowledge about how core partners attend to, assess, and communicate compound hazards and the impacts of COVID-19 on weather-related decision-making.
Jennifer Henderson, Erik Nielsen
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Compound Hazards, Decision-Making, Decision Support, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Interviews, Surveys
Mixed Methods
Emergency Managers, Other Core Partners, Broadcast Meteorologists
As compound hazards, landfalling tropical cyclones (LTC) pose a significant risk to public safety, bringing significant winds, flooding rains, and storm surge in their wake. In the context of Covid-19, risks for harm can become heightened as people make decisions about how to prioritize health concerns for themselves and others amid complex weather risks. When tornadoes co-occur with flash flooding during LTCs, what we call TORFFs, warnings for each can contain contradictory advice that can complicate people's understanding of the threat that is most dangerous to them. This project will examine how core partners conceptualize, plan for, and communicate TORFFs in LTCs. This project will develop new knowledge about (a) how partners attend to, assess, and communicate compound hazards and how Covid-19 magnifies challenges for partners' when giving advice to the public and planning for evacuations and shelters; and (b) how this understanding shapes and is shaped by partners' conceptualization of changes in and indicators for the public's vulnerability.
Through reports and publications, this project will examine how core partners conceptualize, plan for an communicate compound hazards. This project will help forecasters improve warning practices and decision support for core partners in emergency management and broadcasters during compound hazard events, and will be especially benefitial for vulnerable populations in Covid-19.
AFS
Jessica Schauer
NWS Tropical Services Program
jessica.schauer@noaa.gov
improving knowledge about nws forecaster core partner needs for reducing vulnerability to compound threats in landfalling tropical cyclones amid covid 19, this project will develop new knowledge about how core partners attend to assess and communicate compound hazards and the impacts of covid 19 on weather related decision making, as compound hazards landfalling tropical cyclones ltc pose a significant risk to public safety bringing significant winds flooding rains and storm surge in their wake in the context of covid 19 risks for harm can become heightened as people make decisions about how to prioritize health concerns for themselves and others amid complex weather risks when tornadoes co occur with flash flooding during ltcs what we call torffs warnings for each can contain contradictory advice that can complicate people's understanding of the threat that is most dangerous to them this project will examine how core partners conceptualize plan for and communicate torffs in ltcs this project will develop new knowledge about a how partners attend to assess and communicate compound hazards and how covid 19 magnifies challenges for partners' when giving advice to the public and planning for evacuations and shelters and b how this understanding shapes and is shaped by partners' conceptualization of changes in and indicators for the public's vulnerability
NWS Analyze, Forecast, and Support Office (AFS)
Texas Tech University, Texas A & M University
This project will develop new knowledge about how core partners attend to, assess, and communicate compound hazards and the impacts of COVID-19 on weather-related decision-making.
10
Salient, Interactive, Relevant, Confidence, and Action (SIRCA): Using Virtual Reality Storm Surge Simulations to Increase Risk Perception and Prevention Behaviors
Ahn
Social Science
search_activity
2021
Level 3
This project will develop a virtual reality simulation, alongside education and training materials, that will help coastal residents and decision makers make better informed decisions.
Sun Joo Ahn, Jill Gambill, Matthew Browning
Active
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Visualizations, Community Preparedness & Resilience, Personalization of Weather Information, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
SSP
Surveys, Experiments
Quantitative
Public
This project will develop a VR simulation with the following objectives: (1) increase understanding of the risks posed by severe storms, hurricanes, storm surge and flooding; (2) improve knowledge of mitigation and adaptation strategies at individual and community levels, and; (3) engage participants in active learning that builds the environmental literacy necessary for vulnerable coastal communities to increase resilience to climate and weather hazards. The (VR) storm surge simulation that allows individuals to experience a storm surge as if it were happening at the moment, and provides the opportunity to practice preventive behaviors in the virtual world so that individuals can apply those behaviors in the physical world. The VR simulation in tandem with scaffolding education and training materials will help coastal residents and decision makers make better informed decisions and take steps to protect lives and livelihoods in vulnerable coastal communities by heightening risk perceptions and increasing an understanding of the ways in which communities can become more resilient.
The virtual reality storm surge simulation will allow individuals to experience a storm surge as if it were happening in real time, and provides the opportunity to practice preventive behaviors in the virtual world so individuals can apply those behaviors in the physical world. Coupled with the training package, the virtual reality simulation could increase effective response among populations in coastal regions.
NHC
Joshua Alland
National Hurricane Center
joshua.alland@noaa.gov
University of Georgia
Clemson University
NA21OAR4590216, NA21OAR4590217
Tropical Cyclones
Georgia, South Carolina
Social Science Program
August 2021 - July 2025
Visualizations, Community Preparedness & Resilience, Personalization of Weather Information, Risk Perception & Response, "Social Science (SBES)", Visual Risk Communication, Surveys, Experiments, Quantitative, Public
salient interactive relevant confidence and action sirca using virtual reality storm surge simulations to increase risk perception and prevention behaviors, this project will develop a virtual reality simulation alongside education and training materials that will help coastal residents and decision makers make better informed decisions, this project will develop a vr simulation with the following objectives 1 increase understanding of the risks posed by severe storms hurricanes storm surge and flooding 2 improve knowledge of mitigation and adaptation strategies at individual and community levels and 3 engage participants in active learning that builds the environmental literacy necessary for vulnerable coastal communities to increase resilience to climate and weather hazards the vr storm surge simulation that allows individuals to experience a storm surge as if it were happening at the moment and provides the opportunity to practice preventive behaviors in the virtual world so that individuals can apply those behaviors in the physical world the vr simulation in tandem with scaffolding education and training materials will help coastal residents and decision makers make better informed decisions and take steps to protect lives and livelihoods in vulnerable coastal communities by heightening risk perceptions and increasing an understanding of the ways in which communities can become more resilient
National Hurricane Center (NHC)
University of Georgia, Clemson University
This project will develop a virtual reality simulation, alongside education and training materials, that will help coastal residents and decision makers make better informed decisions.
10
Examining Regional Differences in Attitudes and Tendencies for Protective Action Decisions
LaDue
Social Science
search_activity
2021
Level 3
The proposed research aims to gather regionally-derived knowledge and experiences related to protective action decisions from survivors of severe weather events, and use these findings to design experimental probabilistic warnings to identify effective ways of visually and verbally conveying probabilistic warning information and encouraging appropriate protective actions among the public.
Communicating Probabilities & Uncertainty, Community Preparedness & Resilience, Risk Communication, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
SSP
Interviews, Surveys, Experiments
Mixed Methods
Public
This proposed work will speak with survivors of approximately six tornado or high-end thunderstorm events outside of the Southeast US in order to elicit regionally-derived knowledge and experiences related to protective action decisions. This information has not fully been tapped in the creation of effective warning communications, significantly limiting the efficacy of these messages. Second, we will use these findings to design experimental probabilistic warnings and assess how message elements interact with personal experience, social factors, and/or environmental factors in protective action decisions. The primary project outputs are (1) a better understanding of how warning messages are received among survivors and the role of those messages, in conjunction with social and environmental factors, in individuals' sheltering decisions, and (2) identifying effective qualities for visually and verbally conveying probabilistic warning information and encouraging appropriate protective actions among members of the public.
The results from this project will outputs will assist NOAA NWS Weather Forecast Offices, emergency management officials, and broadcast meteorologists in their communications with their publics through better understanding of how to motivate protective actions during hazardous weather that reduce injury and death in high-end convective severe weather.
AFS
Aaron Treadway
NWS Severe Weather Program
aaron.treadway@noaa.gov
examining regional differences in attitudes and tendencies for protective action decisions, the proposed research aims to gather regionally derived knowledge and experiences related to protective action decisions from survivors of severe weather events and use these findings to design experimental probabilistic warnings to identify effective ways of visually and verbally conveying probabilistic warning information and encouraging appropriate protective actions among the public, this proposed work will speak with survivors of approximately six tornado or high end thunderstorm events outside of the southeast us in order to elicit regionally derived knowledge and experiences related to protective action decisions this information has not fully been tapped in the creation of effective warning communications significantly limiting the efficacy of these messages second we will use these findings to design experimental probabilistic warnings and assess how message elements interact with personal experience social factors and or environmental factors in protective action decisions the primary project outputs are 1 a better understanding of how warning messages are received among survivors and the role of those messages in conjunction with social and environmental factors in individuals' sheltering decisions and 2 identifying effective qualities for visually and verbally conveying probabilistic warning information and encouraging appropriate protective actions among members of the public
NWS Analyze, Forecast, and Support Office (AFS)
University of Oklahoma
The proposed research aims to gather regionally-derived knowledge and experiences related to protective action decisions from survivors of severe weather events, and use these findings to design experimental probabilistic warnings to identify effective ways of
0
Analysis of Longitudinal Extreme Weather and Society Data and Development of the Flood Weather Survey and Dashboard
Ripberger
Social Science
search_activity
2022
Level 3
The goal of this project was to expand the Extreme Weather and Society surveys by developing a flooding and precipitation survey.
Joseph Ripberger, Makenzie Krocak, Carol Silva, Hank Jenkins-Smith
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Decision Support, Language & Cultural Contexts, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Surveys
Quantitative
Public
This project will develop Extreme Weather and Society Surveys, which provide generalizable, longitudinal, and experimental data on the extent to which members of the US public receive, understand, and respond to extreme weather forecasts and warnings. This project will implement a Spanish language version of the Tropical survey; continue developing the Winter Weather survey, including developing a Spanish version and performing longitudinal analysis, and establish a Flood survey
Improved understanding of people and communities will help deliver better, more tailored weather information to the public. This will help reduce lives lost to extreme weather.
OSTI
Ji Sun Lee
NWS Social Science Program
jisun.lee@noaa.gov
CIWRO/University of Oklahoma
Nurture Nature Center
NA21OAR4320204-T3-01S019
Water Extremes
Oklahoma, Pennsylvania
Social Science Program
August 2022 - July 2026
Decision Support, Language & Cultural Contexts, Risk Perception & Response, "Social Science (SBES)", Surveys, Quantitative, Public
analysis of longitudinal extreme weather and society data and development of the flood weather survey and dashboard, the goal of this project was to expand the extreme weather and society surveys by developing a flooding and precipitation survey, this project will develop extreme weather and society surveys which provide generalizable longitudinal and experimental data on the extent to which members of the us public receive understand and respond to extreme weather forecasts and warnings this project will implement a spanish language version of the tropical survey continue developing the winter weather survey including developing a spanish version and performing longitudinal analysis and establish a flood survey
NWS Office of Science and Technology Integration (OSTI)
CIWRO/University of Oklahoma, Nurture Nature Center
The goal of this project was to expand the Extreme Weather and Society surveys by developing a flooding and precipitation survey.
0
DRSA (PRECIP1): Improving Delivery of Weather Prediction Center Precipitation Products
Carr
Social Science
search_activity
2023
Level 3
This goal of this project is will provide user testing and iterative refinement of precipitation products in coordination with Weather Prediction Center (WPC) to improve the suite of probabilistic and deterministic rainfall products.
Rachel Hogan Carr, Kathryn Semmens, Keri Maxfield, Maggie Beetstra, Burrell Montz, Joe Miller
Active
Wed Feb 01 2023 03:00:00 GMT-0500 (Eastern Standard Time)
Sun Jan 31 2027 03:00:00 GMT-0500 (Eastern Standard Time)
Forecasters, Emergency Managers, Other Core Partners, Public, Vulnerable Populations
This project will provide user testing and iterative refinement of precipitation products in coordination with Weather Prediction Center (WPC) to improve the suite of probabilistic and deterministic rainfall products, understand how these products are used in engagement and decision-making, aid in the development of the new Urban Rain Rate Dashboard, and conduct a usability study of the WPC website.
This project will provide an expanded understanding of the key users of Weather Prediction Center (WPC) precipitation products, as well as their needs for information for decision-making. The study will also develop an enhanced suite of user-tested precipitation products that better perform for NWS and stakeholders and will also provide a greatly improved platform for users accessing the full suite of WPC products and services.
WPC
Jim Nelson
Weather Prediction Center
james.a.nelson@noaa.gov
Nurture Nature Center
Soapbox Design
East Carolina University
NA23OAR4590137
Water Extremes
North Carolina, Pennsylvania, District of Columbia
drsa precip1 improving delivery of weather prediction center precipitation products, this goal of this project is will provide user testing and iterative refinement of precipitation products in coordination with weather prediction center wpc to improve the suite of probabilistic and deterministic rainfall products, this project will provide user testing and iterative refinement of precipitation products in coordination with weather prediction center wpc to improve the suite of probabilistic and deterministic rainfall products understand how these products are used in engagement and decision making aid in the development of the new urban rain rate dashboard and conduct a usability study of the wpc website
Weather Prediction Center (WPC)
Nurture Nature Center, Soapbox Design, East Carolina University
This goal of this project is will provide user testing and iterative refinement of precipitation products in coordination with Weather Prediction Center (WPC) to improve the suite of probabilistic and deterministic rainfall products.
0
Making FIMs Work for Multiple Users: Understanding the Value and Function of Flood Inundation Mapping to Support Decision Support
Carr
Social Science
search_activity
2023
Level 3
The goal of this project is to provide critical insight into how flood inundation mapping (FIM) products support flooding decision-making and will collect data from key stakeholders to improve product delivery and design.
Rachel Hogan Carr, Kathryn Semmens, Keri Maxfield
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Forecast Product Usability & Design, Decision-Making, Decision Support, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Surveys, Focus Groups
Mixed Methods
Emergency Managers, Other Core Partners, Public, Vulnerable Populations
This project will provide critical insight into how Flood Inundation Mapping (FIM) products support decision making related to following and will collect data from key stakeholders to improve product delivery and design. It will advance understanding of how users would access, use, and value FIM and related information in their decision making.
This project will help improve the delivery and design of Flood Inundation Mapping (FIM) products by providing tested, feasible recommendations for their use with rargeted professional and general audiences. Ultimately, this project will contribute to more effective messaging and communication of flood inundation risk and assist communities in better preparing to manage potential flood impacts.
Office of Water Prediction
David Vallee
NWS Office of Water Prediction
david.vallee@noaa.gov
making fims work for multiple users understanding the value and function of flood inundation mapping to support decision support, the goal of this project is to provide critical insight into how flood inundation mapping fim products support flooding decision making and will collect data from key stakeholders to improve product delivery and design, this project will provide critical insight into how flood inundation mapping fim products support decision making related to following and will collect data from key stakeholders to improve product delivery and design it will advance understanding of how users would access use and value fim and related information in their decision making
Nurture Nature Center
The goal of this project is to provide critical insight into how flood inundation mapping (FIM) products support flooding decision-making and will collect data from key stakeholders to improve product delivery and design.
1
Improving Emergency Communications and Preparedness among the Deaf and Hard of Hearing Community
Wang
Social Science
search_activity
2023
Level 3
The goal of this project is to illuminate the communication needs and preferences of Deaf and Hard of Hearing individuals and to uncover the shortcomings of the current warning system for these populations.
Chongming Wang, John Walsh, Michael Freeman
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Community Preparedness & Resilience, Personalization of Weather Information, Risk Communication, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Interviews, Surveys, Focus Groups
Mixed Methods
Emergency Managers, Vulnerable Populations
This project focuses on the Deaf and Hard of Hearing populations and seeks to illuminate their communication needs and preferences to grow the understanding of how and why the current warning system has placed Deaf and Hard of Hearing individuals at a particular disadvantage.
The inability to hear real-time weather and emergency alert notifications places the the Deaf and Hard of Hearing populations at a particular disadvantage. Therefore, this project aims to better understand the needs, barriers, and priorities of this underrepresented and underserved group and deploy adequate resources and applicable technologies to ensure Deaf and Hard of Hearing individuals are within the purview of the emergency communication and planning vision.
OSTI
Krizia Negron
NWS Language Program
krizia.negron@noaa.gov
Jacksonville State University
Vanderbilt University Medicine Center
NA23OAR4590360
Severe Weather, Tropical Cyclones
Alabama, Tennessee
Social Science Program
August 2023 - July 2026
Community Preparedness & Resilience, Personalization of Weather Information, Risk Communication, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Interviews, Surveys, Focus Groups, Mixed Methods, Emergency Managers, Vulnerable Populations
improving emergency communications and preparedness among the deaf and hard of hearing community, the goal of this project is to illuminate the communication needs and preferences of deaf and hard of hearing individuals and to uncover the shortcomings of the current warning system for these populations, this project focuses on the deaf and hard of hearing populations and seeks to illuminate their communication needs and preferences to grow the understanding of how and why the current warning system has placed deaf and hard of hearing individuals at a particular disadvantage
NWS Office of Science and Technology Integration (OSTI)
Jacksonville State University, Vanderbilt University Medicine Center
The goal of this project is to illuminate the communication needs and preferences of Deaf and Hard of Hearing individuals and to uncover the shortcomings of the current warning system for these populations.
0
Under-reached, under-informed: Improving information and early action for under-represented population in US disasters
Easton-Calabria
Social Science
search_activity
2023
Level 3
The goal of this project is to develop a replicable methodology to effectively map and analyze the information ecosystems of underserved populations in the U.S., with an emphasis on migrant and refugee populations.
Evan Easton-Calabria, Erin Coughlan de Perez
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Flow of Weather Risk Information, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Interviews, Surveys, Focus Groups
Mixed Methods
Vulnerable Populations
This project will develop a comprehensive replicable methodology for mapping weather-related information ecosystems of underserved populations, with an emphasis on migrant and refugee populations in the United States.
This project will directly inform and influence different aspects of NOAA's risk communication efforts, including the design of impact-based forecasts and warning products, choice of message and communication channels, and support for early action that would provide the greatest socioeconomic value to under-represented populations.
AFS
Leticia Williams
NWS Social Science Program
leticia.williams@noaa.gov
Tufts College
NA23OAR4590361
Relevant to All Hazards
Massachusetts
Social Science Program
August 2023 - July 2026
Flow of Weather Risk Information, Social Vulnerability, "Social Science (SBES)", Interviews, Surveys, Focus Groups, Mixed Methods, Vulnerable Populations
under reached under informed improving information and early action for under represented population in us disasters, the goal of this project is to develop a replicable methodology to effectively map and analyze the information ecosystems of underserved populations in the u s with an emphasis on migrant and refugee populations, this project will develop a comprehensive replicable methodology for mapping weather related information ecosystems of underserved populations with an emphasis on migrant and refugee populations in the united states
NWS Analyze, Forecast, and Support Office (AFS)
Tufts College
The goal of this project is to develop a replicable methodology to effectively map and analyze the information ecosystems of underserved populations in the U.S., with an emphasis on migrant and refugee populations.
0
Leveraging Efforts to Provide Localized Probabilistic and Timing Guidance for NWS Core Partners
Marmo
Social Science
search_activity
2023
Level 3
The goal of this project is to enhance comprehension and usability of SPC's timing graphics using feedback from surveys and tabletop exercises with forecasters and emergency managers.
Alex Marmo, Daphne LaDue
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
This project aims to enhance comprehension and usability of the Storm Prediction Center's severe timing guidance graphical forecast products by gathering feedback from survey and tabletop exercises with forecasters and emergency managers.
This project will offer modifications to the SPC's severe timing guidance graphics, providing information that is useful and usable to both NWS forecasters and their partners. Imporved visual graphics can fill a gap in the localization of spatial and temporal information. Finally, the improved graphics will also benefit NWS core partners by adding personal context to the risk, aiding in their decision-making process, and allowing them to communicate more clearly to their stakeholders.
SPC
Israel Jirak
Storm Prediction Center
israel.jirak@noaa.gov
leveraging efforts to provide localized probabilistic and timing guidance for nws core partners, the goal of this project is to enhance comprehension and usability of spc's timing graphics using feedback from surveys and tabletop exercises with forecasters and emergency managers, this project aims to enhance comprehension and usability of the storm prediction center's severe timing guidance graphical forecast products by gathering feedback from survey and tabletop exercises with forecasters and emergency managers
Storm Prediction Center (SPC)
University of Oklahoma
The goal of this project is to enhance comprehension and usability of SPC's timing graphics using feedback from surveys and tabletop exercises with forecasters and emergency managers.
0
Hurricane Naming Conventions and Bilingual Audiences: Characterizing Spanish-Speaking Broadcast Meteorologists' Challenges Communicating Multiple Hazards in Landfalling Tropical Cyclones
Henderson
Social Science
search_activity
2023
Level 3
The goal of this project is to understand how bilingual and Spanish-speaking broadcast meteorologists address challenges in communicating complex, multi-hazard information associated with tropical cyclones.
Jen Henderson, Rodolfo Hernandez, Erik Nielsen
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
This project aims to create new knowledge about how bilingual and Spanish-speaking broadcast meteorologists address challenges in communicating complex, multi-hazard information to their audiences and understand how the use of the Saffir Simpson Hurricane Wind Scale and naming conventions for landfalling tropical cyclone remnants are tailored to multi-language audiences.
This project will help NWS partners in Spanish-speaking and bilingual boradcast meteorology markets improve their ability to communicate risk information for compound hazards in landfalling tropical cyclones.
OSTI
Krizia Negron
NWS Language Program
krizia.negron@noaa.gov
Texas Tech University
Texas A&M University
NA23OAR4590363, NA23OAR4590364
Tropical Cyclones
Texas
Social Science Program
August 2023 - July 2026
Compound Hazards, Language & Cultural Contexts, Social Vulnerability, "Social Science (SBES)", Interviews, Surveys, Focus Groups, Ethnographic Observation, Mixed Methods, Broadcast Meteorologists
hurricane naming conventions and bilingual audiences characterizing spanish speaking broadcast meteorologists' challenges communicating multiple hazards in landfalling tropical cyclones, the goal of this project is to understand how bilingual and spanish speaking broadcast meteorologists address challenges in communicating complex multi hazard information associated with tropical cyclones, this project aims to create new knowledge about how bilingual and spanish speaking broadcast meteorologists address challenges in communicating complex multi hazard information to their audiences and understand how the use of the saffir simpson hurricane wind scale and naming conventions for landfalling tropical cyclone remnants are tailored to multi language audiences
NWS Office of Science and Technology Integration (OSTI)
Texas Tech University, Texas A&M University
The goal of this project is to understand how bilingual and Spanish-speaking broadcast meteorologists address challenges in communicating complex, multi-hazard information associated with tropical cyclones.
0
Streamlining Development of the Brief Vulnerability Overview Tool (BVOT) and Assessing the BVOT's Impact on Tailored Messaging
LaDue
Social Science
search_activity
2023
Level 3
The goal of this project is to streamline the process of creating a Brief Vulnerability Overview Tool (BVOT), and better understand the role of a BVOT for tailoring IDSS and messaging to partners.
Daphne LaDue , Elizabeth Hurst , Michelle Saunders, Alex Marmo
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Visualizations, Decision Support, Personalization of Weather Information, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Interviews, Surveys
Mixed Methods
Forecasters, Emergency Managers
This project aims to streamline the process of creating a Brief Vulnerability Overview Tool (BVOT) and establishes the role of the BVOT in improving the personalization and tailoring of messaging to emergency managers.
This project will benefit all elements of the operational weather enterprise and the publics that is serves by improving NWS WFO awareness of vulnerabilities in their local area so that they can better tailor messaging to EMs and other core partners to protect lives and property.
OSTI
Paul Kirkwood
NWS Southern Region
Paul.Kirkwood@noaa.gov
University of Oklahoma
Mississippi State University
NA23OAR4590365
Relevant to All Hazards
Mississippi, Oklahoma
Social Science Program
August 2023 - July 2026
Visualizations, Decision Support, Personalization of Weather Information, Social Vulnerability, "Social Science (SBES)", Interviews, Surveys, Mixed Methods, Forecasters, Emergency Managers
streamlining development of the brief vulnerability overview tool bvot and assessing the bvot's impact on tailored messaging, the goal of this project is to streamline the process of creating a brief vulnerability overview tool bvot and better understand the role of a bvot for tailoring idss and messaging to partners, this project aims to streamline the process of creating a brief vulnerability overview tool bvot and establishes the role of the bvot in improving the personalization and tailoring of messaging to emergency managers
NWS Office of Science and Technology Integration (OSTI)
University of Oklahoma, Mississippi State University
The goal of this project is to streamline the process of creating a Brief Vulnerability Overview Tool (BVOT), and better understand the role of a BVOT for tailoring IDSS and messaging to partners.
2
Quantifying the socio-economic benefits of augmented hurricane observations
Molina
Social Science
search_activity
2023
Level 3
The goal of this project is to develop a methodology to evaluate how aircraft hurricane observations affect the overall accuracy of a forecast and how this translates to societal benefits.
Renato Molina, Ivan Rudik, Soraida Diaz
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon Aug 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Economic Valuation, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Economic Modeling
Quantitative
N/A
This project will evaluate how the type and frequency of aircraft hurricane observations affect the overall precision and accuracy of a forecast, as well as how these changes translate into direct benefits to society. More importantly, this project will propose and formalize a methodology to conduct benefit-cost assessments that can be used for additional forecasting observation systems as well as the implementation and transition to new observational technologies.
Using data on past storms and previous data denial studies for those storms, this project will establish how the changes in forecasting accuracy and precision due to aircraft hurricane observations lead to direct reductions in the cost of landfalling hurricanes. Further, the knowledge and methology produced from this project can be used by decision makers and elected officials in Congress evaluating the potential upside of the national budget allocated to the science of improving hurricane forecasts and investments and transition associated with observation technologies.
quantifying the socio economic benefits of augmented hurricane observations, the goal of this project is to develop a methodology to evaluate how aircraft hurricane observations affect the overall accuracy of a forecast and how this translates to societal benefits, this project will evaluate how the type and frequency of aircraft hurricane observations affect the overall precision and accuracy of a forecast as well as how these changes translate into direct benefits to society more importantly this project will propose and formalize a methodology to conduct benefit cost assessments that can be used for additional forecasting observation systems as well as the implementation and transition to new observational technologies
University of Miami, Cornell University
The goal of this project is to develop a methodology to evaluate how aircraft hurricane observations affect the overall accuracy of a forecast and how this translates to societal benefits.
0
While you were sleeping: Improving warning reception and response to nocturnal rapid-onset hazards in the Southeast
Ellis
Social Science
search_activity
2023
Level 3
The goal of this project is to develop visual communication products that can enhance nocturnal tornado and flash flood warning reception and decision-making among diverse populations.
Kelsey Ellis, Jennifer First, J. Brian Houston
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Community Preparedness & Resilience, Decision-Making, Language & Cultural Contexts, Risk Communication, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
SSP
Interviews, Surveys
Mixed Methods
Forecasters, Public, Vulnerable Populations
This project will provide more comprehensive knowledge into various factors that place Southeast populations at increased risk for harm during nocturnal rapid-onset hazards (flash floods and tornadoes), and develop multilingual, visual risk communication products to improve nocturnal warning reception and response with diverse populations.
This project will provide essential knowledge into the various factors that place populations at increased risk for harm during nocturnal hazards in the Southeast, improve information ecosystems to support weather-related decision-making among vulnerable population during nocturnal flash flood and tornadoes, and ensure the needs of diverse and vulnerable populations are included in the nocturnal hazard warning process.
AFS
Mark Glaudemans
Water Resources Services Branch
mark.glaudemans@noaa.gov
University of Tennessee
University of Missouri
NA23OAR4590367
Severe Weather, Water Extremes
Missouri, Tennessee
Social Science Program
August 2023 - July 2026
Community Preparedness & Resilience, Decision-Making, Language & Cultural Contexts, Risk Communication, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Visual Risk Communication, Interviews, Surveys, Mixed Methods, Forecasters, Public, Vulnerable Populations
while you were sleeping improving warning reception and response to nocturnal rapid onset hazards in the southeast, the goal of this project is to develop visual communication products that can enhance nocturnal tornado and flash flood warning reception and decision making among diverse populations, this project will provide more comprehensive knowledge into various factors that place southeast populations at increased risk for harm during nocturnal rapid onset hazards flash floods and tornadoes and develop multilingual visual risk communication products to improve nocturnal warning reception and response with diverse populations
NWS Analyze, Forecast, and Support Office (AFS)
University of Tennessee, University of Missouri
The goal of this project is to develop visual communication products that can enhance nocturnal tornado and flash flood warning reception and decision-making among diverse populations.
1
Extreme Heat during Pregnancy: Identifying, Developing and Testing a Media Toolkit
Zottarelli
Social Science
search_activity
2023
Level 3
The goal of this project is to better understand the weather information ecosystem for English- and Spanish-speaking pregnant people, and develop and test an extreme heat media toolkit.
Lisa Zottarelli, Robyn Stassen, Shamshad Khan, Andrea Shields, Thankam Sunil
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Flow of Weather Risk Information, Decision Support, Language & Cultural Contexts, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Surveys, Delphi Group Method
Mixed Methods
Experts, Public
This project will generate new knowledge by identifying the weather information ecosystem for English- and Spanish-speaking pregnant people and using that knowledge to support the development and testing of an Extreme Heat & Pregnancy media toolkit.
This project will provide knowledge about weather information seeking behaviors among pregnant people, and more importantly, result in a tailored and culturally specific Extreme Heat and Pregnancy media tool kit for use within the information ecosystem to cue heat health protective behaviors among pregnant people. This case study of pregnant people can also be scaled to explore and better understand how to collect weather information ecosystem data and develop media toolkits for other underserved populations.
AFS
Kim McMahon
NWS Public Weather Services Program
kimberly.mcmahon@noaa.gov
University of Tennessee
San Antonio College
University of Texas at San Antonio
University of Connecticut
NA23OAR4590368
Extreme Temperatures
Connecticut, Tennessee, Texas
Social Science Program
August 2023 - July 2026
Flow of Weather Risk Information, Decision Support, Language & Cultural Contexts, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Surveys, Delphi Group Method, Mixed Methods, Experts, Public
extreme heat during pregnancy identifying developing and testing a media toolkit, the goal of this project is to better understand the weather information ecosystem for english and spanish speaking pregnant people and develop and test an extreme heat media toolkit, this project will generate new knowledge by identifying the weather information ecosystem for english and spanish speaking pregnant people and using that knowledge to support the development and testing of an extreme heat pregnancy media toolkit
NWS Analyze, Forecast, and Support Office (AFS)
University of Tennessee, San Antonio College, University of Texas at San Antonio, University of Connecticut
The goal of this project is to better understand the weather information ecosystem for English- and Spanish-speaking pregnant people, and develop and test an extreme heat media toolkit.
0
Baseline Heat Knowledge: A National Survey
Olson
Social Science
search_activity
2023
Level 3
The goal of this project is to provide a baseline assessment of how people access NWS heat information, and determine the public's understanding of heat risks.
Michele Olson, Jeannette Sutton
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Flow of Weather Risk Information, Risk Communication, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Surveys, Focus Groups
Mixed Methods
Public, Vulnerable Populations
This project aims to improve NWS heat communication by providing a baseline assessment of how their current heat information is accessed, and how the hazards, its impact, and vulnerable populations are under by publics and heat vulnerable populations in the continental United States.
This project will provide recommendations for potential changes to how the NWS and the broader weather enterprise communicate heat risks. This might include suggestions for improvements to language contained in heat-related watch, warning, and advisory products; enhanced seasonal safety campaign materials and NWS national safety webpages, and nationally supported social media infographics and materials. This may also include recommendations for impact-based decision support services communication strategies to NWS core partners.
AFS
Kim McMahon
NWS Public Weather Services Program
kimberly.mcmahon@noaa.gov
baseline heat knowledge a national survey, the goal of this project is to provide a baseline assessment of how people access nws heat information and determine the public's understanding of heat risks, this project aims to improve nws heat communication by providing a baseline assessment of how their current heat information is accessed and how the hazards its impact and vulnerable populations are under by publics and heat vulnerable populations in the continental united states
NWS Analyze, Forecast, and Support Office (AFS)
SUNY-Albany
The goal of this project is to provide a baseline assessment of how people access NWS heat information, and determine the public's understanding of heat risks.
0
BIL: Integrating Social and Meteorological Data to Assess the Dynamics of Flood Hazards and Impacts: An Interdisciplinary Approach to Leveraging AI, Risk Communication, and Data Sciences
McGovern
Social Science
search_activity
2023
Level 3
The goal of this project is to explore using AI/ML to integrate physical and social science data to enhance our understanding of risk perceptions, response, and impacts related to inland flooding hazards, while also expanding their longitudinal survey methodology to collect real-time data during such events.
Amy McGovern, Ann Bostrom, Julie Demuth, David John Gagne, Gabrielle Wong-Parodi, John Williams
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
This project aims to provide insights into how physical and social science data can be integrated to better understand risk perceptions, response, and impacts to inland flooding hazards. Additionally, this project aims to expand their previously developed longitudinal survey methodology to collect real-time data on people's risk perceptions, response, and impacts to inland flooding events.
This project will enhance our understanding of how to effectively scale the collection and analysis of longitudinal, real-time survey data during ongoing weather events. Additionally, it seems to provide successful examples of integrating both social and meteorological data to evaluate changes across various time and spatial scales, while also exploring the potential of AI in supporting these endeavors.
OSTI
AI2ES/University of Oklahoma
University of Washington
National Center for Atmospheric Research
Stanford University
The Weather Company/IBM
bil integrating social and meteorological data to assess the dynamics of flood hazards and impacts an interdisciplinary approach to leveraging ai risk communication and data sciences, the goal of this project is to explore using ai ml to integrate physical and social science data to enhance our understanding of risk perceptions response and impacts related to inland flooding hazards while also expanding their longitudinal survey methodology to collect real time data during such events, this project aims to provide insights into how physical and social science data can be integrated to better understand risk perceptions response and impacts to inland flooding hazards additionally this project aims to expand their previously developed longitudinal survey methodology to collect real time data on people's risk perceptions response and impacts to inland flooding events
NWS Office of Science and Technology Integration (OSTI)
AI2ES/University of Oklahoma, University of Washington, National Center for Atmospheric Research, Stanford University, The Weather Company/IBM
The goal of this project is to explore using AI/ML to integrate physical and social science data to enhance our understanding of risk perceptions, response, and impacts related to inland flooding hazards, while also expanding
2
The Impact of Weather Forecasts on Small Business Performance:
Methods and Evidence from Atmospheric River (AR) Forecasts in the American West
Nagaraj
Social Science
search_activity
2024
Level 3
This goal of this project is to quantify the economic value of atmospheric river (AR) forecasts on small businesses in the American West, using experiments to compare business outcomes with and without accurate forecasts.
Abhishek Nagaraj, Weilong Wang
Active
Sun Sep 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon Aug 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Precipitation Grand Challenge, Water in the West, Economic Valuation, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Economic Modeling
Quantitative
N/A
This project quantifies the economic value of atmospheric river (AR) forecasts on small businesses in the American West, using natural experiments to compare business outcomes with and without accurate forecasts. The goal is to inform future investments in weather forecasting to mitigate AR impacts on vulnerable communities and businesses.
This project aims to enhance economic resilience by quantifying the value of accurate AR forecasts, reducing economic losses for small businesses, and guiding future investments in weather forecasting.
OSTI
National Bureau of Economic Research
University of California Berkeley
Columbia University
NA24OARX015C0001
Water Extremes
California
Social Science Program
September 2024 - August 2026
Precipitation Grand Challenge, Water in the West, Economic Valuation, Social Vulnerability, "Social Science (SBES)", Economic Modeling, Quantitative
the impact of weather forecasts on small business performance methods and evidence from atmospheric river ar forecasts in the american west, this goal of this project is to quantify the economic value of atmospheric river ar forecasts on small businesses in the american west using experiments to compare business outcomes with and without accurate forecasts, this project quantifies the economic value of atmospheric river ar forecasts on small businesses in the american west using natural experiments to compare business outcomes with and without accurate forecasts the goal is to inform future investments in weather forecasting to mitigate ar impacts on vulnerable communities and businesses
NWS Office of Science and Technology Integration (OSTI)
National Bureau of Economic Research, University of California Berkeley, Columbia University
This goal of this project is to quantify the economic value of atmospheric river (AR) forecasts on small businesses in the American West, using experiments to compare business outcomes with and without accurate forecasts.
0
BIL: Navigating Compound Flood Risks: Enabling a Weather-Ready Nation through
Longitudinal Societal Data Collection and Analysis
Ripberger
Social Science
search_activity
2024
Level 3
The goal of this project is to enhance the collection of longitudinal social science data through the Extreme Weather and Society Survey and the Extreme Weather and Emergency Management Survey. This data will provide crucial insights on compound floods to better align weather services with user needs.
Joseph Ripberger, Makenzie Krocak
Active
Thu Aug 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Compound Hazards, Precipitation Grand Challenge, Decision Support, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Interviews, Surveys
Mixed Methods
Emergency Managers, Public
This project aims to enhance the National Weather Service's flood risk communication by collecting longitudinal social science data through the Extreme Weather and Society and Extreme Weather and Emergency Management surveys. The focus is on understanding user needs during compound flooding events to better align weather services with these needs.
By aligning weather information with user needs through systematic data collection, this research enhances NOAA's ability to develop effective risk communication strategies, identify best practices, and measure the impact of new programs and policies, ultimately improving public safety during extreme weather events.
OSTI, AFS
University of Oklahoma
National Severe Storms Laboratory
bil navigating compound flood risks enabling a weather ready nation through longitudinal societal data collection and analysis, the goal of this project is to enhance the collection of longitudinal social science data through the extreme weather and society survey and the extreme weather and emergency management survey this data will provide crucial insights on compound floods to better align weather services with user needs, this project aims to enhance the national weather service's flood risk communication by collecting longitudinal social science data through the extreme weather and society and extreme weather and emergency management surveys the focus is on understanding user needs during compound flooding events to better align weather services with these needs
University of Oklahoma, National Severe Storms Laboratory
The goal of this project is to enhance the collection of longitudinal social science data through the Extreme Weather and Society Survey and the Extreme Weather and Emergency Management Survey. This data will provide crucial
1
Messaging for a moving target: Advancing longitudinal social science observations to improve hazardous weather risk communication
Demuth
Social Science
search_activity
2024
Level 3
The goal of this project is to expand upon a previously developed methodology to collect longitudinal survey data collected during tropical cyclones and atmospheric rivers, and develop a framework and web-based platform to conduct near-real-time analyses of these social science data.
Julie Demuth, Gabrielle Wong-Parodi, Andrea Schumacher, David Novak, Robbie Berg, Wallace Hogsett, John Williams
Active
Sun Sep 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon Aug 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Precipitation Grand Challenge, Water in the West, Flow of Weather Risk Information, Decision Support, Risk Communication, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Surveys
Quantitative
Public
This project collects and analyzes longitudinal survey data during tropical cyclones and atmospheric river events. It aims to enhance data utility for the Weather Enterprise through cross-sector collaboration, developing near-real-time analysis frameworks, and creating a web-based platform for public data access and interpretation.
This research enhances understanding of public responses to tropical cyclones and atmospheric river events, develops a robust social science database for community use, informs effective risk messaging to reduce harm, and facilitates broad access to important longitudinal data, ultimately improving public safety and communication strategies.
NHC, WPC
National Center for Atmospheric Research
Stanford University
NWS Weather Prediction Center
NWS National Hurricane Center
The Weather Company
NA23OAR4310383B-T1-01
Water Extremes
California, Colorado, Florida, Washington
Social Science Program
September 2024 - August 2026
Precipitation Grand Challenge, Water in the West, Flow of Weather Risk Information, Decision Support, Risk Communication, Risk Perception & Response, "Social Science (SBES)", Surveys, Quantitative, Public
messaging for a moving target advancing longitudinal social science observations to improve hazardous weather risk communication, the goal of this project is to expand upon a previously developed methodology to collect longitudinal survey data collected during tropical cyclones and atmospheric rivers and develop a framework and web based platform to conduct near real time analyses of these social science data, this project collects and analyzes longitudinal survey data during tropical cyclones and atmospheric river events it aims to enhance data utility for the weather enterprise through cross sector collaboration developing near real time analysis frameworks and creating a web based platform for public data access and interpretation
National Center for Atmospheric Research, Stanford University, NWS Weather Prediction Center, NWS National Hurricane Center, The Weather Company
The goal of this project is to expand upon a previously developed methodology to collect longitudinal survey data collected during tropical cyclones and atmospheric rivers, and develop a framework and web-based platform to conduct near-real-time
0
Integrating Mobility Data, Survey Responses, and Forecasting Information to Enhance Understanding of Decision-Making During Hazardous Weather Events in Communities
Li
Social Science
search_activity
2025
Level 3
The primary goal of the proposed project is to integrate cell-phone mobility data and survey responses to develop a comprehensive understanding of population mobility in response to hazardous weather information.
Liqing Li, Mani Rouhi Rad, Majid Shafiee-Jood
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2027 03:00:00 GMT-0400 (Eastern Daylight Time)
Decision Support, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
A database of hazardous weather information (warning and evacuation orders) for hurricanes, wildfires, and flash floods will be created to study its effects on mobility responses. Econometric methods for casual interference and cell-phone mobility data will be used to analyze how diverse socioeconomic groups react to hazardous weather information for both short- and long-fused weather events. Surveys will be integrated with mobility data to address biases in existing evacuation analyses, to enhance understanding of mobility responses from underserved populations, and to estimate variations in evacuation costs and the impact of forecast accuracy on decision-making.
This project will enhance preparedness and responses to hazardous weather, particularly for underserved communities by providing research-based recommendations to improve evaluation tools, the understanding of socioeconomic disparities in hazardous weather event responses, and the forecast accuracy impacts. The creation of a comprehensive dataset will provide resources for refining warning systems and decision-making.
integrating mobility data survey responses and forecasting information to enhance understanding of decision making during hazardous weather events in communities, the primary goal of the proposed project is to integrate cell phone mobility data and survey responses to develop a comprehensive understanding of population mobility in response to hazardous weather information, a database of hazardous weather information warning and evacuation orders for hurricanes wildfires and flash floods will be created to study its effects on mobility responses econometric methods for casual interference and cell phone mobility data will be used to analyze how diverse socioeconomic groups react to hazardous weather information for both short and long fused weather events surveys will be integrated with mobility data to address biases in existing evacuation analyses to enhance understanding of mobility responses from underserved populations and to estimate variations in evacuation costs and the impact of forecast accuracy on decision making
Texas A&M University, University of Virginia
The primary goal of the proposed project is to integrate cell-phone mobility data and survey responses to develop a comprehensive understanding of population mobility in response to hazardous weather information.
0
Toward a ColdRisk Messaging Framework: Research to inform the development and scalability of an extreme cold decision support tool for Washington state
Errett
Social Science
search_activity
2025
Level 3
The goal of this project is to address decision support needs of the NWS response partners during extreme cold events (ECEs) in Washington State by integrating an analysis of the health impacts of ECEs with collaboratively identified intervention points and response strategies, and recommendations on information needs.
Nicole Errett, Reid Wolcott, Jamie Vickery, Jebb Steward, Tania Busch Isaksen, Joan Casey, Ann Bostrom, Bradley Kramer
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2027 03:00:00 GMT-0400 (Eastern Daylight Time)
Decision Support, Risk Communication, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
ECEs in traditionally temperate Washington State lead to widespread health and infrastructure impacts. This project will integrate outcome-specific emergency department visit data with weather, sociodemographic, and power outage data in a case-crossover design; engage regional response partners in facilitated discussions to identify intervention points and response strategies; and conduct and analyze key informant interviews with regional response partners about ECE decision making.
This project will foster a collaborative network of response partners in the region. Response partners will be more equipped with tailored risk communication and response strategies which will ultimately reduce the health impacts of ECEs particularly for vulnerable populations.
University of Washington
National Weather Service
Global Systems Laboratory
Seattle & King County
NA25OARX459C0309-T1-01
Extreme Temperatures
Washington
Social Science Program
August 2025 - July 2027
Decision Support, Risk Communication, Social Vulnerability, "Social Science (SBES)"
toward a coldrisk messaging framework research to inform the development and scalability of an extreme cold decision support tool for washington state, the goal of this project is to address decision support needs of the nws response partners during extreme cold events eces in washington state by integrating an analysis of the health impacts of eces with collaboratively identified intervention points and response strategies and recommendations on information needs, eces in traditionally temperate washington state lead to widespread health and infrastructure impacts this project will integrate outcome specific emergency department visit data with weather sociodemographic and power outage data in a case crossover design engage regional response partners in facilitated discussions to identify intervention points and response strategies and conduct and analyze key informant interviews with regional response partners about ece decision making
University of Washington, National Weather Service, Global Systems Laboratory, Seattle & King County
The goal of this project is to address decision support needs of the NWS response partners during extreme cold events (ECEs) in Washington State by integrating an analysis of the health impacts of ECEs with
0
Human Health Impacts of Hurricane and Hurricane Forecasts
Molina
Social Science
search_activity
2025
Level 3
The goal of this project is to quantify the human health impacts of hurricanes and evaluate how accurate and timely hurricane forecasts mitigate these effects.
Renato Molina, Alex Hollingsworth
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2027 03:00:00 GMT-0400 (Eastern Daylight Time)
This project combines high-resolution health and hurricane data with state-of-the-art econometrics to establish the causal effects of hurricanes on health, beyond mortality, to establish the health benefits of forecast information, and to develop replicable methodology for socio-economic health benefit assessments of other forecast products.
This project will identify the causal impacts of hurricanes on health, demonstrate the life-saving value of forecast accuracy, and support NOAA in enhancing resilience by informing strategic forecast investments. Outcomes include improved public health planning and disaster preparedness.
human health impacts of hurricane and hurricane forecasts, the goal of this project is to quantify the human health impacts of hurricanes and evaluate how accurate and timely hurricane forecasts mitigate these effects, this project combines high resolution health and hurricane data with state of the art econometrics to establish the causal effects of hurricanes on health beyond mortality to establish the health benefits of forecast information and to develop replicable methodology for socio economic health benefit assessments of other forecast products
University of Miami, Ohio State University
The goal of this project is to quantify the human health impacts of hurricanes and evaluate how accurate and timely hurricane forecasts mitigate these effects.
0
Virtual Risks and Hero Projects: Activating Transformative Community Actions for Preparedness Against Extreme Weather Events through Extended Reality and Artificial Intelligence
Ahn
Social Science
search_activity
2025
Level 3
This project's goal is to integrate mixed-reality/immersive simulations and an AI-driven with a community-based participatory research approach to amplify risk communication, co-develop transformative action plans for emergency preparedness, and assist decision-making during hazardous weather events, ultimately increasing community resilience against hurricanes and wildfires in Florida and Oregon.
Sun Joo Ahn, Kyle Johnsen, James Marshall, Daniel Pimentel
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2027 03:00:00 GMT-0400 (Eastern Daylight Time)
This project aims to enhance community resilience against extreme weather events like hurricanes and wildfires by integrating mixed-reality simulations, an AI-driven decision-making tool, and a community-based participatory research approach. Through immersive simulations, the project seeks to localize the experience of risk, while "hero projects" will empower communities in Florida and Oregon to co-develop transformative action plans for emergency preparedness. The tool will further assist decision-making by synthesizing critical weather information, ultimately aiming to overcome individual and community vulnerabilities to weather risks and minimize harm.
This project will amplify risk communication through mixed-reality simulations, co-develop transformative action plans for emergency preparedness with key stakeholders, and create an AI-driven tool to assist decision-making during hazardous weather events. These efforts will ultimately increase community resilience against hurricanes and wildfires in Florida and Oregon, minimizing harm to life and property.
virtual risks and hero projects activating transformative community actions for preparedness against extreme weather events through extended reality and artificial intelligence, this project's goal is to integrate mixed reality immersive simulations and an ai driven with a community based participatory research approach to amplify risk communication co develop transformative action plans for emergency preparedness and assist decision making during hazardous weather events ultimately increasing community resilience against hurricanes and wildfires in florida and oregon, this project aims to enhance community resilience against extreme weather events like hurricanes and wildfires by integrating mixed reality simulations an ai driven decision making tool and a community based participatory research approach through immersive simulations the project seeks to localize the experience of risk while "hero projects" will empower communities in florida and oregon to co develop transformative action plans for emergency preparedness the tool will further assist decision making by synthesizing critical weather information ultimately aiming to overcome individual and community vulnerabilities to weather risks and minimize harm
University of Georgia, University of Oregon
This project's goal is to integrate mixed-reality/immersive simulations and an AI-driven with a community-based participatory research approach to amplify risk communication, co-develop transformative action plans for emergency preparedness, and assist decision-making during hazardous weather events,
0
Advancing Natural-Human System Models to Integrate Hurricane Forecasts, Data, and Impacts
Harris
Social Science
search_activity
Level 3
The goals of this project are to support NOAA, NHC, and the weather community by using FLEE to evaluate the impact of forecast changes on evacuation successes; running evacuation scenarios for NHC to support evacuation order decisions; expanding FLEE's capacity to use different types of forecast information; and advancing FLEE's potential for real-time use.
FLEE's current version will be used as a virtual laboratory to simulate different forecast and evacuation order scenarios. Concurrently, the project team will advance the model and build towards real-time use by ingesting new types of forecast datasets and improving the model code. The project team will then use FLEE to conduct additional analyses.
Results will advance the capacity to link forecasts with societal outcomes. The project benefits NOAA by supporting forecasters communicating the potential impacts of different forecast-evacuation scenarios, enabling new ways of evaluating forecast skill, and providing a framework to expand FLEE to new regions and hazards.
National Center for Atmospheric Research
University of Wisconsin-Milwaukee
advancing natural human system models to integrate hurricane forecasts data and impacts, the goals of this project are to support noaa nhc and the weather community by using flee to evaluate the impact of forecast changes on evacuation successes running evacuation scenarios for nhc to support evacuation order decisions expanding flee's capacity to use different types of forecast information and advancing flee's potential for real time use, flee's current version will be used as a virtual laboratory to simulate different forecast and evacuation order scenarios concurrently the project team will advance the model and build towards real time use by ingesting new types of forecast datasets and improving the model code the project team will then use flee to conduct additional analyses
National Center for Atmospheric Research, University of Wisconsin-Milwaukee
The goals of this project are to support NOAA, NHC, and the weather community by using FLEE to evaluate the impact of forecast changes on evacuation successes; running evacuation scenarios for NHC to support evacuation
0
Understanding How Businesses Process and Prioritize Hazardous Weather Information to Make Protective Action Decisions for Employees and Patrons
Saunders
Social Science
search_activity
2025
Level 3
The goal of this project is to understand how businesses perceive, integrate, process, and prioritize hazardous weather information and set thresholds for the quantity and frequency of weather information, including probabilistic information, when making protective action decisions for employees and patrons. What severe weather safety plans are in place and how are the executed?
Michelle Saunders
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Community Preparedness & Resilience, Decision-Making, Personalization of Weather Information, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Using a mixed-methods approach, qualitative interviews will be conducted with small businesses across Mississippi. Interview analyses will inform the creation of a survey that will collect data from businesses across the southeastern US. Both datasets will inform the creation of best practices and materials for hazardous weather preparedness.
The project team expects that receiving probabilistic weather information will improve decision making. Many businesses may not have formal hazardous weather plans and will benefit from resources and materials produced from this study.
Mississippi State University
NA25OARX459C0278-T1-01
Severe Weather, Relevant to All Hazards
Mississippi
Social Science Program
August 2025 - July 2025
Community Preparedness & Resilience, Decision-Making, Personalization of Weather Information, Risk Perception & Response, "Social Science (SBES)"
understanding how businesses process and prioritize hazardous weather information to make protective action decisions for employees and patrons, the goal of this project is to understand how businesses perceive integrate process and prioritize hazardous weather information and set thresholds for the quantity and frequency of weather information including probabilistic information when making protective action decisions for employees and patrons what severe weather safety plans are in place and how are the executed?, using a mixed methods approach qualitative interviews will be conducted with small businesses across mississippi interview analyses will inform the creation of a survey that will collect data from businesses across the southeastern us both datasets will inform the creation of best practices and materials for hazardous weather preparedness
Mississippi State University
The goal of this project is to understand how businesses perceive, integrate, process, and prioritize hazardous weather information and set thresholds for the quantity and frequency of weather information, including probabilistic information, when making protective
0
Emergency Alert Systems: Exploring the Perspectives and Behavior of Populations with Limited-English Proficiency (LEP)
Painter
Social Science
search_activity
2025
Level 3
The goal of this project is to understand how populations with limited English proficiency (LEP) - specifically Spanish, Chinese, and Vietnamese speakers - receive, engage, perceive, and respond to emergency information related to extreme weather events.
Mary Angelica Painter, Carson MacPherson-Krutsky
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Community Preparedness & Resilience, Flow of Weather Risk Information, Language & Cultural Contexts, "Organizational Dimensions (e.g., culture, management, communication)", Risk Communication, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
SSP
Though efforts have improved the availability of emergency information across many channels, it remains unclear whether LEP populations are aware of emergency weather alerts or how alert systems could be improved to reduce information barriers. This project, through focus groups, aims to identify common themes and factors that influence how LEP populations receive and process emergency weather alerts and information and identify opportunities for improvement.
This project will provide critical insights into how emergency alerts reach populations with LEP during several weather. Benefits include refinement of communication channels and practices, with more appropriate messaging guidance and improved trust between alerting authorities and the public to ensure emergency messaging is effective and reduces harm.
Natural Hazards Center, University of Colorado Boulder
NA25OARX459C0277-T1-01
Relevant to All Hazards
Colorado
Social Science Program
August 2025 - July 2025
Community Preparedness & Resilience, Flow of Weather Risk Information, Language & Cultural Contexts, "Organizational Dimensions (e.g., culture, management, communication)", Risk Communication, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)"
emergency alert systems exploring the perspectives and behavior of populations with limited english proficiency lep, the goal of this project is to understand how populations with limited english proficiency lep specifically spanish chinese and vietnamese speakers receive engage perceive and respond to emergency information related to extreme weather events, though efforts have improved the availability of emergency information across many channels it remains unclear whether lep populations are aware of emergency weather alerts or how alert systems could be improved to reduce information barriers this project through focus groups aims to identify common themes and factors that influence how lep populations receive and process emergency weather alerts and information and identify opportunities for improvement
Natural Hazards Center, University of Colorado Boulder
The goal of this project is to understand how populations with limited English proficiency (LEP) - specifically Spanish, Chinese, and Vietnamese speakers - receive, engage, perceive, and respond to emergency information related to extreme weather
0
JCSDA EPIC Funded Projects
Gibbs
EPIC
search_activity
2024
Level 3
Use/develop the the Joint Center for Satellite Data Assimilation's (JCSDA) Joint Efforts for Data Assimilation Integration (JEDI) framework which is an integral component in enabling both the operational (i.e., Global Data Assimilation System (GDAS) Testbed Application (TA)) and research communities to work together to address specific scientific challenges
Mon Jun 30 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC)
EPIC
Unified Forecast System (UFS)
The JEDI framework is crucial in facilitating collaboration between the operational and research communities to tackle specific scientific challenges. Key areas of interest and collaboration relevant to the EPIC program include project management and administration, Spack-stack, data assimilation development and optimization, testing via Skylab, continuous integration, CRTM improvements, and helpdesk support.
Improving weather data, modeling, computing, forecasting, and warnings for the protection of life and property and for the enhancement of the national economy, "accelerate community-developed scientific and technological enhancements into the operational applications for NWP, support innovation in modeling by allowing interested stakeholders to have easy and complete access to the models used by the Administration, continuous scientific developments for exploiting the full potential of the data
CPO
Kendra Hammond
UCAR
WPO24-EPIC-3
Relevant to All Hazards
Colorado, Maryland
Earth Prediction Innovation Center (EPIC)
July 2024 - June 2025
Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC)
jcsda epic funded projects, use develop the the joint center for satellite data assimilation's jcsda joint efforts for data assimilation integration jedi framework which is an integral component in enabling both the operational i e global data assimilation system gdas testbed application ta and research communities to work together to address specific scientific challenges, the jedi framework is crucial in facilitating collaboration between the operational and research communities to tackle specific scientific challenges key areas of interest and collaboration relevant to the epic program include project management and administration spack stack data assimilation development and optimization testing via skylab continuous integration crtm improvements and helpdesk support
UCAR
Use/develop the the Joint Center for Satellite Data Assimilation's (JCSDA) Joint Efforts for Data Assimilation Integration (JEDI) framework which is an integral component in enabling both the operational (i.e., Global Data Assimilation System (GDAS) Testbed
4
NOAA-NASA Joint Project on Reanalysis Production and Experimentation Project at PSL
Frolov
EPIC
search_activity
2024
Level 3
we propose to build a shared framework for reanalysis development and
experimentation, leveraging the existing capabilities of each agency,
S. Frolov, J. Whitaker, R. Gelaro, and D. Kleist
Active
Fri May 17 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, High-Performance Computing (HPC), Reanalysis & Reforecasting, Numerical Weather Prediction (NWP), Probabilistic Weather Forecasting, Verification & Validation, Decision Support
EPIC
Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
This project supports reanalysis production and experimentation. For more information please contact WPO. This project is funded through WPO Director's funds.
Reduction of systematic model errors; Access to standardized high-quality observational data; Improved use of satellite observations in cloudy and non-oceanic environments; Development of cost-effective strategies for coupled assimilation
PSL
NOAA/OAR/PSL, NASA/GSFC, NOAA/NWS/EMC
WPO24-DIR-2
Relevant to All Hazards
Colorado, Maryland
Earth Prediction Innovation Center (EPIC)
May 2024 - September 2025
Data Assimilation and Ensembles, High-Performance Computing (HPC), Reanalysis & Reforecasting, Numerical Weather Prediction (NWP), Probabilistic Weather Forecasting, Verification & Validation, Decision Support
noaa nasa joint project on reanalysis production and experimentation project at psl, we propose to build a shared framework for reanalysis development and experimentation leveraging the existing capabilities of each agency, this project supports reanalysis production and experimentation for more information please contact wpo this project is funded through wpo director's funds
NOAA/OAR/PSL, NASA/GSFC, NOAA/NWS/EMC
we propose to build a shared framework for reanalysis development and
experimentation, leveraging the existing capabilities of each agency,
0
Consortium for Advanced Data Assimilation Research and Education
Wang
EPIC
search_activity
2024
Level 3
create a Multi-University Consortium for Advanced Data Assimilation
Research and Education (CADRE). CADRE will partner closely with NOAA to significantly
advance DA education and research.
Xuguang Wang, David Stensrud, Jonathan Poterjoy, Zhaoxia Pu, Peter Jan Van Leeuwen, Sen Chiao
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC), High-Performance Computing (HPC)
EPIC
Global Data Assimilation System (GDAS), Global Ensemble Forecast System (GEFS), Hurricane Analysis and Forecast System (HAFS), Rapid Refresh Forecast System (RRFS), Seasonal Forecast System (SFS), Unified Forecast System (UFS), UFS-Season to Subseasonal (UFS-S2S)
The next-gen NOAA Unified Forecast System Data Assimilation (DA) faces significant challenges associated with earth system modeling and observations. Serious gaps in DA inhibit addressing these challenges. A a Multi-University Consortium for Advanced Data Assimilation Research and Education will partner closely with NOAA to advance DA education and research. Supported will be 12 DA research thrusts and their implementation to the UFS. The projects will deliver improvements to DA, the workforce, and improve short range to S2S forecasts.
UFS DA capabilities, addressing DA shortcomings, developing the DA workforce, University team working with NOAA
University of Oklahoma, University of Utah, Colorado State University, University of Maryland at College Park, Howard University, The Pennsylvania State University
Colorado, Maryland, Oklahoma, Pennsylvania, Utah, District of Columbia
Earth Prediction Innovation Center (EPIC)
April 2024 - April 2027
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC), High-Performance Computing (HPC)
consortium for advanced data assimilation research and education, create a multi university consortium for advanced data assimilation research and education cadre cadre will partner closely with noaa to significantly advance da education and research, the next gen noaa unified forecast system data assimilation da faces significant challenges associated with earth system modeling and observations serious gaps in da inhibit addressing these challenges a a multi university consortium for advanced data assimilation research and education will partner closely with noaa to advance da education and research supported will be 12 da research thrusts and their implementation to the ufs the projects will deliver improvements to da the workforce and improve short range to s2s forecasts
University of Oklahoma, University of Utah, Colorado State University, University of Maryland at College Park, Howard University, The Pennsylvania State University
create a Multi-University Consortium for Advanced Data Assimilation
Research and Education (CADRE). CADRE will partner closely with NOAA to significantly
advance DA education and research.
42
IRA B2-Modeling-OAR: Algorithm Development
Hu
EPIC
search_activity
2023
Level 3
The Inflation Reduction Act (IRA) (H.R. 5376, P.L. 117-169) provides a total of $3.3B to NOAA from FY 2022 through FY 2026. Per Sec. 40004, NOAA will advance "research, observation systems, modeling, forecasting, assessments, and dissemination of information to the public as it pertains to ocean and atmospheric processes related to weather, coasts, oceans, and climate
Ming Hu, Sergey Frolov
Active
Wed Mar 01 2023 03:00:00 GMT-0500 (Eastern Standard Time)
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC), Forecast Skill Metrics, High-Performance Computing (HPC), Numerical Weather Prediction (NWP), Verification & Validation, Decision-Making, Decision Support
WPO
Global Data Assimilation System (GDAS), Global Ensemble Forecast System (GEFS), Hurricane Analysis and Forecast System (HAFS), Rapid Refresh Forecast System (RRFS), Seasonal Forecast System (SFS), Unified Forecast System (UFS), UFS-Season to Subseasonal (UFS-S2S)
AI/ML projects: Advances in DA will significantly improve the skill of numerical weather prediction on the timescales of minutes to weeks. This proposal aligns with the Priorities of Weather Research (PWR) recommendation of establishing new methodologies and workforce development for data assimilation (PWR OD3). The project includes a focus on the data needed to improve the prediction of extremes, oceans, and ecosystems. This project includes:
DA algorithm development, in-kind commitment to JCSDA: Coordinating with the Joint Center for Satellite Data Assimilation's Joint Effort for Data assimilation Integration to increase new and current data assimilated into UFS-based applications and NOAA operational systems.
The skill of NOAA's regional and global model prediction systems can be improved with more effective use of satellite data, improved assimilation of near-surface fields within a rapidly-updating framework, leading to better use of satellite and other observational datasets and improved regional and global model forecasts, leverage emerging ML technologies to generate more accurate background-error estimates that will extract more information from observations and result in more accurate forecasts
NOAA/OAR/PSL, NOAA/OAR/GSL
Relevant to All Hazards
Colorado, Maryland
Earth Prediction Innovation Center (EPIC)
March 2023 - September 2027
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC), Forecast Skill Metrics, High-Performance Computing (HPC), Numerical Weather Prediction (NWP), Verification & Validation, Decision-Making, Decision Support
ira b2 modeling oar algorithm development, the inflation reduction act ira h r 5376 p l 117 169 provides a total of $3 3b to noaa from fy 2022 through fy 2026 per sec 40004 noaa will advance "research observation systems modeling forecasting assessments and dissemination of information to the public as it pertains to ocean and atmospheric processes related to weather coasts oceans and climate, ai ml projects advances in da will significantly improve the skill of numerical weather prediction on the timescales of minutes to weeks this proposal aligns with the priorities of weather research pwr recommendation of establishing new methodologies and workforce development for data assimilation pwr od3 the project includes a focus on the data needed to improve the prediction of extremes oceans and ecosystems this project includes da algorithm development in kind commitment to jcsda coordinating with the joint center for satellite data assimilation's joint effort for data assimilation integration to increase new and current data assimilated into ufs based applications and noaa operational systems
NOAA/OAR/PSL, NOAA/OAR/GSL
The Inflation Reduction Act (IRA) (H.R. 5376, P.L. 117-169) provides a total of $3.3B to NOAA from FY 2022 through FY 2026. Per Sec. 40004, NOAA will advance "research, observation systems, modeling, forecasting, assessments, and
0
JCSDA EPIC Funded Projects
Gibbs
EPIC
search_activity
2025
Level 3
Use/develop the the Joint Center for Satellite Data Assimilation's (JCSDA) Joint Efforts for Data Assimilation Integration (JEDI) framework which is an integral component in enabling both the operational (i.e., Global Data Assimilation System (GDAS) Testbed Application (TA)) and research communities to work together to address specific scientific challenges
Tue Jun 30 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC)
EPIC
Unified Forecast System (UFS)
The JEDI framework is crucial in facilitating collaboration between the operational and research communities to tackle specific scientific challenges. Key areas of interest and collaboration relevant to the EPIC program include project management and administration, Spack-stack, data assimilation development and optimization, testing via Skylab, continuous integration, CRTM improvements, and helpdesk support.
Improving weather data, modeling, computing, forecasting, and warnings for the protection of life and property and for the enhancement of the national economy, "accelerate community-developed scientific and technological enhancements into the operational applications for NWP, support innovation in modeling by allowing interested stakeholders to have easy and complete access to the models used by the Administration, continuous scientific developments for exploiting the full potential of the data
CPO
Kendra Hammond
UCAR
WPO25-EPIC-3
Relevant to All Hazards
Colorado, Maryland
Earth Prediction Innovation Center (EPIC)
July 2025 - June 2026
Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC)
jcsda epic funded projects, use develop the the joint center for satellite data assimilation's jcsda joint efforts for data assimilation integration jedi framework which is an integral component in enabling both the operational i e global data assimilation system gdas testbed application ta and research communities to work together to address specific scientific challenges, the jedi framework is crucial in facilitating collaboration between the operational and research communities to tackle specific scientific challenges key areas of interest and collaboration relevant to the epic program include project management and administration spack stack data assimilation development and optimization testing via skylab continuous integration crtm improvements and helpdesk support
UCAR
Use/develop the the Joint Center for Satellite Data Assimilation's (JCSDA) Joint Efforts for Data Assimilation Integration (JEDI) framework which is an integral component in enabling both the operational (i.e., Global Data Assimilation System (GDAS) Testbed
0
Consortium for Advanced Data Assimilation Research and Education
Wang
EPIC
search_activity
2024
Level 3
create a Multi-University Consortium for Advanced Data Assimilation
Research and Education (CADRE). CADRE will partner closely with NOAA to significantly
advance DA education and research.
Xuguang Wang, David Stensrud, Jonathan Poterjoy, Zhaoxia Pu, Peter Jan Van Leeuwen, Sen Chiao
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC), High-Performance Computing (HPC)
IRA
Global Data Assimilation System (GDAS), Global Ensemble Forecast System (GEFS), Hurricane Analysis and Forecast System (HAFS), Rapid Refresh Forecast System (RRFS), Seasonal Forecast System (SFS), Unified Forecast System (UFS), UFS-Season to Subseasonal (UFS-S2S)
The next-gen NOAA Unified Forecast System Data Assimilation (DA) faces significant challenges associated with earth system modeling and observations. Serious gaps in DA inhibit addressing these challenges. A a Multi-University Consortium for Advanced Data Assimilation Research and Education will partner closely with NOAA to advance DA education and research. Supported will be 12 DA research thrusts and their implementation to the UFS. The projects will deliver improvements to DA, the workforce, and improve short range to S2S forecasts.
UFS DA capabilities, addressing DA shortcomings, developing the DA workforce, University team working with NOAA
University of Oklahoma, University of Utah, Colorado State University, University of Maryland at College Park, Howard University, The Pennsylvania State University
Colorado, Maryland, Oklahoma, Pennsylvania, Utah, District of Columbia
Earth Prediction Innovation Center (EPIC)
April 2024 - April 2027
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC), High-Performance Computing (HPC)
consortium for advanced data assimilation research and education, create a multi university consortium for advanced data assimilation research and education cadre cadre will partner closely with noaa to significantly advance da education and research, the next gen noaa unified forecast system data assimilation da faces significant challenges associated with earth system modeling and observations serious gaps in da inhibit addressing these challenges a a multi university consortium for advanced data assimilation research and education will partner closely with noaa to advance da education and research supported will be 12 da research thrusts and their implementation to the ufs the projects will deliver improvements to da the workforce and improve short range to s2s forecasts
University of Oklahoma, University of Utah, Colorado State University, University of Maryland at College Park, Howard University, The Pennsylvania State University
create a Multi-University Consortium for Advanced Data Assimilation
Research and Education (CADRE). CADRE will partner closely with NOAA to significantly
advance DA education and research.
42
Augmentation of VORTEX-SE Intensive Observations Period Measurements with Infrasound Observations to Detect and Track Tornadoes
Talmadge
Observations
check_circle
2016
Level 3
To better understand the source and propagation of the infrasonic signal, its relevance to tracking and detection, and its possible relevance to tornadogenesis by deploying infrasound/low-frequency sensor to collect infrasound data from tornadoes.
Carrick Talmadge
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Dec 31 2017 03:00:00 GMT-0500 (Eastern Standard Time)
In-situ Observation Technologies and Sensors, Radar
IS
to deploy infrasound/low-frequency sensor arrays in support of the Intensive Observing Periods of the 2017 VORTEX-SE data collection campaign to collect infrasound data from tornadoes. This data will be analyzed and correlated with data from other sensors to better understand the source and propagation of the infrasonic signal, its relevance to tracking and detection, and its possible relevance to tornadogenesis.
more accurate determination of the source, strength and location, including the path of observed individual tornadoes
Research
N/A
University of Mississippi
NA16OAR4590204
Severe Weather
Mississippi
October 2016 - December 2017
In-situ Observation Technologies and Sensors, Radar
augmentation of vortex se intensive observations period measurements with infrasound observations to detect and track tornadoes, to better understand the source and propagation of the infrasonic signal its relevance to tracking and detection and its possible relevance to tornadogenesis by deploying infrasound low frequency sensor to collect infrasound data from tornadoes, to deploy infrasound low frequency sensor arrays in support of the intensive observing periods of the 2017 vortex se data collection campaign to collect infrasound data from tornadoes this data will be analyzed and correlated with data from other sensors to better understand the source and propagation of the infrasonic signal its relevance to tracking and detection and its possible relevance to tornadogenesis
University of Mississippi
To better understand the source and propagation of the infrasonic signal, its relevance to tracking and detection, and its possible relevance to tornadogenesis by deploying infrasound/low-frequency sensor to collect infrasound data from tornadoes.
0
Direct detection of tornadoes using Infrasound remote sensing: assessment of capabilities through comparison with dual polarization radar and other direct detection measurements
Rinehart
Observations
check_circle
2016
Level 3
To determine the effectiveness of infrasound technology in the direct detection of tornadoes within a long-term infrasonic testbed to be established over northern Alabama.
Hank Rinehart, Kevin Knupp
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Radar
IS
to determine the effectiveness of infrasound technology in the direct detection of tornadoes within a long-term infrasonic testbed to be established over northern Alabama, which will also represent the primary VORTEX-SE (VSE) domain in 2017
determine if precursor radar signatures are correlated with changes in infrasonic emissions, a correlation not comprehensively established in prior studies.
SWIRLL
N/A
General Atomics;
University of Alabama-Huntsville
NA16OAR4590205
NA16OAR4590206
Severe Weather
Alabama, California
October 2016 - September 2018
In-situ Observation Technologies and Sensors, Radar
direct detection of tornadoes using infrasound remote sensing assessment of capabilities through comparison with dual polarization radar and other direct detection measurements, to determine the effectiveness of infrasound technology in the direct detection of tornadoes within a long term infrasonic testbed to be established over northern alabama, to determine the effectiveness of infrasound technology in the direct detection of tornadoes within a long term infrasonic testbed to be established over northern alabama which will also represent the primary vortex se vse domain in 2017
General Atomics;, University of Alabama-Huntsville
To determine the effectiveness of infrasound technology in the direct detection of tornadoes within a long-term infrasonic testbed to be established over northern Alabama.
3
Evaluation and Improvements of Tornado Detection using Infrasound Remote Sensing: Comparative Analysis of Infrasound, Radar, Profiler, and Meteorological Data Sets, and Potential Impacts on NOAA/NWS Operations
Rinehart
Observations
check_circle
2017
Level 3
To determine the effectiveness of infrasound technology in the direct detection of tornadoes using a developing, long-term infrasonic testbed over northern Alabama.
Hank Rinehart, Kevin Knupp
Complete
Sun Oct 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Radar
IS
To determine the effectiveness of IS technology in the direct detection of tornadoes using a developing, long-term infrasonic testbed over northern Alabama, which will also represent the primary VORTEX-SE (VSE) domain in 2017.
Determine if precursor radar or visual signatures are associated with infrasonic emissions, a correlation not comprehensively established in prior studies.
SWIRLL
N/A
University of Alabama-Huntsville
NA17OAR4590167
NA17OAR4590169
Severe Weather
Alabama, California
October 2017 - September 2018
In-situ Observation Technologies and Sensors, Radar
evaluation and improvements of tornado detection using infrasound remote sensing comparative analysis of infrasound radar profiler and meteorological data sets and potential impacts on noaa nws operations, to determine the effectiveness of infrasound technology in the direct detection of tornadoes using a developing long term infrasonic testbed over northern alabama, to determine the effectiveness of is technology in the direct detection of tornadoes using a developing long term infrasonic testbed over northern alabama which will also represent the primary vortex se vse domain in 2017
University of Alabama-Huntsville
To determine the effectiveness of infrasound technology in the direct detection of tornadoes using a developing, long-term infrasonic testbed over northern Alabama.
0
Infrasound Detection of Tornadoes
Waxler
Observations
check_circle
2017
Level 3
To continue investigating the use of infrasound for detecting and tracking tornadoes and to determine the efficacy of tornado generated infrasound for use in severe weather warning systems.
Tue Dec 31 2019 03:00:00 GMT-0500 (Eastern Standard Time)
In-situ Observation Technologies and Sensors, Radar
IS
Arrays will be deployed again in 2018 to collect further data for analysis and correlation with data from other sensors to study the source and propagation of the infrasonic signal, its relevance to tracking and detection, and its possible relevance to tornadogenesis.
More accurately determine the source, strength and location, including the path of observed individual tornadoes
Research
N/A
University of Mississippi
NA17OAR4590168
Severe Weather
Mississippi
September 2017 - December 2019
In-situ Observation Technologies and Sensors, Radar
infrasound detection of tornadoes, to continue investigating the use of infrasound for detecting and tracking tornadoes and to determine the efficacy of tornado generated infrasound for use in severe weather warning systems, arrays will be deployed again in 2018 to collect further data for analysis and correlation with data from other sensors to study the source and propagation of the infrasonic signal its relevance to tracking and detection and its possible relevance to tornadogenesis
University of Mississippi
To continue investigating the use of infrasound for detecting and tracking tornadoes and to determine the efficacy of tornado generated infrasound for use in severe weather warning systems.
1
Airborne Phased Array Radar (APAR) Development and Risk Mitigation Project
Grubisic
Observations
check_circle
2017
Level 3
The goal of this project is to help the Earth Observing Laboratory (EOL) at the National Center for Atmospheric Research (NCAR) further reduce risks associated with the development of Airborne Phased Array Radar (APAR). The outcome of these activities will allow EOL to refine technical requirements, identify and address known risks, and implement mitigation strategies in the focused design phase.
Vanda Grubisic
Complete
Sun Oct 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Mar 31 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Radar
APAR
This proposal is focused on tasks that will help Earth Observing Laboratory (EOL) at the National Center for Atmospheric Research (NCAR)further reduce risks associated with the development of Airborne Phased Array Radar (APAR). The proposal is based on the APAR Technical Requirements Document (APAR TRD, April 2016) and the APAR Master Project Management Plan (APAR MP2, Feb 2017), which were developed by EOL over the last 18 months. Building on the detailed work that went into the preparation of the APAR Technical Requirements Document (TRD) and MP2 documents, EOL has selected four critical tasks that will have major positive impacts on clarifying the radar system design and reducing several risks identified in the development plans. Tasks include: i) an industry Trade Study to address specific technical questions about the APAR design; ii) an end-to-end demonstration of integrated antenna, transceiver, digital receiver, and radar control
and processing software; iii) an airframe vibration characterization of the NSF/NCAR C-130; and iv) a model simulation and evaluation of sampling performance of C-band active electronically scanned area (AESA) antennas using a prototype APAR Observation Simulator (AOS). EOL will utilize in-house expertise, experienced external consultants, and aerospace industry specialists to complete the proposed tasks. The outcome of these activities will allow EOL to refine technical requirements, identify and address known risks, and implement mitigation strategies in the focused design phase. Ultimately, APAR will be the first phased array radar to make weather observations from an aircraft; the only airborne weather radar to operate at C-band, allowing deeper penetration into clouds than traditional X-band radars; and the first airborne precipitation radar with polarimetric capability.
The outcomes from the four tasks (deliverables, specific test results, consultant reports, and
recommendations) will allow updates and adjustments to the APAR Technical Requirements Document,
which will include requirement level (mandatory or desirable), requirement description, and updates to
specific value or range of values to reflect study outcome. EOL anticipates that all of this work will also
result in updates to the MP2. Since these are deliverables resulting from this grant, they will be
completed by the end of the period of performance (Spring 2019).
NCAR
N/A
National Center for Atmospheric Research
NA17OAR0110333
Relevant to All Hazards
Colorado
October 2017 - March 2019
Aircraft Reconnaissance, Radar
airborne phased array radar apar development and risk mitigation project, the goal of this project is to help the earth observing laboratory eol at the national center for atmospheric research ncar further reduce risks associated with the development of airborne phased array radar apar the outcome of these activities will allow eol to refine technical requirements identify and address known risks and implement mitigation strategies in the focused design phase, this proposal is focused on tasks that will help earth observing laboratory eol at the national center for atmospheric research ncar further reduce risks associated with the development of airborne phased array radar apar the proposal is based on the apar technical requirements document apar trd april 2016 and the apar master project management plan apar mp2 feb 2017 which were developed by eol over the last 18 months building on the detailed work that went into the preparation of the apar technical requirements document trd and mp2 documents eol has selected four critical tasks that will have major positive impacts on clarifying the radar system design and reducing several risks identified in the development plans tasks include i an industry trade study to address specific technical questions about the apar design ii an end to end demonstration of integrated antenna transceiver digital receiver and radar control and processing software iii an airframe vibration characterization of the nsf ncar c 130 and iv a model simulation and evaluation of sampling performance of c band active electronically scanned area aesa antennas using a prototype apar observation simulator aos eol will utilize in house expertise experienced external consultants and aerospace industry specialists to complete the proposed tasks the outcome of these activities will allow eol to refine technical requirements identify and address known risks and implement mitigation strategies in the focused design phase ultimately apar will be the first phased array radar to make weather observations from an aircraft the only airborne weather radar to operate at c band allowing deeper penetration into clouds than traditional x band radars and the first airborne precipitation radar with polarimetric capability
National Center for Atmospheric Research
The goal of this project is to help the Earth Observing Laboratory (EOL) at the National Center for Atmospheric Research (NCAR) further reduce risks associated with the development of Airborne Phased Array Radar (APAR). The
0
Airborne Phased Array Radar (APAR) Development and Risk Mitigation Project
Atlas
Observations
check_circle
2017
Level 3
The goal of this project is to help the Earth Observing Laboratory (EOL) at the National Center for Atmospheric Research (NCAR) further reduce risks associated with the development of Airborne Phased Array Radar (APAR). The outcome of these activities will allow EOL to refine technical requirements, identify and address known risks, and implement mitigation strategies in the focused design phase.
Robert Atlas, Brian Dahl, W-C Lee
Complete
Thu Jun 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu May 31 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Radar
Obs
This proposal is focused on tasks that will help Earth Observing Laboratory (EOL) at the National Center for Atmospheric Research (NCAR)further reduce risks associated with the development of Airborne Phased Array Radar (APAR). The proposal is based on the APAR Technical Requirements Document (APAR TRD, April 2016) and the APAR Master Project Management Plan (APAR MP2, Feb 2017), which were developed by EOL over the last 18 months. Building on the detailed work that went into the preparation of the APAR Technical Requirements Document (TRD) and MP2 documents, EOL has selected four critical tasks that will have major positive impacts on clarifying the radar system design and reducing several risks identified in the development plans. Tasks include: i) an industry Trade Study to address specific technical questions about the APAR design; ii) an end-to-end demonstration of integrated antenna, transceiver, digital receiver, and radar control
and processing software; iii) an airframe vibration characterization of the NSF/NCAR C-130; and iv) a model simulation and evaluation of sampling performance of C-band active electronically scanned area (AESA) antennas using a prototype APAR Observation Simulator (AOS). EOL will utilize in-house expertise, experienced external consultants, and aerospace industry specialists to complete the proposed tasks. The outcome of these activities will allow EOL to refine technical requirements, identify and address known risks, and implement mitigation strategies in the focused design phase. Ultimately, APAR will be the first phased array radar to make weather observations from an aircraft; the only airborne weather radar to operate at C-band, allowing deeper penetration into clouds than traditional X-band radars; and the first airborne precipitation radar with polarimetric capability.
The outcomes from the four tasks (deliverables, specific test results, consultant reports, and
recommendations) will allow updates and adjustments to the APAR Technical Requirements Document,
which will include requirement level (mandatory or desirable), requirement description, and updates to
specific value or range of values to reflect study outcome. EOL anticipates that all of this work will also
result in updates to the MP2. Since these are deliverables resulting from this grant, they will be
completed by the end of the period of performance (Spring 2019).
NCAR
N/A
NOAA/OAR/AOML
CIMAS
NCAR
BAA
Relevant to All Hazards
Florida
June 2017 - May 2018
Aircraft Reconnaissance, Radar
airborne phased array radar apar development and risk mitigation project, the goal of this project is to help the earth observing laboratory eol at the national center for atmospheric research ncar further reduce risks associated with the development of airborne phased array radar apar the outcome of these activities will allow eol to refine technical requirements identify and address known risks and implement mitigation strategies in the focused design phase, this proposal is focused on tasks that will help earth observing laboratory eol at the national center for atmospheric research ncar further reduce risks associated with the development of airborne phased array radar apar the proposal is based on the apar technical requirements document apar trd april 2016 and the apar master project management plan apar mp2 feb 2017 which were developed by eol over the last 18 months building on the detailed work that went into the preparation of the apar technical requirements document trd and mp2 documents eol has selected four critical tasks that will have major positive impacts on clarifying the radar system design and reducing several risks identified in the development plans tasks include i an industry trade study to address specific technical questions about the apar design ii an end to end demonstration of integrated antenna transceiver digital receiver and radar control and processing software iii an airframe vibration characterization of the nsf ncar c 130 and iv a model simulation and evaluation of sampling performance of c band active electronically scanned area aesa antennas using a prototype apar observation simulator aos eol will utilize in house expertise experienced external consultants and aerospace industry specialists to complete the proposed tasks the outcome of these activities will allow eol to refine technical requirements identify and address known risks and implement mitigation strategies in the focused design phase ultimately apar will be the first phased array radar to make weather observations from an aircraft the only airborne weather radar to operate at c band allowing deeper penetration into clouds than traditional x band radars and the first airborne precipitation radar with polarimetric capability
NOAA/OAR/AOML, CIMAS, NCAR
The goal of this project is to help the Earth Observing Laboratory (EOL) at the National Center for Atmospheric Research (NCAR) further reduce risks associated with the development of Airborne Phased Array Radar (APAR). The
0
Improving National Water Model Snowmelt Runoff Prediction
Niu
Observations
check_circle
2018
Level 3
To improve the National Weather Model Snowmelt Runoff Prediction by developing a snow depletion curve for assimilating JPSS/VIIRS SCF and a bias-correction scheme for reducing errors in the input snowpack data.
Guo-Yue Niu, Michael Barlage
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Physics, Data Assimilation and Ensembles, Parameterization, Verification & Validation
SPSM
National Water Model (NWM), Weather Research and Forecasting Model (WRF), Weather Research and Forecasting Model - Hydro (WRF-Hydro)
Improve snowpack (SWE) physics parameterization in NWM/WRF-Hydro and will advance snow data assimilation for hydrologic prediction by developing a snow depletion curve for assimilating JPSS/VIIRS SCF and a bias-correction scheme for reducing errors in the input snowpack data.
Improve the operational capabilities of NOAA's National Water Center and contribute directly to its mission.
NWC
N/A
University of Arizona;
National Center for Atmospheric Research
NA18OAR4590397
NA18OAR4590398
Water Extremes
Arizona, California
September 2018 - August 2021
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Physics, Data Assimilation and Ensembles, Parameterization, Verification & Validation
improving national water model snowmelt runoff prediction, to improve the national weather model snowmelt runoff prediction by developing a snow depletion curve for assimilating jpss viirs scf and a bias correction scheme for reducing errors in the input snowpack data, improve snowpack swe physics parameterization in nwm wrf hydro and will advance snow data assimilation for hydrologic prediction by developing a snow depletion curve for assimilating jpss viirs scf and a bias correction scheme for reducing errors in the input snowpack data
National Water Center (NWC)
University of Arizona;, National Center for Atmospheric Research
To improve the National Weather Model Snowmelt Runoff Prediction by developing a snow depletion curve for assimilating JPSS/VIIRS SCF and a bias-correction scheme for reducing errors in the input snowpack data.
22
Evaluation and diagnosis of National Water Model simulations over CONUS using a novel snow reanalysis dataset
Andreadis
Observations
check_circle
2018
Level 3
To assess the representation SWE in the NWM and to utilize a state-of-the-art satellite-based dataset in order to advance "the use of snowpack (snow water equivalent) remote sensing data to improve the National Water Model".
Konstantinos Andreadis, Dennis Lettenmaier
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Extend a satellite-derived snow water equivalent (SWE) reanalysis (from Landsat, MODIS, and VIIRS) over the Continental United States (CONUS) and assess the representation of SWE in the National Water Model (NWM).
Improve the operational capabilities of NOAA's National Water Center and contribute directly to its mission.
evaluation and diagnosis of national water model simulations over conus using a novel snow reanalysis dataset, to assess the representation swe in the nwm and to utilize a state of the art satellite based dataset in order to advance "the use of snowpack snow water equivalent remote sensing data to improve the national water model", extend a satellite derived snow water equivalent swe reanalysis from landsat modis and viirs over the continental united states conus and assess the representation of swe in the national water model nwm
National Water Center (NWC)
University of California Los Angeles
To assess the representation SWE in the NWM and to utilize a state-of-the-art satellite-based dataset in order to advance "the use of snowpack (snow water equivalent) remote sensing data to improve the National Water Model".
5
Implementing Snow Data Assimilation Capabilities for the National Water Model and Experimental Assimilation of JPSS Observations of Snow Water Equivalent
Zhang
Observations
check_circle
2018
Level 3
Improve the operational capabilities of NOAA's National Water Center by implementing a snow water equivalent preprocessor and a background error covariance model which will enable NWM to assimilate a broad suite of snow observations.
Yu Zhang, Cezar Kongoli
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Satellite & Remote Sensing Technologies
SPSM
National Water Model (NWM)
Implement, within the framework of the Joint Center for Satellite Data Assimilation's (JCSDA) Joint Effort for Data assimilation Integration (JEDI), a snow water equivalent (SWE) preprocessor and a background error covariance model which will enable NWM to assimilate a broad suite of snow observations using ensemble Kalman filter (EnKF) or variational assimilation (VAR).
Improve the operational capabilities of NOAA's National Water Center and contribute directly to its mission.
NWC
N/A
University of Texas Arlington;
University of Maryland
NA18OAR4590410
NA18OAR4590411
Water Extremes
Maryland, Texas
September 2018 - August 2022
Data Assimilation and Ensembles, Satellite & Remote Sensing Technologies
implementing snow data assimilation capabilities for the national water model and experimental assimilation of jpss observations of snow water equivalent, improve the operational capabilities of noaa's national water center by implementing a snow water equivalent preprocessor and a background error covariance model which will enable nwm to assimilate a broad suite of snow observations, implement within the framework of the joint center for satellite data assimilation's jcsda joint effort for data assimilation integration jedi a snow water equivalent swe preprocessor and a background error covariance model which will enable nwm to assimilate a broad suite of snow observations using ensemble kalman filter enkf or variational assimilation var
National Water Center (NWC)
University of Texas Arlington;, University of Maryland
Improve the operational capabilities of NOAA's National Water Center by implementing a snow water equivalent preprocessor and a background error covariance model which will enable NWM to assimilate a broad suite of snow observations.
6
Use of MRMS-derived hydrometeor classification for determining initial hydrometeor phase in the National Water Model
Reeves
Observations
check_circle
2018
Level 3
To test whether using the Multi-Radar Multi-Sensor (MRMS) hydrometeor classification algorithm as input to the National Water Model (NWM) produces more accurate streamflow than the current temperature-only method and test the sensitivity to bin size and use of subhourly classification by the Spectral Bin Classifier (SBC).
Heather Reeves, Mimi Hughes, David Gochis
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Radar
SPSM
Multi-Radar/Multi-Sensor System (MRMS), National Water Model (NWM)
Test whether using the Multi-Radar Multi-Sensor (MRMS) hydrometeor classification algorithm as input to the National Water Model (NWM) produces more accurate streamflow than the current temperature-only method and test the sensitivity to bin size and use of subhourly classification by the Spectral Bin Classifier (SBC).
Improve the operational capabilities of NOAA's National Water Center and contribute directly to its mission.
NWC
N/A
University of Oklahoma;
Cooperative Institute for Research in Environmental Sciences;
National Center for Atmospheric Research
NA18OAR4590413
NA18OAR4590412
NA18OAR4590414
Water Extremes
Colorado, Oklahoma
September 2018 - August 2021
Radar
use of mrms derived hydrometeor classification for determining initial hydrometeor phase in the national water model, to test whether using the multi radar multi sensor mrms hydrometeor classification algorithm as input to the national water model nwm produces more accurate streamflow than the current temperature only method and test the sensitivity to bin size and use of subhourly classification by the spectral bin classifier sbc, test whether using the multi radar multi sensor mrms hydrometeor classification algorithm as input to the national water model nwm produces more accurate streamflow than the current temperature only method and test the sensitivity to bin size and use of subhourly classification by the spectral bin classifier sbc
National Water Center (NWC)
University of Oklahoma;, Cooperative Institute for Research in Environmental Sciences;, National Center for Atmospheric Research
To test whether using the Multi-Radar Multi-Sensor (MRMS) hydrometeor classification algorithm as input to the National Water Model (NWM) produces more accurate streamflow than the current temperature-only method and test the sensitivity to bin size
0
Experimental Framework for Testing the National Water Model: Operationalizing the Use of Snow Remote Sensing in Alaska
Bennett
Observations
check_circle
2018
Level 3
To test, validate and benchmark NWM model skill and recommend physics and parameter improvements to better capture the processes critical to hydrologic prediction in Alaska, focused on the modeling challenges of deep snowpack, permafrost and glaciers.
Katrina Bennett, Vladmir Alexeev, Aubrey Dugger
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Feb 29 2024 03:00:00 GMT-0500 (Eastern Standard Time)
Atmospheric Physics
SPSM
Community Hydrologic Prediction System (CHPS), National Water Model (NWM)
Develop a near-real-time data stream of simple, first-order assimilations of remotely sensed snow cover extent and snow water equivalent data in Alaska and operationalize components of this work for use in NOAA's Community Hydrologic Prediction System (CHPS) framework. Transfer this knowledge to the National Water Model (NWM) system and run experiments using the NWM model for key study watersheds representing a range of hydro-climatic regimes in Alaska.
Improve the operational capabilities of NOAA's National Water Center and contribute directly to its mission.
NWC
N/A
Los Alamos National Laboratory;
New Mexico Consortium;
University of Alaska Fairbanks;
Research Applications Lab
NA18OAR4590415
Water Extremes
Alaska, Colorado, New Mexico
September 2018 - February 2024
Atmospheric Physics
experimental framework for testing the national water model operationalizing the use of snow remote sensing in alaska, to test validate and benchmark nwm model skill and recommend physics and parameter improvements to better capture the processes critical to hydrologic prediction in alaska focused on the modeling challenges of deep snowpack permafrost and glaciers, develop a near real time data stream of simple first order assimilations of remotely sensed snow cover extent and snow water equivalent data in alaska and operationalize components of this work for use in noaa's community hydrologic prediction system chps framework transfer this knowledge to the national water model nwm system and run experiments using the nwm model for key study watersheds representing a range of hydro climatic regimes in alaska
National Water Center (NWC)
Los Alamos National Laboratory;, New Mexico Consortium;, University of Alaska Fairbanks;, Research Applications Lab
To test, validate and benchmark NWM model skill and recommend physics and parameter improvements to better capture the processes critical to hydrologic prediction in Alaska, focused on the modeling challenges of deep snowpack, permafrost and
0
Identification of the Fluid Mechanisms Associated with Tornadic Storm Infrasound
Elbing
Observations
check_circle
2018
Level 3
To more accurately determine the source, strength, location, and path of observed individual tornadoes by identifying the fluid mechanism(s) associated with tornadic storm infrasound.
Brian Elbing
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Radar
IS
Identify the fluid mechanism(s) associated with tornadic storm infrasound.
More accurately determine the source, strength and location, including the path of observed individual tornadoes.
Research
N/A
Oklahoma State University
NA18OAR4590307
Severe Weather
Oklahoma
September 2018 - August 2021
In-situ Observation Technologies and Sensors, Radar
identification of the fluid mechanisms associated with tornadic storm infrasound, to more accurately determine the source strength location and path of observed individual tornadoes by identifying the fluid mechanism s associated with tornadic storm infrasound, identify the fluid mechanism s associated with tornadic storm infrasound
Oklahoma State University
To more accurately determine the source, strength, location, and path of observed individual tornadoes by identifying the fluid mechanism(s) associated with tornadic storm infrasound.
5
Infrasound Detection of Tornadoes (FY18)
Waxler
Observations
check_circle
2018
Level 3
To more accurately determine the source, strength, location, and path of observed individual tornadoes by deploying infrasonic sensing arrays to collect further data for analysis and correlation with data from other sensors to study the source and propagation of the infrasonic signal, its relevance to tracking and detection, and its possible relevance to tornadogenesis.
Roger Waxler
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Radar
IS
Infrasonic sensing arrays will be deployed again in 2019 and 2020 to collect further data for analysis and correlation with data from other sensors to study the source and propagation of the infrasonic signal, its relevance to tracking and detection, and its possible relevance to tornadogenesis.
More accurately determine the source, strength and location, including the path of observed individual tornadoes.
Research
N/A
University of Mississippi
NA18OAR4590305
Severe Weather
Mississippi
September 2018 - August 2021
In-situ Observation Technologies and Sensors, Radar
infrasound detection of tornadoes fy18, to more accurately determine the source strength location and path of observed individual tornadoes by deploying infrasonic sensing arrays to collect further data for analysis and correlation with data from other sensors to study the source and propagation of the infrasonic signal its relevance to tracking and detection and its possible relevance to tornadogenesis, infrasonic sensing arrays will be deployed again in 2019 and 2020 to collect further data for analysis and correlation with data from other sensors to study the source and propagation of the infrasonic signal its relevance to tracking and detection and its possible relevance to tornadogenesis
University of Mississippi
To more accurately determine the source, strength, location, and path of observed individual tornadoes by deploying infrasonic sensing arrays to collect further data for analysis and correlation with data from other sensors to study the
1
Prediction and Measurement of Infrasound Propagation in the Turbulent Atmosphere
Miller
Observations
check_circle
2018
Level 3
To more accurately determine the source, strength, location, and path of observed individual tornadoes by quantifying the alteration of the propagation of infrasound due to the atmospheric turbulent boundary layer under various conditions.
Steven Miller
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Feb 28 2021 03:00:00 GMT-0500 (Eastern Standard Time)
In-situ Observation Technologies and Sensors, Radar
IS
Quantify the alteration of the propagation of infrasound due to the atmospheric turbulent boundary layer under various conditions
More accurately determine the source, strength and location, including the path of observed individual tornadoes.
Research
N/A
University of Florida
NA18OAR4590306
Severe Weather
Florida
September 2018 - February 2021
In-situ Observation Technologies and Sensors, Radar
prediction and measurement of infrasound propagation in the turbulent atmosphere, to more accurately determine the source strength location and path of observed individual tornadoes by quantifying the alteration of the propagation of infrasound due to the atmospheric turbulent boundary layer under various conditions, quantify the alteration of the propagation of infrasound due to the atmospheric turbulent boundary layer under various conditions
University of Florida
To more accurately determine the source, strength, location, and path of observed individual tornadoes by quantifying the alteration of the propagation of infrasound due to the atmospheric turbulent boundary layer under various conditions.
1
2019 - 2020 Airborne Phased Array Radar (APAR)
Grubisic
Observations
check_circle
2018
Level 3
To develop an Airborne Phased Array Radar (APAR) for deployment on the NSF/NCAR C-130 aircraft in order to improve forecasts, warnings, and decision support for high-impact weather events.
Vanda Grubisic
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Nov 30 2021 03:00:00 GMT-0500 (Eastern Standard Time)
Aircraft Reconnaissance, Radar, Decision Support
APAR
Develop an Airborne Phased Array Radar (APAR) for deployment on the NSF/NCAR C-130 aircraft.
Support NOAA's long-term Mission Goal of developing America's capabilities as a "weather ready" nation by improving forecasts, warnings, and decision support for high-impact weather events.
NCAR
N/A
National Center for Atmospheric Research
NA18OAR4590431
Relevant to All Hazards
Colorado
October 2018 - November 2021
Aircraft Reconnaissance, Radar, Decision Support
2019 2020 airborne phased array radar apar, to develop an airborne phased array radar apar for deployment on the nsf ncar c 130 aircraft in order to improve forecasts warnings and decision support for high impact weather events, develop an airborne phased array radar apar for deployment on the nsf ncar c 130 aircraft
National Center for Atmospheric Research
To develop an Airborne Phased Array Radar (APAR) for deployment on the NSF/NCAR C-130 aircraft in order to improve forecasts, warnings, and decision support for high-impact weather events.
3
Evaluation and Improvement of Snowmelt Processes in the National Water Model During Extreme Atmospheric River Events in the Western U.S. Events
Ralph
Observations
check_circle
2019
Level 3
To improve water prediction, researchers intend to study extreme atmospheric river events, where atmospheric rivers can be visualized as the rivers in the sky that transport most of the water vapor. Researchers will assess the underlying meteorological and hydrological processes and landscape variables that are linked to rapid snowmelt in the current model and will quantify the National Water Model's simulation performance for the changes in snowpack.
Martin Ralph
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
To improve water prediction, researchers intend to study extreme atmospheric river events, where atmospheric rivers can be visualized as the rivers in the sky that transport most of the water vapor. This requires in situ and remote-sensing of the snowpack and soil moisture to understand how warm atmospheric rivers lead to rapid snowmelt and therefore high river flows. Researchers will assess the underlying meteorological and hydrological processes and landscape variables that are linked to rapid snowmelt in the current model and will quantify the National Water Model's simulation performance for the changes in snowpack. This simulation, and attendant opportunities for improvement, will be informed by machine learning techniques. Finally, and in support of the National Water Model, researchers also will develop a situational awareness tool for the Sierra Nevada and Cascades that could identify potential extreme snowmelt conditions for hydrologic forecasters.
evaluation and improvement of snowmelt processes in the national water model during extreme atmospheric river events in the western u s events, to improve water prediction researchers intend to study extreme atmospheric river events where atmospheric rivers can be visualized as the rivers in the sky that transport most of the water vapor researchers will assess the underlying meteorological and hydrological processes and landscape variables that are linked to rapid snowmelt in the current model and will quantify the national water model's simulation performance for the changes in snowpack, to improve water prediction researchers intend to study extreme atmospheric river events where atmospheric rivers can be visualized as the rivers in the sky that transport most of the water vapor this requires in situ and remote sensing of the snowpack and soil moisture to understand how warm atmospheric rivers lead to rapid snowmelt and therefore high river flows researchers will assess the underlying meteorological and hydrological processes and landscape variables that are linked to rapid snowmelt in the current model and will quantify the national water model's simulation performance for the changes in snowpack this simulation and attendant opportunities for improvement will be informed by machine learning techniques finally and in support of the national water model researchers also will develop a situational awareness tool for the sierra nevada and cascades that could identify potential extreme snowmelt conditions for hydrologic forecasters
National Water Center (NWC)
Scripps Institute, UCSD
To improve water prediction, researchers intend to study extreme atmospheric river events, where atmospheric rivers can be visualized as the rivers in the sky that transport most of the water vapor. Researchers will assess the
1
Modernizing Observation Operator and Error Assessment for Assimilating In-situ and Remotely Sensed Snow/Soil Moisture Measurements into NWM
Pan
Observations
check_circle
2019
Level 3
To facilitate the data assimilation of snowpack and soil moisture datasets by dynamically resolving the physical processes from in situ probes (meters) to the NWM (1 kilometer) and satellite (tens of kilometers) scales. The project will develop the currently missing but critical modeling tools to physically and dynamically resolve the scaling-related systematic gaps between the NWM output and in-situ and satellite-based snow/soil moisture observations to enable the most effective data assimilation for the NWM.
Ming Pan, Nathaniel Chaney, Clara Draper, Craig Ferguson
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, In-situ Observation Technologies and Sensors, Satellite & Remote Sensing Technologies
SPSM
National Water Model (NWM)
For the National Water Model (NWM), researchers intend to facilitate data assimilation of snowpack and soil moisture datasets by dynamically resolving the physical processes from in situ probes (meters) to the NWM (1 kilometer) and satellite (tens of kilometers) scales. Physical processes include horizontal moisture transfer along hill slopes, water table interactions, local variabilities due to elevation, temperature, land cover, vegetation, soil, slope/aspect, among others. The resulting operator will be implemented with the Joint Effort for Data assimilation Integration (JEDI) framework for operational data assimilation at NOAA's Office of Water Prediction and the National Water Center.
Improved water resource assessment.
NWC
N/A
Princeton University - CIMES;
Duke University;
University of Colorado - CIRES;
SUNY Albany
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, In-situ Observation Technologies and Sensors, Satellite & Remote Sensing Technologies
modernizing observation operator and error assessment for assimilating in situ and remotely sensed snow soil moisture measurements into nwm, to facilitate the data assimilation of snowpack and soil moisture datasets by dynamically resolving the physical processes from in situ probes meters to the nwm 1 kilometer and satellite tens of kilometers scales the project will develop the currently missing but critical modeling tools to physically and dynamically resolve the scaling related systematic gaps between the nwm output and in situ and satellite based snow soil moisture observations to enable the most effective data assimilation for the nwm, for the national water model nwm researchers intend to facilitate data assimilation of snowpack and soil moisture datasets by dynamically resolving the physical processes from in situ probes meters to the nwm 1 kilometer and satellite tens of kilometers scales physical processes include horizontal moisture transfer along hill slopes water table interactions local variabilities due to elevation temperature land cover vegetation soil slope aspect among others the resulting operator will be implemented with the joint effort for data assimilation integration jedi framework for operational data assimilation at noaa's office of water prediction and the national water center
National Water Center (NWC)
Princeton University - CIMES;, Duke University;, University of Colorado - CIRES;, SUNY Albany
To facilitate the data assimilation of snowpack and soil moisture datasets by dynamically resolving the physical processes from in situ probes (meters) to the NWM (1 kilometer) and satellite (tens of kilometers) scales. The project
15
Improving Snow and Streamflow Simulation in the National Water Model by Leveraging Advanced Mesonet Observations from the Northeastern United States
Minder
Observations
check_circle
2019
Level 3
To improve snow state initialization and prediction by leveraging observations from the newly installed New York State Mesonet (NYSM) and in collaboration with NOAA's Northeast River Forecast Center and the National Operational Hydrologic Remote Sensing Center.
Justin Minder, David Gochis, Theodore Letcher
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Surface Observing Networks
SPSM
National Water Model (NWM)
The current [at the time of proposal] operational version of the National Water Model (NWM) initializes snow states of the forecast hourly using a simple deterministic analysis. This leads to substantial errors in snowpack characterization as biases in forcing and snowpack physics accumulate within the modeled snow state throughout the winter season; the extent of this error is largely unknown and varies according to region. Leveraging observations from the newly installed New York State Mesonet (NYSM) and in collaboration with NOAA's Northeast River Forecast Center and the National Operational Hydrologic Remote Sensing Center, researchers will improve snow state initialization and prediction.
Improved water resource assessment.
NWC
N/A
SUNY Albany;
National Center for Atmospheric Research;
USACE CRREL
NA19OAR4590203
NA19OAR4590204
OWAQOBS001 (IAA)
Winter Weather
Colorado, New Hampshire, New York
September 2019 - September 2023
Atmospheric Physics, Data Assimilation and Ensembles, Surface Observing Networks
improving snow and streamflow simulation in the national water model by leveraging advanced mesonet observations from the northeastern united states, to improve snow state initialization and prediction by leveraging observations from the newly installed new york state mesonet nysm and in collaboration with noaa's northeast river forecast center and the national operational hydrologic remote sensing center, the current [at the time of proposal] operational version of the national water model nwm initializes snow states of the forecast hourly using a simple deterministic analysis this leads to substantial errors in snowpack characterization as biases in forcing and snowpack physics accumulate within the modeled snow state throughout the winter season the extent of this error is largely unknown and varies according to region leveraging observations from the newly installed new york state mesonet nysm and in collaboration with noaa's northeast river forecast center and the national operational hydrologic remote sensing center researchers will improve snow state initialization and prediction
National Water Center (NWC)
SUNY Albany;, National Center for Atmospheric Research;, USACE CRREL
To improve snow state initialization and prediction by leveraging observations from the newly installed New York State Mesonet (NYSM) and in collaboration with NOAA's Northeast River Forecast Center and the National Operational Hydrologic Remote Sensing
4
Airborne Snow Depth Retrieval for Improved Hydrological Modeling
Arnold
Observations
check_circle
2019
Level 3
To improve hydrologic modeling and predictions by measuring snow depth in a variety of terrains and vegetative states and use WRF-Hydro to develop and demonstrate an assimilation approach that uses these snow depth data.
Emily Arnold
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance
SPSM
National Water Model (NWM), Weather Research and Forecasting Model (WRF), Weather Research and Forecasting Model - Hydro (WRF-Hydro)
Current research depends on snow models within land-surface models for seasonal prediction. In this research, though, researchers will measure snow depth in a variety of terrains and vegetative states and will use WRF-Hydro to develop and demonstrate an assimilation approach that uses these snow depth data to improve hydrologic modeling and predictions.
Improved water resource assessment.
NOHRSC
N/A
University of Kansas
NA19OAR4590206
Winter Weather
Kansas
September 2019 - August 2022
Aircraft Reconnaissance
airborne snow depth retrieval for improved hydrological modeling, to improve hydrologic modeling and predictions by measuring snow depth in a variety of terrains and vegetative states and use wrf hydro to develop and demonstrate an assimilation approach that uses these snow depth data, current research depends on snow models within land surface models for seasonal prediction in this research though researchers will measure snow depth in a variety of terrains and vegetative states and will use wrf hydro to develop and demonstrate an assimilation approach that uses these snow depth data to improve hydrologic modeling and predictions
University of Kansas
To improve hydrologic modeling and predictions by measuring snow depth in a variety of terrains and vegetative states and use WRF-Hydro to develop and demonstrate an assimilation approach that uses these snow depth data.
1
Study of Infrasound Propagation from Tornadic Storms in Dynamic Atmospheres over Hilly Terrain
Waxler
Observations
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2019
Level 3
To advance tornado warning methods, development of forecasting tools, and tornado observation methods by better understanding infrasound propagation in the frequency band of 1 to 10 Hz in hilly areas.
Roger Waxler
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Radar
IS
This research involves modeling and evaluating meteorological and terrain effects on signal detection from tornadoes using existing propagation model packages. The researchers intend to better understand infrasound propagation in the frequency band of 1 to 10 Hz in hilly areas.
These outcomes provide benefit to society, weather forecasting, and the science of tornado generation and motion through possible advancement in tornado warning methods, forecasting tool development and tornado observation methods, respectively.
Research
N/A
University of Mississippi
NA19OAR4590338
Severe Weather
Mississippi
September 2019 - August 2022
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Radar
study of infrasound propagation from tornadic storms in dynamic atmospheres over hilly terrain, to advance tornado warning methods development of forecasting tools and tornado observation methods by better understanding infrasound propagation in the frequency band of 1 to 10 hz in hilly areas, this research involves modeling and evaluating meteorological and terrain effects on signal detection from tornadoes using existing propagation model packages the researchers intend to better understand infrasound propagation in the frequency band of 1 to 10 hz in hilly areas
University of Mississippi
To advance tornado warning methods, development of forecasting tools, and tornado observation methods by better understanding infrasound propagation in the frequency band of 1 to 10 Hz in hilly areas.
0
Modeling of Infrasound Generation from Tornadic Storms
Waxler
Observations
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2019
Level 3
To identify non-tornadic storm elements that produce infrasound and falsely resemble tornadoes by using tornado simulations to produce measurements with sufficient vertical resolution. This will identify the location of infrasound within the funnel and measurements with sufficient resolution to model acoustic radiation in the 1 to 10 Hz band.
Roger Waxler
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Radar
IS
This work will use tornado simulation to produce measurements with sufficient vertical resolution to identify the location of infrasound within the funnel and measurements with sufficient resolution to model acoustic radiation in the 1 to 10 Hz band. Researchers also intend to identify non-tornadic storm elements that produce infrasound and that falsely resemble tornadoes.
Infrasound monitoring of tornadoes could lead to decreased tornadic false alarms and to a method for remotely observing tornadoes.
Research
N/A
University of Mississippi
NA19OAR4590339
Severe Weather
Mississippi
September 2019 - August 2022
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Radar
modeling of infrasound generation from tornadic storms, to identify non tornadic storm elements that produce infrasound and falsely resemble tornadoes by using tornado simulations to produce measurements with sufficient vertical resolution this will identify the location of infrasound within the funnel and measurements with sufficient resolution to model acoustic radiation in the 1 to 10 hz band, this work will use tornado simulation to produce measurements with sufficient vertical resolution to identify the location of infrasound within the funnel and measurements with sufficient resolution to model acoustic radiation in the 1 to 10 hz band researchers also intend to identify non tornadic storm elements that produce infrasound and that falsely resemble tornadoes
University of Mississippi
To identify non-tornadic storm elements that produce infrasound and falsely resemble tornadoes by using tornado simulations to produce measurements with sufficient vertical resolution. This will identify the location of infrasound within the funnel and measurements
2
Infrasound Observations and Demonstration of Real-Time Tools
Elbing
Observations
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2019
Level 3
To demonstrate how infrasound data can enhance tornado threat predication via correlations between the radar and infrasound metrics and demonstrate improved algorithms for real-time processing and analysis to operational meteorologists.
Brian Elbing
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Radar
IS
Researchers intend to illustrate the potential value of infrasound technology by co-locating an infrasound array with a Weather Surveillance Radar-1988 Doppler (WSR-88D) site, reducing uncertainty from sound propagation, correlating flow-field metrics with infrasound measurements, and demonstrating processing algorithms to enable real-time analysis.
Infrasound monitoring of tornadoes could lead to decreased tornadic false alarms and to a method for remotely observing tornadoes.
Research
N/A
Oklahoma State University
NA19OAR4590340
Severe Weather
Oklahoma
September 2019 - August 2022
In-situ Observation Technologies and Sensors, Radar
infrasound observations and demonstration of real time tools, to demonstrate how infrasound data can enhance tornado threat predication via correlations between the radar and infrasound metrics and demonstrate improved algorithms for real time processing and analysis to operational meteorologists, researchers intend to illustrate the potential value of infrasound technology by co locating an infrasound array with a weather surveillance radar-1988 doppler wsr 88d site reducing uncertainty from sound propagation correlating flow field metrics with infrasound measurements and demonstrating processing algorithms to enable real time analysis
Oklahoma State University
To demonstrate how infrasound data can enhance tornado threat predication via correlations between the radar and infrasound metrics and demonstrate improved algorithms for real-time processing and analysis to operational meteorologists.
9
Understanding the infrasound characteristics of nontornadic and tornadic supercells in VORTEX2 and VORTEX-SE environments using high-resolution ensemble simulations
Parker
Observations
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2019
Level 3
To provide NOAA forecasters and administrators with a clearer idea of whether improved discrimination of tornadic vs. nontornadic storms can be reasonably expected if an infrasound observation network were deployed to supplement the existing Doppler radar network.
Matthew Parker
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, In-situ Observation Technologies and Sensors, Radar
IS
Researchers intend to identify differences in infrasound signals between tornadic and nontornadic Great Plains supercells using VORTEX 2 simulations. They will also compare infrasound characteristics in simulations of differing convective modes using VORTEX-SE data from the Southeastern United States.
NOAA's National Weather Service Forecasters, administrators, and the general public would benefit from this research, which is intended to increase awareness of, preparedness for, and safety from tornadoes while reducing false alarms.
Research
N/A
North Carolina State University
NA19OAR4590341
Severe Weather
North Carolina
September 2019 - August 2022
Data Assimilation and Ensembles, Dynamics and Nesting, In-situ Observation Technologies and Sensors, Radar
understanding the infrasound characteristics of nontornadic and tornadic supercells in vortex2 and vortex se environments using high resolution ensemble simulations, to provide noaa forecasters and administrators with a clearer idea of whether improved discrimination of tornadic vs nontornadic storms can be reasonably expected if an infrasound observation network were deployed to supplement the existing doppler radar network, researchers intend to identify differences in infrasound signals between tornadic and nontornadic great plains supercells using vortex 2 simulations they will also compare infrasound characteristics in simulations of differing convective modes using vortex se data from the southeastern united states
North Carolina State University
To provide NOAA forecasters and administrators with a clearer idea of whether improved discrimination of tornadic vs. nontornadic storms can be reasonably expected if an infrasound observation network were deployed to supplement the existing Doppler
5
2020 Airborne Phased Array Radar Preliminary Design and Risk Mitigation
Grubisic
Observations
check_circle
2019
Level 3
To improve weather predictions by continuing Airborne Phased Array Radar (APAR) development in order to gather more detailed weather observations over time.
Vanda Grubisic
Complete
Tue Oct 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Nov 30 2021 03:00:00 GMT-0500 (Eastern Standard Time)
Aircraft Reconnaissance, Radar
APAR
The proposed research will continue to develop airborne phased array radar (APAR) to gather more detailed observations over time.
Enhanced observations will improve weather prediction.
NCAR
N/A
National Center for Atmospheric Research
NA19OAR4590245
Severe Weather
Colorado
October 2019 - November 2021
Aircraft Reconnaissance, Radar
2020 airborne phased array radar preliminary design and risk mitigation, to improve weather predictions by continuing airborne phased array radar apar development in order to gather more detailed weather observations over time, the proposed research will continue to develop airborne phased array radar apar to gather more detailed observations over time
National Center for Atmospheric Research
To improve weather predictions by continuing Airborne Phased Array Radar (APAR) development in order to gather more detailed weather observations over time.
1
Real-time Observations of the Three-Dimensional Hurricane Boundary Layer Winds and Ocean Surface Vector Winds with an Imaging Airborne Profiler
Jelenak
Observations
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Level 3
To demonstrate that the gaps in HBL observations can and will be filled using the unique Imaging Wind and Rain Airborne Profiler (IWRAP) sampling protocol in parallel with the retrieval processor, enabling improvements in operational forecasting.
Zorana Jelenak, Paul Chang, Stephen Guimond
Complete
Aircraft Reconnaissance
Obs
Advanced Weather Interactive Processing System (AWIPS)
This project will demonstrate that the gaps in HBL observations can and will be filled using the unique Imaging Wind and Rain Airborne Profiler (IWRAP) sampling protocol in parallel with the retrieval processor enabling improvements in operational forecasting.
Protection of life and property from landfalling hurricanes.
HRD, NHC, EMC
N/A
UCAR;
University of Maryland Baltimore County;
NESDIS
NA19OAR4590328
NA19OAR4590329
Tropical Cyclones
Colorado, Maryland
Aircraft Reconnaissance
real time observations of the three dimensional hurricane boundary layer winds and ocean surface vector winds with an imaging airborne profiler, to demonstrate that the gaps in hbl observations can and will be filled using the unique imaging wind and rain airborne profiler iwrap sampling protocol in parallel with the retrieval processor enabling improvements in operational forecasting, this project will demonstrate that the gaps in hbl observations can and will be filled using the unique imaging wind and rain airborne profiler iwrap sampling protocol in parallel with the retrieval processor enabling improvements in operational forecasting
UCAR;, University of Maryland Baltimore County;, NESDIS
To demonstrate that the gaps in HBL observations can and will be filled using the unique Imaging Wind and Rain Airborne Profiler (IWRAP) sampling protocol in parallel with the retrieval processor, enabling improvements in operational
3
Enhancing Observations of Melting Level to Support Forecasts of Rain-Snow Partitioning in the Sierra Nevada
Ralph
Observations
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2019
Level 3
To support operational forecasts and improve observations of the lower atmosphere over the Sierra Nevada to better understand and predict high impact hydrometeorological events by deploying instruments in the mountains and host the data in near-real-time.
Fred Ralph, Andrew Martin
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Surface Observing Networks
Obs
Errors in forecasting the melting level, when rain turns to snow, in the Sierra Nevada Mountains have a large impact on reservoir flood control decisions. Current observations through the Hydrometeorological Testbed network, located in the Sierra Nevada foothills, provided melting level height at lower elevations but do not directly measure conditions at high elevation. This project will deploy instruments in the mountains and host these data in near-real-time to support operational forecasts.
Improved water resource assessment.
OPPSD
N/A
Scripps Institute, UCSD;
Portland State University
NA19OAR4590330
NA19OAR4590331
Water Extremes
California, Oregon
September 2019 - August 2022
In-situ Observation Technologies and Sensors, Surface Observing Networks
enhancing observations of melting level to support forecasts of rain snow partitioning in the sierra nevada, to support operational forecasts and improve observations of the lower atmosphere over the sierra nevada to better understand and predict high impact hydrometeorological events by deploying instruments in the mountains and host the data in near real time, errors in forecasting the melting level when rain turns to snow in the sierra nevada mountains have a large impact on reservoir flood control decisions current observations through the hydrometeorological testbed network located in the sierra nevada foothills provided melting level height at lower elevations but do not directly measure conditions at high elevation this project will deploy instruments in the mountains and host these data in near real time to support operational forecasts
Scripps Institute, UCSD;, Portland State University
To support operational forecasts and improve observations of the lower atmosphere over the Sierra Nevada to better understand and predict high impact hydrometeorological events by deploying instruments in the mountains and host the data in
0
Toward Obtaining Daily Vertical Profiles of Boundary Layer Temperature and Moisture Fields using Small Unmanned Aircraft Systems
Lee
Observations
check_circle
2019
Level 3
To validate and improve operational models such as the High-Resolution Rapid Refresh (HRRR) by deploying small unmanned aircraft systems (sUAS) to gather information about temperature, moisture, pressure, and wind up to one kilometer above ground level.
Temple Lee, Bruce Baker
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Dynamics and Nesting, Uncrewed Systems & Vehicles
Researchers will use small unmanned aircraft systems (sUAS) to gather information about temperature, moisture, pressure, and wind up to one kilometer above ground level and will provide this information consistently to NOAA's National Weather Service's (NWS) Weather Forecast Office (WFO) in Morristown, Tennessee. The data from the sUAS will also be used to validate and improve operational models such as the High-Resolution Rapid Refresh (HRRR).
Improved characterization of the lower atmosphere that can lead to improved forecasts of hazardous convective weather, such as thunderstorms.
AOC
N/A
University of Oklahoma/Cooperative Institute for Mesoscale Meteorological Studies;
ARL
NA19OAR4590332
Severe Weather
Oklahoma
September 2019 - August 2022
Aircraft Reconnaissance, Dynamics and Nesting, Uncrewed Systems & Vehicles
toward obtaining daily vertical profiles of boundary layer temperature and moisture fields using small unmanned aircraft systems, to validate and improve operational models such as the high resolution rapid refresh hrrr by deploying small unmanned aircraft systems suas to gather information about temperature moisture pressure and wind up to one kilometer above ground level, researchers will use small unmanned aircraft systems suas to gather information about temperature moisture pressure and wind up to one kilometer above ground level and will provide this information consistently to noaa's national weather service's nws weather forecast office wfo in morristown tennessee the data from the suas will also be used to validate and improve operational models such as the high resolution rapid refresh hrrr
University of Oklahoma/Cooperative Institute for Mesoscale Meteorological Studies;, ARL
To validate and improve operational models such as the High-Resolution Rapid Refresh (HRRR) by deploying small unmanned aircraft systems (sUAS) to gather information about temperature, moisture, pressure, and wind up to one kilometer above ground
0
Operational Implementation of Near-Real Time High-Temporal Resolution Thermodynamic Retrievals
Wagner
Observations
check_circle
2019
Level 3
To explore the role that real-time high-temporal resolution profiling can play in operational meteorology by providing forecasters with the tools to observe and interpret these observations. This will be done by integrating real-time temperature and water vapor profile retrievals from AERIs into WFO's operational workflows, as well as reprocessing a dataset of observations from a multiyear AERI network deployment to assess the characteristics of boundary layer evolution as observed by AERI ahead of significant weather events.
Timothy Wagner
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting
Obs
Advanced Weather Interactive Processing System (AWIPS)
Atmospheric stability can be monitored by measuring thermodynamic profiles in the atmosphere. Atmospheric Emitted Radiance Interferometer (AERI) are ground based instruments that can produce near real-time profiles every 10 minutes. This data can be helpful for diagnosing atmospheric stability before severe weather events. A software plug-in will be developed for the Advanced Weather Interactive Processing System (AWIPS) software that is used by NOAA's National Weather Service (NWS) forecaster stations.
Improved characterization of the lower atmosphere that can lead to improved forecasts of hazardous convective weather, such as thunderstorms.
ESRL
N/A
University of Wisconsin-CIMSS
NA19OAR4590319
Extreme Temperatures
Wisconsin
September 2019 - August 2023
Atmospheric Physics, Dynamics and Nesting
operational implementation of near real time high temporal resolution thermodynamic retrievals, to explore the role that real time high temporal resolution profiling can play in operational meteorology by providing forecasters with the tools to observe and interpret these observations this will be done by integrating real time temperature and water vapor profile retrievals from aeris into wfo's operational workflows as well as reprocessing a dataset of observations from a multiyear aeri network deployment to assess the characteristics of boundary layer evolution as observed by aeri ahead of significant weather events, atmospheric stability can be monitored by measuring thermodynamic profiles in the atmosphere atmospheric emitted radiance interferometer aeri are ground based instruments that can produce near real time profiles every 10 minutes this data can be helpful for diagnosing atmospheric stability before severe weather events a software plug in will be developed for the advanced weather interactive processing system awips software that is used by noaa's national weather service nws forecaster stations
University of Wisconsin-CIMSS
To explore the role that real-time high-temporal resolution profiling can play in operational meteorology by providing forecasters with the tools to observe and interpret these observations. This will be done by integrating real-time temperature and
2
Lower-Tropospheric Thermodynamic and Wind Profiling Impact Study
Weckwerth
Observations
check_circle
2019
Level 3
To increase high-impact weather forecasting skill by improving lower-tropospheric profiles of wind, temperature, and water vapor. A field study using autonomous remote sensing instruments for these variables will be conducted.
Tammy Weckwerth
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Surface Observing Networks
Obs
Improving lower-tropospheric profiles of wind, temperature, and water vapor will be critical to increasing high-impact weather forecasting skill. To support this project, a field study at five sites will combine autonomous remote sensing instruments for these variables. This project will use the data from the three-month field campaign to evaluate the value of this combined approach to supplement the National Mesonet Program.
Improved protection of life and property from hazardous mesoscale weather phenomena.
NMP
N/A
National Center for Atmospheric Research
NA19OAR4590320
Severe Weather
Colorado
September 2019 - August 2023
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Surface Observing Networks
lower tropospheric thermodynamic and wind profiling impact study, to increase high impact weather forecasting skill by improving lower tropospheric profiles of wind temperature and water vapor a field study using autonomous remote sensing instruments for these variables will be conducted, improving lower tropospheric profiles of wind temperature and water vapor will be critical to increasing high impact weather forecasting skill to support this project a field study at five sites will combine autonomous remote sensing instruments for these variables this project will use the data from the three month field campaign to evaluate the value of this combined approach to supplement the national mesonet program
National Center for Atmospheric Research
To increase high-impact weather forecasting skill by improving lower-tropospheric profiles of wind, temperature, and water vapor. A field study using autonomous remote sensing instruments for these variables will be conducted.
4
Diode-Laser-Based Remote Sensing for Thermodynamic Profiling of the Lower Troposphere
Repasky
Observations
check_circle
2019
Level 3
To enhance Numerical Weather Prediction (NWP) skill by integrating new measurement capabilities for aerosols and temperature profiling into the diode-laser-based (DLB) lidar instrument architecture, allowing for ground based real-time parameters for data assimilation.
Kevin Repasky, Scott Spuler
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Numerical Weather Prediction (NWP), Satellite & Remote Sensing Technologies
Obs
Improving data assimilation is a key method for enhancing Numerical Weather Prediction (NWP) skill. This study will integrate new measurement capabilities for aerosols and temperature profiling into the diode-laser-based (DLB) lidar instrument architecture, allowing for ground based real-time parameters for data assimilation. These data will supply the needed information to initialize the NWP models.
Improved protection of life and property from hazardous mesoscale weather phenomena.
NCAR
N/A
Montana State University;
National Center for Atmospheric Research
NA19OAR4590323
NA19OAR4590324
Severe Weather
Colorado, Montana
September 2019 - August 2022
Atmospheric Composition and Chemistry, Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Numerical Weather Prediction (NWP), Satellite & Remote Sensing Technologies
diode laser based remote sensing for thermodynamic profiling of the lower troposphere, to enhance numerical weather prediction nwp skill by integrating new measurement capabilities for aerosols and temperature profiling into the diode laser based dlb lidar instrument architecture allowing for ground based real time parameters for data assimilation, improving data assimilation is a key method for enhancing numerical weather prediction nwp skill this study will integrate new measurement capabilities for aerosols and temperature profiling into the diode laser based dlb lidar instrument architecture allowing for ground based real time parameters for data assimilation these data will supply the needed information to initialize the nwp models
Montana State University;, National Center for Atmospheric Research
To enhance Numerical Weather Prediction (NWP) skill by integrating new measurement capabilities for aerosols and temperature profiling into the diode-laser-based (DLB) lidar instrument architecture, allowing for ground based real-time parameters for data assimilation.
5
Developing New Capabilities and Research Applications for the National Water Model Over the Southeastern US
Dyer
Observations
check_circle
2019
Level 3
To install operational and developmental versions of the NWM on MSU supercomputer facilities, and develop new capabilities in the model and apply the model to relevant water-related research projects over selected sub-domains within the Southeastern US.
Jamie Dyer, Andrew Mercer
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance
SPSM
National Water Model (NWM), Unified Forecast System (UFS)
The proposed work will maintain and fully develop the existing partial copy of the WWRP/WCRP Sub-seasonal to Seasonal Prediction Project (S2S) database and tools in the IRI Data Library (IRIDL) for the duration of Phase II of the S2S Prediction Project (2019-2023). This will include adding the S2S ocean data from the primary S2S archive at ECMWF, together with any extensions to it made at ECMWF such as new models. In so doing, the IRI Data Library (IRIDL) will serve as a third archiving centre for S2S data, along with ECMWF and CMA. Beyond an S2S database copy in the US, IRIDL will provide important new resources compared to those available through ECMWF and CMA, accelerating S2S research and development of S2S applications.
IRIDL will provide important new resources compared to those available through ECMWF and CMA, accelerating S2S research and development of S2S applications.
CPC
N/A
Mississippi State University
NA19OAR4590411
Water Extremes
Mississippi
September 2019 - August 2024
Model & Forecast Guidance
developing new capabilities and research applications for the national water model over the southeastern us, to install operational and developmental versions of the nwm on msu supercomputer facilities and develop new capabilities in the model and apply the model to relevant water related research projects over selected sub domains within the southeastern us, the proposed work will maintain and fully develop the existing partial copy of the wwrp wcrp sub seasonal to seasonal prediction project s2s database and tools in the iri data library iridl for the duration of phase ii of the s2s prediction project 2019-2023 this will include adding the s2s ocean data from the primary s2s archive at ecmwf together with any extensions to it made at ecmwf such as new models in so doing the iri data library iridl will serve as a third archiving centre for s2s data along with ecmwf and cma beyond an s2s database copy in the us iridl will provide important new resources compared to those available through ecmwf and cma accelerating s2s research and development of s2s applications
Climate Prediction Center (CPC)
Mississippi State University
To install operational and developmental versions of the NWM on MSU supercomputer facilities, and develop new capabilities in the model and apply the model to relevant water-related research projects over selected sub-domains within the Southeastern
15
Developing new soil moisture technologies and applications for improving assessments and forecasts of water issues in the Southeast such as flash droughts, floods, and ecosystem health - Alabama phase
Christy
Observations
check_circle
2020
Level 3
To enhance materially the soil moisture network in the Southeast and to improve applications of these data through a tri-state research effort, and to further improves forecasting extreme weather events such as flash floods and flash droughts.
John Christy, Lee Ellenburg
Complete
Thu Oct 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Satellite & Remote Sensing Technologies, Surface Observing Networks, Decision Support
SPSM
National Integrated Drought Information System (NIDIS)
This project is the Alabama contribution of a three-state proposal seeking funding
for a coordinated effort among the states of Alabama, Georgia and Florida to
materially enhance the soil moisture network in the Southeast and to improve
applications of these data by (1) expanding the network throughout the three states,
(2) establishing test beds for assessing new designs for low-cost sensors, (3)
updating and adding to agricultural models to improve the detection of stress
indices such as the wilting point - the time at which SE agricultural losses began to
accrue, (4) calibrating and testing current and new remote-sensing techniques so as
to provide complete three-state coverage of drought stress and flood indices, (5)
developing tools to provide conformity and utility of soil moisture data and their
derived products, and (6) provide a publicly-accessible platform for product
distribution.
Specific Outcomes - Expanding the soil moisture sensor network in Alabama will have a direct
impact on observations this vital metric which will be used to improve existing models and
remote sensing products. The products will better-define the magnitude of soil-moisture and
vegetative stresses across the SE for application in (a) agricultural operations, (b) determination
of real-time agricultural drought stress, (c) general scientific research involving the
land/atmosphere interface and (d) defining levels for federal assistance payments to those who
suffer economic losses. Our products will be available to support NIDIS and other NOAA
drought activities.
Broader Outcomes - Creating a database of soil moisture sensors that can be included in the
National Soil Moisture Network will contribute to collaborative research and applications across
the country in an immediate sense as the fundamental measurements here will be the same as
those reported in the nation-wide USCRN and USDA SCAN stations. Increasing the accuracy of
Remote sensing, model and gridded products through additional in situ soil moisture monitoring
will have a broader benefit of contributing to a better understanding of the hydrological cycle.
Societal Outcomes - Improving forecasts and drought designations can have large societal
impacts. Communities can build in resilience and plan for mitigation of extreme events. Public
water supplies could define and implement drought plans based on accurate forecasts. Farmers
and producers could determine what level of water storage and irrigation infrastructure is needed
and affordable to offset the damage from a given that flash droughts will occur. Much of the
government's response to a drought disaster is dependent on defining the extent and duration of
the drought, thus a more accurate and objective depiction of crop stress is a fundamental
advancement for the legislated activity that assists farmers
OWP
N/A
University of Alabama in Huntsville
NA20OAR4590495
Water Extremes
Alabama
October 2020 - September 2023
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Satellite & Remote Sensing Technologies, Surface Observing Networks, Decision Support
developing new soil moisture technologies and applications for improving assessments and forecasts of water issues in the southeast such as flash droughts floods and ecosystem health alabama phase, to enhance materially the soil moisture network in the southeast and to improve applications of these data through a tri state research effort and to further improves forecasting extreme weather events such as flash floods and flash droughts, this project is the alabama contribution of a three state proposal seeking funding for a coordinated effort among the states of alabama georgia and florida to materially enhance the soil moisture network in the southeast and to improve applications of these data by 1 expanding the network throughout the three states 2 establishing test beds for assessing new designs for low cost sensors 3 updating and adding to agricultural models to improve the detection of stress indices such as the wilting point - the time at which se agricultural losses began to accrue 4 calibrating and testing current and new remote sensing techniques so as to provide complete three state coverage of drought stress and flood indices 5 developing tools to provide conformity and utility of soil moisture data and their derived products and 6 provide a publicly accessible platform for product distribution
NWS Office of Water Prediction (OWP)
University of Alabama in Huntsville
To enhance materially the soil moisture network in the Southeast and to improve applications of these data through a tri-state research effort, and to further improves forecasting extreme weather events such as flash floods and
0
Developing new soil moisture technologies and applications for improving assessments and forecasts of water issues in the Southeast such as flash droughts, floods and ecosystem health - Georgia Phase
Vellidis
Observations
check_circle
2020
Level 3
To enhance materially the soil moisture network in the Southeast and to improve applications of these data through a tri-state research effort, and to further improves forecasting extreme weather events such as flash floods and flash droughts.
George Vellidis
Complete
Thu Oct 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Satellite & Remote Sensing Technologies, Surface Observing Networks, Decision Support
SPSM
National Integrated Drought Information System (NIDIS)
This project is a coordinated effort between Alabama, Florida, and Georgia whose goal is to enhance materially the soil moisture network in the Southeast and to improve applications of these data. To achieve this goal, the project will address the following objectives. The states that will be primarily involved in each objective are listed in parentheses. However, all states will provide supporting data or expertise to complete the project's objectives.
1. Assessing the viability of low-cost soil moisture sensors (AL, GA).
2. Expanding the soil moisture network by installing viable versions of low-cost sensors in the existing networks (AL, FL).
3. Remote sensing-driven root-zone soil moisture (AL, FL, GA).
4. Improving the Cropping Model System (AL, GA).
5. Will the data and products be accessible in useful formats? (AL, GA).
University of Georgia (UGA) will use its Extension system to transfer information to stakeholders. Specifically, UGA will inform farmers and ranchers about the project's deliverables and train Extension County Agents and USDA NRCS staff to use the project's deliverables to assist their clients.
OWP
N/A
University of Georgia
NA20OAR4590498
Water Extremes
Georgia
October 2020 - September 2023
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Satellite & Remote Sensing Technologies, Surface Observing Networks, Decision Support
developing new soil moisture technologies and applications for improving assessments and forecasts of water issues in the southeast such as flash droughts floods and ecosystem health georgia phase, to enhance materially the soil moisture network in the southeast and to improve applications of these data through a tri state research effort and to further improves forecasting extreme weather events such as flash floods and flash droughts, this project is a coordinated effort between alabama florida and georgia whose goal is to enhance materially the soil moisture network in the southeast and to improve applications of these data to achieve this goal the project will address the following objectives the states that will be primarily involved in each objective are listed in parentheses however all states will provide supporting data or expertise to complete the project's objectives 1 assessing the viability of low cost soil moisture sensors al ga 2 expanding the soil moisture network by installing viable versions of low cost sensors in the existing networks al fl 3 remote sensing driven root zone soil moisture al fl ga 4 improving the cropping model system al ga 5 will the data and products be accessible in useful formats? al ga
NWS Office of Water Prediction (OWP)
University of Georgia
To enhance materially the soil moisture network in the Southeast and to improve applications of these data through a tri-state research effort, and to further improves forecasting extreme weather events such as flash floods and
0
Developing new soil moisture technologies and applications for improving assessments and forecasts of water issues in the Southeast such as flash droughts, floods and ecosystem health - Florida Phase
Lusher
Observations
check_circle
2020
Level 3
To enhance materially the soil moisture network in the Southeast and to improve applications of these data through a tri-state research effort, and to further improves forecasting extreme weather events such as flash floods and flash droughts.
William Lusher
Complete
Thu Oct 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Satellite & Remote Sensing Technologies, Surface Observing Networks, Decision Support
SPSM
National Integrated Drought Information System (NIDIS)
The overarching goals of this project are to 1) expand the soil moisture monitoring
network throughout the three states, (2) establish test beds for assessing new designs
for low-cost sensors, (3) improve existing agricultural models to improve detection of
stress indices such as the wilting point - the time at which agricultural losses began
to accrue, (4) validate new remote-sensing techniques, e.g., satellite based
observations, toward improved evaluation of drought stress and flood indices, (5)
develop new data-driven decision making tools, and (6) provide a high resolution,
high quality dataset for public distribution.
Desired outcomes of this project include expansion of the soil moisture monitoring
network throughout Florida, Georgia, and Alabama, evaluation of low-cost sensors,
improving existing agricultural models, validating new remote-sensing techniques
toward improved evaluation of drought stress and flood indices, developing new data-
driven decision-making tools, and high resolution, high quality datasets for public distribution.
OWP
N/A
University of Florida
NA20OAR4590496
Water Extremes
Florida
October 2020 - September 2023
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Satellite & Remote Sensing Technologies, Surface Observing Networks, Decision Support
developing new soil moisture technologies and applications for improving assessments and forecasts of water issues in the southeast such as flash droughts floods and ecosystem health florida phase, to enhance materially the soil moisture network in the southeast and to improve applications of these data through a tri state research effort and to further improves forecasting extreme weather events such as flash floods and flash droughts, the overarching goals of this project are to 1 expand the soil moisture monitoring network throughout the three states 2 establish test beds for assessing new designs for low cost sensors 3 improve existing agricultural models to improve detection of stress indices such as the wilting point - the time at which agricultural losses began to accrue 4 validate new remote sensing techniques e g satellite based observations toward improved evaluation of drought stress and flood indices 5 develop new data driven decision making tools and 6 provide a high resolution high quality dataset for public distribution
NWS Office of Water Prediction (OWP)
University of Florida
To enhance materially the soil moisture network in the Southeast and to improve applications of these data through a tri-state research effort, and to further improves forecasting extreme weather events such as flash floods and
1
Experimental Framework for Testing the National Water Model: Operationalizing the use of Snow Remote Sensing in Alaska
Bennett
Observations
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2020
Level 3
To improve the National Water Model (NWM) with respect to its ability to reproduce snow conditions and its ability to predict streamflow in snow-dominated river basins.
Katrina Bennett, Emily Niebuhr, David Streubel
Complete
Sun Mar 01 2020 03:00:00 GMT-0500 (Eastern Standard Time)
Thu Feb 29 2024 03:00:00 GMT-0500 (Eastern Standard Time)
Data Assimilation and Ensembles
SPSM
Community Hydrologic Prediction System (CHPS), National Water Model (NWM)
Develop a near-real-time data stream of simple, first-order assimilations of remotely sensed snow cover extent and snow water equivalent data in Alaska and operationalize components of this work for use in NOAA's Community Hydrologic Prediction System (CHPS) framework. Transfer this knowledge to the National Water Model (NWM) system and run experiments using the NWM model for key study watersheds representing a range of hydro-climatic regimes in Alaska
Improve the operational capabilities of NOAA's National Water Center and contribute directly to its mission.
Alaska-Pacific River Forecast Center (APRFC)
ATB
NWC
N/A
New Mexico Consortium
NWS/ATB (Alaska Region)
NWS/APRFC (Alaska Region)
NA20OAR4590255
Winter Weather
New Mexico
March 2020 - February 2024
Data Assimilation and Ensembles
experimental framework for testing the national water model operationalizing the use of snow remote sensing in alaska, to improve the national water model nwm with respect to its ability to reproduce snow conditions and its ability to predict streamflow in snow dominated river basins, develop a near real time data stream of simple first order assimilations of remotely sensed snow cover extent and snow water equivalent data in alaska and operationalize components of this work for use in noaa's community hydrologic prediction system chps framework transfer this knowledge to the national water model nwm system and run experiments using the nwm model for key study watersheds representing a range of hydro climatic regimes in alaska
New Mexico Consortium, NWS/ATB (Alaska Region), NWS/APRFC (Alaska Region)
To improve the National Water Model (NWM) with respect to its ability to reproduce snow conditions and its ability to predict streamflow in snow-dominated river basins.
2
A New Global 4-km Multi-Decadal Snow Cover Extent/Snow Water Equivalent/Snow Depth Dataset from Blended In-situ and Satellite Observations
Romanov
Observations
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2021
Level 3
To develop a new snow cover monitoring system, as well as enhanced daily snow products. This work will contribute to reanalysis products and will benefit operational activities with the near real-time snow cover data.
Peter Romanov, Cezar Kongoli
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will develop a new snow cover monitoring system, as well as enhanced daily snow products. This work will contribute to reanalysis products and will benefit operational activities with the near real-time snow cover data.
Long-term temporally and spatially consistent estimates of daily snowpack properties - Snow Cover Area (SCA), Snow Water Equivalent (SWE) and Snow Depth (SD) - over seasonal snow covered land are needed for detailed assessments of and inputs to weather, climate and hydrological prediction models especially over poorly monitored areas. However, current applications have temporal and spatial inconsistencies and variations. This project aims to develop a new advanced snow cover monitoring system and a set of enhanced global daily snow products with improved accuracy, spatial resolution and spatial coverage.
TBD
Michael Barlage
City College of New York (NOAA-CESSRT), ESSIC (University of Maryland)
a new global 4 km multi decadal snow cover extent snow water equivalent snow depth dataset from blended in situ and satellite observations, to develop a new snow cover monitoring system as well as enhanced daily snow products this work will contribute to reanalysis products and will benefit operational activities with the near real time snow cover data, this project will develop a new snow cover monitoring system as well as enhanced daily snow products this work will contribute to reanalysis products and will benefit operational activities with the near real time snow cover data
City College of New York (NOAA-CESSRT), ESSIC (University of Maryland)
To develop a new snow cover monitoring system, as well as enhanced daily snow products. This work will contribute to reanalysis products and will benefit operational activities with the near real-time snow cover data.
1
Analysis and OSEs of UAS observations for improved high impact weather forecasts
Yussouf
Observations
check_circle
2021
Level 3
To collect vertical profiles before and during storm events by building and deploying uncrewed aerial system (UAS) platforms in the central United States. This work will illustrate the benefits that UAS observations have on forecasting and numerical weather predictions of supercells, mesoscale convective systems, and winter storms.
Nusrat Yussouf
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Numerical Weather Prediction (NWP), Observing System Simulation Experiments (OSSEs), Uncrewed Systems & Vehicles, "Social, Behavioral, and Economic Sciences (SBES)"
Obs
This project will build and deploy uncrewed aerial system (UAS) platforms in the central United States to collect vertical profiles before and during storm events. This work will illustrate the benefits of UAS observations have on forecasting and numerical weather prediction of supercells, mesoscale convective systems, and winter storms.
This study proposes to demonstrate the utility of the affordable Uncrewed Aerial System (UAS) observing technologies for the analysis and prediction of adverse weather phenomena resulting from springtime supercell storms, summertime mesoscale convective systems, and winter storms.
TBD
Todd Lindley
CIMMS (University of Oklahoma)
NA21OAR4590362
Severe Weather
Oklahoma
August 2021 - July 2025
Numerical Weather Prediction (NWP), Observing System Simulation Experiments (OSSEs), Uncrewed Systems & Vehicles, "Social Science (SBES)"
analysis and oses of uas observations for improved high impact weather forecasts, to collect vertical profiles before and during storm events by building and deploying uncrewed aerial system uas platforms in the central united states this work will illustrate the benefits that uas observations have on forecasting and numerical weather predictions of supercells mesoscale convective systems and winter storms, this project will build and deploy uncrewed aerial system uas platforms in the central united states to collect vertical profiles before and during storm events this work will illustrate the benefits of uas observations have on forecasting and numerical weather prediction of supercells mesoscale convective systems and winter storms
CIMMS (University of Oklahoma)
To collect vertical profiles before and during storm events by building and deploying uncrewed aerial system (UAS) platforms in the central United States. This work will illustrate the benefits that UAS observations have on forecasting
1
Anonymization, Bias Correction, and Assimilation of Smartphone Pressure Observations for Use in Numerical Weather Prediction in NOAA
Mass
Observations
check_circle
2021
Level 3
The goal of this project is to bring bias corrected, anonymized smartphone pressure data into NOAA operations and research. Specifically, this project will evaluate the use of voluminous smartphone pressure data for the initialization of NOAA numerical weather prediction models and for application in NOAA and other atmospheric research.
Clifford Mass
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Verification & Validation
Obs
This project will utilize pressure sensors within smartphones and collect smartphone pressure data to bring into the operational environment. The team will assess the impact of the large number of smartphone pressure measurements on operational forecasting and numerical weather prediction.
Pressure data is a valuable surface parameter and is crucial for mesoscale weather prediction, one of the key challenges facing NOAA. With billions of smartphones now capable of measuring atmospheric pressure, the utilization of this bias corrected, anonymized smartphone pressure data into NOAA operations and research will provide the ability to define important mesoscale weather features in regions of sparse operational observations.
TBD
Curtis Marshall
University of Washington
NA21OAR4590380
Relevant to All Hazards
Colorado, Washington
August 2021 - July 2025
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Verification & Validation
anonymization bias correction and assimilation of smartphone pressure observations for use in numerical weather prediction in noaa, the goal of this project is to bring bias corrected anonymized smartphone pressure data into noaa operations and research specifically this project will evaluate the use of voluminous smartphone pressure data for the initialization of noaa numerical weather prediction models and for application in noaa and other atmospheric research, this project will utilize pressure sensors within smartphones and collect smartphone pressure data to bring into the operational environment the team will assess the impact of the large number of smartphone pressure measurements on operational forecasting and numerical weather prediction
University of Washington
The goal of this project is to bring bias corrected, anonymized smartphone pressure data into NOAA operations and research. Specifically, this project will evaluate the use of voluminous smartphone pressure data for the initialization of
4
Autonomous Measurements of Air-Sea Interaction from Saildrones for Improved Hurricane Intensity Prediction
Zhang
Observations
check_circle
2021
Level 3
To advance improvements in hurricane forecasting by deploying Uncrewed Surface Vehicle (USV) Saildrones to measure the near-surface atmosphere and upper ocean during peak hurricane season.
Dongxiao Zhang, Jun Zhang
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Uncrewed Surface Vehicle (USV) Saildrones will be deployed to measure the near-surface atmosphere and upper ocean during peak hurricane season. Invaluable data collected by this innovative observing system will advance improvements in hurricane forecasting.
Accurately forecasting tropical cyclone (TC) intensity remains a challenge, which undoubtedly can have a major impact on life and property. Atmospheric and oceanic observations from Uncrewed Surface Vehicle (USV) saildrones in this project will provide unique and critical in-situ measurements of the upper ocean and near-surface atmosphere to improve Atlantic TC intensity forecasts.
TBD
CICOES (University of Washington), CIMAS (University of Miami)
NA21OAR4590394 NA22OAR4590118
Tropical Cyclones
Florida, Washington
August 2021 - July 2025
Uncrewed Systems & Vehicles
autonomous measurements of air sea interaction from saildrones for improved hurricane intensity prediction, to advance improvements in hurricane forecasting by deploying uncrewed surface vehicle usv saildrones to measure the near surface atmosphere and upper ocean during peak hurricane season, uncrewed surface vehicle usv saildrones will be deployed to measure the near surface atmosphere and upper ocean during peak hurricane season invaluable data collected by this innovative observing system will advance improvements in hurricane forecasting
CICOES (University of Washington), CIMAS (University of Miami)
To advance improvements in hurricane forecasting by deploying Uncrewed Surface Vehicle (USV) Saildrones to measure the near-surface atmosphere and upper ocean during peak hurricane season.
37
Development and Demonstration of a Low-Cost, Standalone Mode S EHS Aircraft Derived Atmospheric Observation System for Enhanced Weather Forecasting
McPartland
Observations
search_activity
2021
Level 3
To develop an existing prototype of the MIT Lincoln Lab's Portable Aircraft-Derived Weather Observation System (PADWOS) to demonstrate the instrument's ability to collect and extract in-situ wind and temperature observations from aircrafts equipped with Mode S Enhanced Surveillance (EHS) transponders. This work will provide valuable real-time data numerical weather prediction improvements and weather forecasting, particularly for the aviation industry.
Michael McPartland, Jason English
Active
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will further develop an existing prototype of the MIT Lincoln Lab's Portable Aircraft-Derived Weather Observation System (PADWOS) to demonstrate the instrument's ability to collect and extract in-situ wind and temperature observations from aircrafts equipped with Mode S Enhanced Surveillance (EHS) transponders. This work will provide valuable real-time data numerical weather prediction improvements and weather forecasting, particularly for the aviation industry.
Aircraft derived observations (ADOs) provide direct atmospheric measurements of parameters such as wind speed/direction and temperature, the highest valued inputs to Numerical Weather Prediction (NWP) models. These models are used extensively by society for a multitude of decision-making purposes affecting safety, economic performance, and efficiency of the nation. However, current ADO systems installed on select commercial aircraft are limited by low equipage rate, low spatial and temporal resolution, and significant latency, which limits their value to the weather forecasting community. This project will demonstrate a low-cost, standalone system that can obtain rapidly updating in situ wind and temperature observations from Mode S EHS-equipped aircraft in real-time, improving forecasting accuracy.
NWS Office of Observations
Curtis Marshall
MIT Lincoln Laboratory, CIRES (University of Colorado)
development and demonstration of a low cost standalone mode s ehs aircraft derived atmospheric observation system for enhanced weather forecasting, to develop an existing prototype of the mit lincoln lab's portable aircraft derived weather observation system padwos to demonstrate the instrument's ability to collect and extract in situ wind and temperature observations from aircrafts equipped with mode s enhanced surveillance ehs transponders this work will provide valuable real time data numerical weather prediction improvements and weather forecasting particularly for the aviation industry, this project will further develop an existing prototype of the mit lincoln lab's portable aircraft derived weather observation system padwos to demonstrate the instrument's ability to collect and extract in situ wind and temperature observations from aircrafts equipped with mode s enhanced surveillance ehs transponders this work will provide valuable real time data numerical weather prediction improvements and weather forecasting particularly for the aviation industry
MIT Lincoln Laboratory, CIRES (University of Colorado)
To develop an existing prototype of the MIT Lincoln Lab's Portable Aircraft-Derived Weather Observation System (PADWOS) to demonstrate the instrument's ability to collect and extract in-situ wind and temperature observations from aircrafts equipped with Mode
2
Development and Deployment of a Sea Clutter Class within the Operational WSR-88D Hydrometeor Classification Algorithm
Kurdzo
Observations
check_circle
2021
Level 3
To develop a new algorithm for sea clutter classification within the Open Radar Product Generator, the processing environment for Level-III products, for the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. With the sea clutter classifcation, air traffic controllers, weather forecasters, and other users will be able to properly discriminate radar returns to ensure safer airspace, provide accurate radar data for weather forecasting, and assist in greater confidence in discerning coastal weather in radar signatures.
James Kurdzo
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will develop a new algorithm for sea clutter classification within the Open Radar Product Generator, the processing environment for Level-III products, for the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. With the sea clutter class, air traffic controllers, weather forecasters, and other users will be able to properly discriminate radar returns to ensure safer airspace, provide accurate radar data for weather forecasting, and assist in greater confidence in discerning coastal weather in radar signatures.
Doppler weather radar is the single most-important remote sensing tool at the disposal of users with focus on weather hazard monitoring, nowcasting, and warning. Radar-derived products can be severely impacted by incorrect hydrometeor classifications, which can be passed onto algorithms that could potentially cause users to misinterpret radar display information and introduce further errors in other algorithms using the data. Development of a sea clutter database and Sea Clutter Detection Algorithm (SCDA) will improve WSR-88D data.
TBD
Michael Istok
MIT Lincoln Laboratory
NA21OAR4590385
Severe Weather
Massachusetts
August 2021 - July 2024
Radar
development and deployment of a sea clutter class within the operational wsr 88d hydrometeor classification algorithm, to develop a new algorithm for sea clutter classification within the open radar product generator the processing environment for level iii products for the weather surveillance radar 1988 doppler wsr 88d network with the sea clutter classifcation air traffic controllers weather forecasters and other users will be able to properly discriminate radar returns to ensure safer airspace provide accurate radar data for weather forecasting and assist in greater confidence in discerning coastal weather in radar signatures, this project will develop a new algorithm for sea clutter classification within the open radar product generator the processing environment for level iii products for the weather surveillance radar 1988 doppler wsr 88d network with the sea clutter class air traffic controllers weather forecasters and other users will be able to properly discriminate radar returns to ensure safer airspace provide accurate radar data for weather forecasting and assist in greater confidence in discerning coastal weather in radar signatures
MIT Lincoln Laboratory
To develop a new algorithm for sea clutter classification within the Open Radar Product Generator, the processing environment for Level-III products, for the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. With the sea clutter classifcation, air
0
Employing a combined observation and simulation- based framework to investigate spatiotemporal variability in urban heat and associated heat advection
Pal
Observations
check_circle
2021
Level 3
To develop a better understanding of the spatial variability and movement of urban heat within and around small cities utilizing a network of near surface observing systems. The datasets provided by this work will aid in improving urban heat forecasts, urban planning, and risk management.
Sandip Pal
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri Jan 31 2025 03:00:00 GMT-0500 (Eastern Standard Time)
In-situ Observation Technologies and Sensors, Surface Observing Networks, Community Preparedness & Resilience
Obs
High-Resolution Rapid Refresh (HRRR)
This project will utilize a network of near surface observing systems to develop a better understanding of the spatial variability and movement of urban heat within and around small cities. The datasets provided by this work will aid in improving urban heat forecasts, urban planning, and risk management.
Understanding Urban Heat Island (UHI) -induced meteorological processes in the atmosphere, areas in which populations are projected to increase over 60% around the world by 2050, is inadequate, and in turn, having a grave impact on human life. New observational data obtained through this project will improve understanding of UHI patterns and magnitude in different heat regimes, resulting in better forecasts for heat extremes, providing important information for planning outdoor events, and essentially helping the public avoid excessive heat-related hazards.
TBD
Danielle Nagele
Texas Tech University
NA21OAR4590361
Extreme Temperatures
Texas
August 2021 - January 2025
In-situ Observation Technologies and Sensors, Surface Observing Networks, Community Preparedness & Resilience
employing a combined observation and simulation based framework to investigate spatiotemporal variability in urban heat and associated heat advection, to develop a better understanding of the spatial variability and movement of urban heat within and around small cities utilizing a network of near surface observing systems the datasets provided by this work will aid in improving urban heat forecasts urban planning and risk management, this project will utilize a network of near surface observing systems to develop a better understanding of the spatial variability and movement of urban heat within and around small cities the datasets provided by this work will aid in improving urban heat forecasts urban planning and risk management
Texas Tech University
To develop a better understanding of the spatial variability and movement of urban heat within and around small cities utilizing a network of near surface observing systems. The datasets provided by this work will aid
12
Employing Small Unmanned Aircraft Systems to Improve Situational Awareness and Operational Physics Routines Used to Predict Tropical Cyclone Structure and Intensity
Zhang
Observations
check_circle
2021
Level 3
To advance understanding of tropical cyclone environments and improve physical routines within operational models using small uncrewed aircraft systems (sUAS) to collect data within the air-sea boundary. The data will be used to improve situational awareness for the National Hurricane Center forecasts.
Jun Zhang
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Uncrewed Systems & Vehicles, Community Preparedness & Resilience, Decision-Making
Obs
Advanced Weather Interactive Processing System (AWIPS), Hurricane Analysis and Forecast System (HAFS)
This project will use small uncrewed aircraft systems (sUAS) to collect data within the air-sea boundary in a tropical cyclone (TC) to fill a critical data gap. This data will advance understanding of this environment, which will aid in improving physical routines within operational forecast models. Additionally, the data will improve situational awareness for the National Hurricane Center forecasts.
Real-time boundary layer observations in Tropical Cyclones (TC) are crucial to National Hurricane Center forecasters and emergency managers responsible for making life and death decisions associated with landfalling tropical systems. However, data collection of temperature, moisture, and wind data below 500m altitude in the air-sea boundary where energy and momentum are exchanged with the sea and where severe winds at landfall can directly affect the lives and property is difficult to collect given the dangerous conditions. This project will evaluate and assess the benefits of using low-altitude small unmanned aircraft systems (sUAS) to improve operational situational awareness for TCs and enhance physical routines used by NOAA forecast models to predict TC structure and intensity change.
TBD
CIMAS/University of Miami
NA21OAR4590370
Tropical Cyclones
Florida
August 2021 - July 2025
Uncrewed Systems & Vehicles, Community Preparedness & Resilience, Decision-Making
employing small unmanned aircraft systems to improve situational awareness and operational physics routines used to predict tropical cyclone structure and intensity, to advance understanding of tropical cyclone environments and improve physical routines within operational models using small uncrewed aircraft systems suas to collect data within the air sea boundary the data will be used to improve situational awareness for the national hurricane center forecasts, this project will use small uncrewed aircraft systems suas to collect data within the air sea boundary in a tropical cyclone tc to fill a critical data gap this data will advance understanding of this environment which will aid in improving physical routines within operational forecast models additionally the data will improve situational awareness for the national hurricane center forecasts
CIMAS/University of Miami
To advance understanding of tropical cyclone environments and improve physical routines within operational models using small uncrewed aircraft systems (sUAS) to collect data within the air-sea boundary. The data will be used to improve situational
19
Evaluating Impact of In-situ Observations from Dynamically Targeted Long-Range Long-Duration Balloons
Sushko
Observations
check_circle
2021
Level 3
To evaluate the impact of global targeted soundings collected by long endurance, high-altitude balloons. Forecast skill will be analyzed to determine the overall effect of the soundings and will benefit operational forecasting and understanding.
Andrey Sushko
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri Mar 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Forecast Skill Metrics, In-situ Observation Technologies and Sensors, Uncrewed Systems & Vehicles, Verification & Validation
Obs
Global Forecast System (GFS)
This project will evaluate the impact of global targeted soundings collected by long endurance, high-altitude balloons. Forecast skill will be analyzed to determine the overall effect of the soundings from this innovative observing platform. This effort will benefit operational forecasting and understanding of weather systems across the globe if successful.
An observational platform that can cost-effectively augment and, in some cases, replace radiosonde and dropsonde observations would have significant value through improved data density and reduced operational cost. This effort supports NOAA's pursuit of innovative observing technologies to benefit operational forecasting, helping improve predictions and understanding of weather systems across the US and around the world.
NWS Office of Observations
Jordan Gerth
Windborne Systems Inc.
NA21OAR4590396
Severe Weather
California
August 2021 - March 2023
Data Assimilation and Ensembles, Dynamics and Nesting, Forecast Skill Metrics, In-situ Observation Technologies and Sensors, Uncrewed Systems & Vehicles, Verification & Validation
evaluating impact of in situ observations from dynamically targeted long range long duration balloons, to evaluate the impact of global targeted soundings collected by long endurance high altitude balloons forecast skill will be analyzed to determine the overall effect of the soundings and will benefit operational forecasting and understanding, this project will evaluate the impact of global targeted soundings collected by long endurance high altitude balloons forecast skill will be analyzed to determine the overall effect of the soundings from this innovative observing platform this effort will benefit operational forecasting and understanding of weather systems across the globe if successful
Windborne Systems Inc.
To evaluate the impact of global targeted soundings collected by long endurance, high-altitude balloons. Forecast skill will be analyzed to determine the overall effect of the soundings and will benefit operational forecasting and understanding.
0
High-impact Observations for Enhancing Great Lakes Snowfall Forecasting
L'Ecuyer
Observations
check_circle
2021
Level 3
To improve winter weather forecasting and situational awareness by deploying vertical pointing radars, snow particle imagers, and boundary layer sensors to examine snow characteristics.
Tristan L'Ecuyer
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Community Preparedness & Resilience, Forecast Product Usability & Design, Decision-Making, Decision Support
Obs
This project will deploy vertical pointing radars, snow particle imagers, and boundary layer sensors to examine snow characteristics. These snow observations will be used to demonstrate the full value of these data for improving winter weather forecasting, as well as winter weather situational awareness.
Improved snowfall forecasts can help mitigate the socioeconomic impacts through better planning and improved preparation of communities for extreme weather conditions. Further understanding of snow processes will lead to improved snow forecasts.
TBD
Jeff Waldstreicher
University of Wisconsin-Madison
NA21OAR4590367
Winter Weather
Illinois, Michigan, Wisconsin
August 2021 - July 2025
In-situ Observation Technologies and Sensors, Community Preparedness & Resilience, Forecast Product Usability & Design, Decision-Making, Decision Support
high impact observations for enhancing great lakes snowfall forecasting, to improve winter weather forecasting and situational awareness by deploying vertical pointing radars snow particle imagers and boundary layer sensors to examine snow characteristics, this project will deploy vertical pointing radars snow particle imagers and boundary layer sensors to examine snow characteristics these snow observations will be used to demonstrate the full value of these data for improving winter weather forecasting as well as winter weather situational awareness
University of Wisconsin-Madison
To improve winter weather forecasting and situational awareness by deploying vertical pointing radars, snow particle imagers, and boundary layer sensors to examine snow characteristics.
4
Improvement in NOAA Winter Weather Operations using In Situ Mesonet Observations
Wang
Observations
check_circle
2021
Level 3
To more accurately monitor, warn, and verify winter weather utilizing data from the New York State Mesonet (NYSM). This project will demonstrate an innovative, high quality system for monitoring snow depth, snowfall rates and accumulation, snow water equivalent, freezing rain, and precipitation type at high spatial resolution in real-time.
Junhong "June" Wang
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Surface Observing Networks, Decision-Making
Obs
This project will demonstrate an innovative observing system for monitoring snow depth, snowfall types and accumulation, snow water equivalent, freezing rain, and precipitation type at high spatial resolution in real time. This new observing system will more accurately monitor, warn, and verify winter weather.
Winter weather is one of the most impactful weather phenomena and among the most difficult to monitor and predict. Improvement to winter weather products and situational awareness will lead to improved winter weather forecasts.
TBD
Jeff Waldstreicher
University at Albany - SUNY
NA21OAR4590376
Winter Weather
New York
August 2021 - July 2024
Dynamics and Nesting, In-situ Observation Technologies and Sensors, Surface Observing Networks, Decision-Making
improvement in noaa winter weather operations using in situ mesonet observations, to more accurately monitor warn and verify winter weather utilizing data from the new york state mesonet nysm this project will demonstrate an innovative high quality system for monitoring snow depth snowfall rates and accumulation snow water equivalent freezing rain and precipitation type at high spatial resolution in real time, this project will demonstrate an innovative observing system for monitoring snow depth snowfall types and accumulation snow water equivalent freezing rain and precipitation type at high spatial resolution in real time this new observing system will more accurately monitor warn and verify winter weather
University at Albany - SUNY
To more accurately monitor, warn, and verify winter weather utilizing data from the New York State Mesonet (NYSM). This project will demonstrate an innovative, high quality system for monitoring snow depth, snowfall rates and accumulation,
6
Improving Analysis and Communication of Extreme Temperatures Across the New York City Metropolis Using a Dense Network of In Situ Observations
Bassill
Observations
check_circle
2021
Level 3
To improve forecasts, risk communication, and decision making by developing a real-time Extreme Temperature Dashboard, which will offer granular extreme temperature analyses to fill data gaps in the ASOS observational network.
Nick Bassill
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Surface Observing Networks, Visualizations, Decision-Making, Risk Communication, "Social, Behavioral, and Economic Sciences (SBES)"
Obs
This project will develop a real-time Extreme Temperature Dashboard, which will offer granular extreme temperature analyses, to fill data gaps in the ASOS observational network. This product will be implemented into daily operations and will aid in improving forecasts, risk communication, as well as decision making.
Heat/Extreme temperatures kill more Americans than any other type of hazardous weather. Better utilization of existing disparate observational networks of opportunity for situational awareness, better understanding of model error and bias characteristics, and an improved understanding of micro-climates within NYC will improve probability of detection of extreme temperatures and lead to more accurate forecasts with better lead times. Additionally, it will identify geographical areas and populations that have the greatest vulnerability to extreme temperatures.
improving analysis and communication of extreme temperatures across the new york city metropolis using a dense network of in situ observations, to improve forecasts risk communication and decision making by developing a real time extreme temperature dashboard which will offer granular extreme temperature analyses to fill data gaps in the asos observational network, this project will develop a real time extreme temperature dashboard which will offer granular extreme temperature analyses to fill data gaps in the asos observational network this product will be implemented into daily operations and will aid in improving forecasts risk communication as well as decision making
University at Albany - SUNY
To improve forecasts, risk communication, and decision making by developing a real-time Extreme Temperature Dashboard, which will offer granular extreme temperature analyses to fill data gaps in the ASOS observational network.
3
Improving Flood Inundation Mapping Using UAS-Based Optical Imagery
Dyer
Observations
search_activity
2021
Level 3
To enhance the mission capabilities of UAS observations of hydrologic events which includes production of land-water masks to show inundated areas, development of digital surface models (DSM), and identification of surface features such as debris lines and submerged vegetation.
Jamie Dyer
Active
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Nov 30 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Uncrewed Systems & Vehicles, Verification & Validation
Obs
National Water Model (NWM)
This project will deploy a large uncrewed aerial system (UAS) platform during active flood events and conditions to collect imagery and disseminate the imagery to river forecast centers, specifically in the NWS Southern Region. This project will not only enhance the capabilities of UAS during hydrologic events, but will also provide data that will aid in model verification during these conditions.
River flooding is one of the leading causes of the loss of life and property, making improved prediction of high-water conditions critical, especially during active flooding situations. Observations along rivers are primarily limited to main-stem reaches, these local-scale conditions and the associated hydrologic responses are often unaccounted for and/or poorly represented in numerical prediction models. This project aims to enhance the mission capabilities of UAS observations of hydrologic events through product development and data analysis that will aid forecasters during flooding events.
TBD
Jason Johnson
Mississippi State University
NA21OAR4590366
Water Extremes
Mississippi
August 2021 - November 2025
Uncrewed Systems & Vehicles, Verification & Validation
improving flood inundation mapping using uas based optical imagery, to enhance the mission capabilities of uas observations of hydrologic events which includes production of land water masks to show inundated areas development of digital surface models dsm and identification of surface features such as debris lines and submerged vegetation, this project will deploy a large uncrewed aerial system uas platform during active flood events and conditions to collect imagery and disseminate the imagery to river forecast centers specifically in the nws southern region this project will not only enhance the capabilities of uas during hydrologic events but will also provide data that will aid in model verification during these conditions
Mississippi State University
To enhance the mission capabilities of UAS observations of hydrologic events which includes production of land-water masks to show inundated areas, development of digital surface models (DSM), and identification of surface features such as debris
0
Improving Quality Control of Multi-Radar Multi-Sensor Products from Non-Meteorological Radar Data Artifacts
Brogden
Observations
check_circle
2021
Level 3
To upgrade the MRMS Quality Control (QC) process to more effectively identify and filter non-meteorological artifacts from the raw radar variables before they are used downstream in any MRMS product generation.
Jeff Brogden
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will focus on upgrading the quality control algorithm for the Multi-Radar Multi-Senor (MRMS) system. The upgrade will allow the system to more effectively identify and filter non-meteorological artifacts from the radar data. This will eliminate these non-meteorological artifacts from transferring into final radar data products that serve operational end users across aviation, severe weather, and numerical weather prediction.
Warning operations at The National Weather Service (NWS) is critical to the mission of the protection of life, property, and the economy. Improvement of quality control of Multi-Radar Multi-Sensor (MRMS) products from non-meteorological radar data artifacts will improve data utilized for warning decisions by operational meteorologists, as well as automated systems such as numerical models, decision support and situational awareness dashboards, and data-driven machine learning models.
improving quality control of multi radar multi sensor products from non meteorological radar data artifacts, to upgrade the mrms quality control qc process to more effectively identify and filter non meteorological artifacts from the raw radar variables before they are used downstream in any mrms product generation, this project will focus on upgrading the quality control algorithm for the multi radar multi senor mrms system the upgrade will allow the system to more effectively identify and filter non meteorological artifacts from the radar data this will eliminate these non meteorological artifacts from transferring into final radar data products that serve operational end users across aviation severe weather and numerical weather prediction
CIMMS (University of Oklahoma)
To upgrade the MRMS Quality Control (QC) process to more effectively identify and filter non-meteorological artifacts from the raw radar variables before they are used downstream in any MRMS product generation.
0
Observation of Sea Level Air Pressure and Directional Wave Spectra from Innovative Expendable Drifting Buoys
Centurioni
Observations
check_circle
2021
Level 3
To improve the accessibility of air pressure data from drifters and address the scientific challenges of how to determine the optimum density and time-continuum of the Sea Level Pressure (SLP) array to support forecasts and evaluation of forecasts.
Luca Centurioni
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Advanced Weather Interactive Processing System (AWIPS)
This project will add barometers to the Global Drifter Program's Directional Wave Spectra Drifter (DWSD) buoys. The addition of barometers will provide air pressure observations in the ocean environment, which have been shown to improve weather forecasting and aid in validation of storm forecasts.
Air pressure observations from drifters are known to dramatically improve the short-to medium range weather forecasting and provide data for the validation of forecasts of severe storms. Yet many drifters are too often deployed without a barometer. This project aims to to increase the availability of observations of Sea Level Pressure (SLP) from drifters in critical areas such as the north east coast and north west coast of the continental United States (CONUS), the Gulf Stream region, and key areas of the north Pacific where accurate warnings are critical to civilian users and commerce.
TBD
Joseph Sienkiewicz
University of California - San Diego
NA21OAR4590374
Severe Weather
California, Maryland
August 2021 - July 2024
Verification & Validation, Weather Buoy Systems
observation of sea level air pressure and directional wave spectra from innovative expendable drifting buoys, to improve the accessibility of air pressure data from drifters and address the scientific challenges of how to determine the optimum density and time continuum of the sea level pressure slp array to support forecasts and evaluation of forecasts, this project will add barometers to the global drifter program's directional wave spectra drifter dwsd buoys the addition of barometers will provide air pressure observations in the ocean environment which have been shown to improve weather forecasting and aid in validation of storm forecasts
University of California - San Diego
To improve the accessibility of air pressure data from drifters and address the scientific challenges of how to determine the optimum density and time-continuum of the Sea Level Pressure (SLP) array to support forecasts and
0
Particle Imaging and Ceilometer Observations for Snowfall Properties and Blizzard Parameters
Wood
Observations
check_circle
2021
Level 3
To demonstrate that existing operational instruments (ceilometers) and new instrumentation (particle imagers) can be used to develop operational support products for blizzards and other hazardous winter precipitation events.
Norman Wood, Aaron Kennedy
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will use new particle imagers and laser ceilometers to measure and detect various snow properties. This work will aid in improving blizzard and winter precipitation forecasting, as well as contribute to model verification.
Blizzard conditions caused by high winds and severely limited visibility over several hours can result in transportation hazards and human health and safety concerns, as well as animal health and livestock operations. As a result, accurate forecasting of blizzard occurrence, intensity and extent is key to improving response to these hazards. This project will contribute to reducing adverse outcomes related to transportation, commerce, and health associated with blizzard events.
TBD
Chauncy Schultz
University of Wisconsin-Madison, University of North Dakota
particle imaging and ceilometer observations for snowfall properties and blizzard parameters, to demonstrate that existing operational instruments ceilometers and new instrumentation particle imagers can be used to develop operational support products for blizzards and other hazardous winter precipitation events, this project will use new particle imagers and laser ceilometers to measure and detect various snow properties this work will aid in improving blizzard and winter precipitation forecasting as well as contribute to model verification
University of Wisconsin-Madison, University of North Dakota
To demonstrate that existing operational instruments (ceilometers) and new instrumentation (particle imagers) can be used to develop operational support products for blizzards and other hazardous winter precipitation events.
0
Rapid Floodwater Extent and Depth Measurements Using Optical UAV and SAR
Hashemi-Beni
Observations
check_circle
2021
Level 3
To develop machine learning techniques that utilize high resolution, remotely sensed data for flood products, and to provide more accurate flood extent and depth products to local NWS WFOs and SERFC.
Leila Hashemi-Beni
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Dynamics and Nesting, Uncrewed Systems & Vehicles, Decision-Making, Risk Communication
Obs
This project will develop machine learning techniques that utilize high resolution, remotely sensed data for flood products. These products include more accurate flood extent, water depth, and floodplain topography to benefit flood forecasting, decision making, risk communication, and flood damage assessment practices.
Flooding is a catastrophic and frequently occurring natural disaster that causes extensive damage to life, infrastructure, and the environment. Optical and SAR sensor data and associated algorithm and model development from this project will provide more accurate flood extent and depth products, even in heavily vegetated areas where water is often obscured in satellite RS products, important in flood forecasting and disaster management.
TBD
Shawn Carter
North Carolina Agricultural & Technical State University
NA21OAR4590358
Water Extremes
North Carolina
August 2021 - July 2024
Artificial Intelligence (AI) / Machine Learning (ML), Dynamics and Nesting, Uncrewed Systems & Vehicles, Decision-Making, Risk Communication
rapid floodwater extent and depth measurements using optical uav and sar, to develop machine learning techniques that utilize high resolution remotely sensed data for flood products and to provide more accurate flood extent and depth products to local nws wfos and serfc, this project will develop machine learning techniques that utilize high resolution remotely sensed data for flood products these products include more accurate flood extent water depth and floodplain topography to benefit flood forecasting decision making risk communication and flood damage assessment practices
North Carolina Agricultural & Technical State University
To develop machine learning techniques that utilize high resolution, remotely sensed data for flood products, and to provide more accurate flood extent and depth products to local NWS WFOs and SERFC.
8
Uncrewed aircraft observations to characterize the land surface and its interaction with the lower atmosphere in areas of complex terrain for improved prediction of water and weather
Boer
Observations
check_circle
2021
Level 3
To provide new observational perspectives on surface properties including soil moisture, snow cover, snow reflectivity and vegetation density and health through the utilization of uncrewed aerial systems (UAS), and provide products and outputs that support weather prediction operational needs.
Gijs de Boer
Complete
Sun Aug 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Radar, Uncrewed Systems & Vehicles
Obs
Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
This project will utilize observations from uncrewed aerial systems (UAS) to gain a better understanding, as well as new perspectives, of weather and water over complex terrain. These observations will include soil moisture, snow cover, snow reflectivity, vegetation, as well as lower atmosphere properties. These observations will aid in development and advancement of forecasting and prediction within weather and water prediction communities.
Areas of mountainous terrain not only play a critical role in supporting water security for millions of people, but weather in mountainous regions is additionally important for many reasons including the development of renewable energy resources, understanding fire potential, and aviation safety. As a result, accurate weather and water prediction over areas of complex terrain is a critical societal need that must be informed by the best observational networks and prediction tools available. This project aims to demonstrate the impacts of innovative observing technologies on prediction and understanding of weather and water over complex terrain.
TBD
Jim O'Sullivan
CIRES/University of Colorado
NA21OAR4590363
Winter Weather
Colorado
August 2021 - July 2024
Aircraft Reconnaissance, Radar, Uncrewed Systems & Vehicles
uncrewed aircraft observations to characterize the land surface and its interaction with the lower atmosphere in areas of complex terrain for improved prediction of water and weather, to provide new observational perspectives on surface properties including soil moisture snow cover snow reflectivity and vegetation density and health through the utilization of uncrewed aerial systems uas and provide products and outputs that support weather prediction operational needs, this project will utilize observations from uncrewed aerial systems uas to gain a better understanding as well as new perspectives of weather and water over complex terrain these observations will include soil moisture snow cover snow reflectivity vegetation as well as lower atmosphere properties these observations will aid in development and advancement of forecasting and prediction within weather and water prediction communities
CIRES/University of Colorado
To provide new observational perspectives on surface properties including soil moisture, snow cover, snow reflectivity and vegetation density and health through the utilization of uncrewed aerial systems (UAS), and provide products and outputs that support
7
Autonomous Measurements of Air-Sea Interaction from Saildrones for Improved Hurricane Intensity Prediction
Zhang
Observations
search_activity
2022
Level 3
To advance improvements in hurricane forecasting by deploying Uncrewed Surface Vehicle (USV) Saildrones to measure the near-surface atmosphere and upper ocean during peak hurricane season.
Jun Zhang
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Parameterization, Uncrewed Systems & Vehicles
Obs
Advanced Weather Interactive Processing System (AWIPS), Hurricane Analysis and Forecast System (HAFS)
Improving the accuracy and ultimate value of NOAA's operational hurricane forecasts requires
more complete real-time knowledge of atmospheric and oceanic conditions and more realistic
representation of key physical processes in hurricane forecast models. This proposal seeks to
address these needs through the deployment of Uncrewed Surface Vehicle (USV) saildrones
during the peak of the Atlantic hurricane season. Powered by renewable energy (wind and solar),
the USV saildrones are larger (7 m long, more than 220 lb payload capacity), faster (3-5 kt on
average) and have longer endurance (8-12 months science mission) than most other marine
Uncrewed Systems (UxS). A new development is the specially designed reinforced saildrones
that can sustain hurricane force wind and high seas. In this project, the uncrewed autonomous
marine system will continuously measure the near-surface atmosphere (temperature, humidity, 3-
dimensional wind, solar and longwave radiation, and pressure) and upper ocean (sea surface
temperature and salinity, surface and subsurface ocean currents, surface wave height and period),
with goals to transmit the observations in real-time to GTS and assimilate into NOAA's
operational hurricane forecast models. The saildrones will follow tracks in the western tropical
Atlantic and Gulf of Mexico, regions with high hurricane track densities and high probability of
storm development. They will be navigated to systematically collect observations ahead of and
inside of storms of interest by repeating tracks. The proposed saildrone measurements will be
coordinated with NOAA's operational hurricane field programs that have been providing
invaluable observations. Planned coordination with underwater glider observations will provide
the first collocated measurements of the upper ocean and atmosphere from uncrewed vehicles
during hurricane events. In addition, analysis will be conducted to quantify air-sea heat and
momentum fluxes, assess hurricane model errors, improve model parameterizations, and
determine the value of saildrone observations for data assimilation and prediction. The proposed
saildrones will serve as a proof of concept for making regular autonomous measurements across
the air-sea interface during the hurricane season. The cross-line office (OAR, NWS) commitment
to the proposal illustrates the significant value the proposed measurements would likely bring to
NOAA and the high potential for transitioning to routine operations. This project is directly
related to the WPO "Observations" program, specifically "UxS and expendables." It also
addresses key priorities of the Weather Research and Forecasting Innovation Act of 2017, by
"identifying data gaps in observing capabilities and determining a range of options to address
those gaps" and improving the prediction of hurricane rapid intensification.
Analyze air-sea flux and wave data. Conduct model evaluation. Coordinate with
hurricane sUAS and reconnaissance flight missions. Aid in data assimilation of saildrone atmospheric data.
TBD
CIMAS (University of Miami)
NA22OAR4590118
Tropical Cyclones
Florida
August 2022 - July 2025
Atmospheric Physics, Data Assimilation and Ensembles, Parameterization, Uncrewed Systems & Vehicles
autonomous measurements of air sea interaction from saildrones for improved hurricane intensity prediction, to advance improvements in hurricane forecasting by deploying uncrewed surface vehicle usv saildrones to measure the near surface atmosphere and upper ocean during peak hurricane season, improving the accuracy and ultimate value of noaa's operational hurricane forecasts requires more complete real time knowledge of atmospheric and oceanic conditions and more realistic representation of key physical processes in hurricane forecast models this proposal seeks to address these needs through the deployment of uncrewed surface vehicle usv saildrones during the peak of the atlantic hurricane season powered by renewable energy wind and solar the usv saildrones are larger 7 m long more than 220 lb payload capacity faster 3 5 kt on average and have longer endurance 8 12 months science mission than most other marine uncrewed systems uxs a new development is the specially designed reinforced saildrones that can sustain hurricane force wind and high seas in this project the uncrewed autonomous marine system will continuously measure the near surface atmosphere temperature humidity 3 dimensional wind solar and longwave radiation and pressure and upper ocean sea surface temperature and salinity surface and subsurface ocean currents surface wave height and period with goals to transmit the observations in real time to gts and assimilate into noaa's operational hurricane forecast models the saildrones will follow tracks in the western tropical atlantic and gulf of mexico regions with high hurricane track densities and high probability of storm development they will be navigated to systematically collect observations ahead of and inside of storms of interest by repeating tracks the proposed saildrone measurements will be coordinated with noaa's operational hurricane field programs that have been providing invaluable observations planned coordination with underwater glider observations will provide the first collocated measurements of the upper ocean and atmosphere from uncrewed vehicles during hurricane events in addition analysis will be conducted to quantify air sea heat and momentum fluxes assess hurricane model errors improve model parameterizations and determine the value of saildrone observations for data assimilation and prediction the proposed saildrones will serve as a proof of concept for making regular autonomous measurements across the air sea interface during the hurricane season the cross line office oar nws commitment to the proposal illustrates the significant value the proposed measurements would likely bring to noaa and the high potential for transitioning to routine operations this project is directly related to the wpo "observations" program specifically "uxs and expendables " it also addresses key priorities of the weather research and forecasting innovation act of 2017 by "identifying data gaps in observing capabilities and determining a range of options to address those gaps" and improving the prediction of hurricane rapid intensification
CIMAS (University of Miami)
To advance improvements in hurricane forecasting by deploying Uncrewed Surface Vehicle (USV) Saildrones to measure the near-surface atmosphere and upper ocean during peak hurricane season.
19
Infrasonic Weather Monitoring Research to Improve Detection of Violent Weather
Waxler
Observations
check_circle
2022
Level 3
To install a network of infrasound sensor arrays to test the hypothesis that the infrasound signal emitted by tornadoes can be used to track tornadoes accurately and reliably.
Roger Waxler, William Garth Frazier, Carrick L. Talmadge
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Atmospheric Physics
Obs
Analysis of data collected by researchers at the National Center for Physical Acoustics
(NCPA) at the University of Mississippi (UM) under contract to NOAA (Infrasound Detection of Tornadoes FY2017 and FY2018) has shown that in every case in which a tornado
was in a location for which propagation conditions suggested that an infrasonic signal from
that tornado would be detected on a deployed array it was. Detections were in the 2 to
8 Hz band. Analysis of storms that did not produce tornadoes showed that only lightning
(that can easily be distinguished from tornadoes) and static anthropomorphic sources (again
easily identifiable as such) produced detections in the 2 to 8 Hz band. Further, in cases in
which ground truth tornado tracks are available, back azimuths from the tornado detections coincide with the directions from the detecting array to the tornado tracks. We are
convinced that, in cases in which a tornado is detected on multiple arrays a tornado track
can be determined. If true, this could provide automated tornado detection and tracking
capability, albeit subject to the time delays (about three seconds per kilometer) inherent in
acoustic propagation. It is our goal, in this project, to install a prototype detection network
of infrasound sensing arrays to the hypothesis that infrasound detection could be a practical
tool for tornado hazard monitoring. Currently, the approach is to use the crudest information from the infrasonic signal: detection of the signal. Work completed under contract
to NOAA (contract NA19OAR4590339) on identifying the physical mechanism underlying
these infrasonic emissions will continue. An understanding of the elements of a tornado that
cause the emission of infrasound will inform future data analysis and could lead to methods
through which information about a specific tornado could be extracted from the received
infrasonic signals.
The proposed activity would advance the science of tornado detection and monitoring, both for civil applications and for improved cataloging and observation of tornado genesis
N/A
N/A
University of Mississippi
NA22OAR4690593
Severe Weather
Mississippi
September 2022 - August 2024
Atmospheric Composition and Chemistry, Atmospheric Physics
infrasonic weather monitoring research to improve detection of violent weather, to install a network of infrasound sensor arrays to test the hypothesis that the infrasound signal emitted by tornadoes can be used to track tornadoes accurately and reliably, analysis of data collected by researchers at the national center for physical acoustics ncpa at the university of mississippi um under contract to noaa infrasound detection of tornadoes fy2017 and fy2018 has shown that in every case in which a tornado was in a location for which propagation conditions suggested that an infrasonic signal from that tornado would be detected on a deployed array it was detections were in the 2 to 8 hz band analysis of storms that did not produce tornadoes showed that only lightning that can easily be distinguished from tornadoes and static anthropomorphic sources again easily identifiable as such produced detections in the 2 to 8 hz band further in cases in which ground truth tornado tracks are available back azimuths from the tornado detections coincide with the directions from the detecting array to the tornado tracks we are convinced that in cases in which a tornado is detected on multiple arrays a tornado track can be determined if true this could provide automated tornado detection and tracking capability albeit subject to the time delays about three seconds per kilometer inherent in acoustic propagation it is our goal in this project to install a prototype detection network of infrasound sensing arrays to the hypothesis that infrasound detection could be a practical tool for tornado hazard monitoring currently the approach is to use the crudest information from the infrasonic signal detection of the signal work completed under contract to noaa contract na19oar4590339 on identifying the physical mechanism underlying these infrasonic emissions will continue an understanding of the elements of a tornado that cause the emission of infrasound will inform future data analysis and could lead to methods through which information about a specific tornado could be extracted from the received infrasonic signals
Not Applicable (N/A)
University of Mississippi
To install a network of infrasound sensor arrays to test the hypothesis that the infrasound signal emitted by tornadoes can be used to track tornadoes accurately and reliably.
1
Airborne Phased Array Radar (APAR) Sub-Array Panel (SAP) Build and Final Design Activities
Joseph
Observations
search_activity
2022
Level 3
To provide the research community access to an airborne precipitation radar with the resolution and dual-polarimetric capabilities required.
Everette Joseph
Active
Sat Oct 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Atmospheric Physics, Dynamics and Nesting, Radar
APAR
Development of the next generation of airborne Doppler radars is
required in order to: a) close the capability gap in airborne remote sensing of precipitating
systems created by the retirement of the Electra Doppler Radar (ELDORA) and the anticipated
retirement of the Tail Doppler Radar (TDR) in the next decade, and b) provide remote sensing
observations at wavelengths capable of penetrating deeper into heavy precipitation and with
dual-polarimetric capabilities necessary for microphysical retrievals and agile scanning. The
National Center for Atmospheric Research (NCAR), through a partnership with NOAA and
university and industry collaborators, proposes to implement the first-ever Airborne Phased
Array Radar (APAR).
The unparalleled capabilities of Airborne Phases Array Radar (APAR) will provide much-needed observational data to advance physics and microphysics representations of clouds and precipitation in numerical models and improve the understanding and predictability of weather and climate hazards that are an
increasing threat to society due to climate change. APAR will feature four removable, C-band, active electronically-scanned arrays (AESAs) mounted on the top, both sides, and the back ramp of the NSF/NCAR C-130 as shown in Fig. 1.
OMAO/AOC
NWS/EMC
NWS/NHC
OAR/HRD
Michael Brennan
Vijay Tallapragada
National Center for Atmospheric Research
NA23OAR4590125
Severe Weather
Colorado
October 2022 - September 2024
Aircraft Reconnaissance, Atmospheric Physics, Dynamics and Nesting, Radar
airborne phased array radar apar sub array panel sap build and final design activities, to provide the research community access to an airborne precipitation radar with the resolution and dual polarimetric capabilities required, development of the next generation of airborne doppler radars is required in order to a close the capability gap in airborne remote sensing of precipitating systems created by the retirement of the electra doppler radar eldora and the anticipated retirement of the tail doppler radar tdr in the next decade and b provide remote sensing observations at wavelengths capable of penetrating deeper into heavy precipitation and with dual polarimetric capabilities necessary for microphysical retrievals and agile scanning the national center for atmospheric research ncar through a partnership with noaa and university and industry collaborators proposes to implement the first ever airborne phased array radar apar
National Center for Atmospheric Research
To provide the research community access to an airborne precipitation radar with the resolution and dual-polarimetric capabilities required.
0
Demonstrate and Expand a 21st Century Radar, Modeling, and Decision Support System to Improve Environmental Equity and Resilience to Extreme Precipitation in a Mountainous, West-Coast, Urban Area: Phase 2 of the Advanced Quantitative Precipitation Information (AQPI) System
Ralph
Observations
search_activity
2023
Level 3
To address gaps in current meteorological observations that are characteristic of west-coast urban areas to improve the prediction of extreme precipitation across the San Francisco Bay Area, demonstrating the value of gap-filling radars in providing better observations and forecasts.
Fred Ralph, V. Chandrasekar
Active
Sat Jul 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon Jun 30 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Radar, Community Preparedness & Resilience, Decision Support
Obs
This project will apply and advance the recently established Advanced Quantitative Precipitation Information (AQPI) system in the San Francisco Bay Area and demonstrate how the AQPI network of 5 gap-filling radars can be integrated with high-resolution modeling and novel decision support tools to increase resilience to extreme precipitation and related hazards across the San Francisco Bay Area. This project will address gaps in current meteorological observations that are characteristic of west-coast urban areas to improve the prediction of extreme precipitation in these areas.
This project will demonstrate how the Advanced Quantitative Precipitation Information (AQPI) network of 5 gap-filling radars can be integrated with high-resolution modeling and novel decision support tools to increase resilience to extreme precipitation and related hazards across the San Francisco Bay Area. The project will partner with local, regional, and national NWS offices, as well as local entities, to pave the way for longer-term implementation where warranted.
TBD
TBD
CIMEAS/Scripps Institute of Oceanography
NA20OAR4320278
Water Extremes
California
July 2023 - June 2025
Dynamics and Nesting, Radar, Community Preparedness & Resilience, Decision Support
demonstrate and expand a 21st century radar modeling and decision support system to improve environmental equity and resilience to extreme precipitation in a mountainous west coast urban area phase 2 of the advanced quantitative precipitation information aqpi system, to address gaps in current meteorological observations that are characteristic of west coast urban areas to improve the prediction of extreme precipitation across the san francisco bay area demonstrating the value of gap filling radars in providing better observations and forecasts, this project will apply and advance the recently established advanced quantitative precipitation information aqpi system in the san francisco bay area and demonstrate how the aqpi network of 5 gap filling radars can be integrated with high resolution modeling and novel decision support tools to increase resilience to extreme precipitation and related hazards across the san francisco bay area this project will address gaps in current meteorological observations that are characteristic of west coast urban areas to improve the prediction of extreme precipitation in these areas
CIMEAS/Scripps Institute of Oceanography
To address gaps in current meteorological observations that are characteristic of west-coast urban areas to improve the prediction of extreme precipitation across the San Francisco Bay Area, demonstrating the value of gap-filling radars in providing
1
A Southeastern US Regional Flash Drought Review and Agriculture Impact Assessment
Ellenburg
Observations
check_circle
2023
Level 3
To conduct a spatial and temporal review of rapid onset drought metrics across the SE using regionally derived datasets to compare characteristics and be correlated with crop calendars to assess the highest risk potential of flash drought for multiple representative crops of the region.
Lee Ellenburg
Complete
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
National Integrated Drought Information System (NIDIS)
With the specific focus on the onset, timing, duration, and impacts of flash droughts across the Southeastern US, this project will conduct a spatial and temporal review of rapid onset drought metrics using regionally derived datasets to compare characteristics such as timing, antecedent conditions, impacts, and amelioration. The project's outcome will be a quantitative assessment and these results will be correlated with crop calendars to assess the highest risk potential of flash drought for multiple representative crops of the region.
This project will conduct a spatial and temporal review of rapid onset drought metrics across the SE using regionally derived datasets to compare characteristics such as timing, antecedent conditions, impacts, and amelioration. These results will be correlated with crop calendars to assess the highest risk potential of flash drought for multiple representative crops of the region.
TBD
TBD
University of Alabama in Huntsville
NA23OAR4590590
Water Extremes
Alabama
August 2023 - July 2024
a southeastern us regional flash drought review and agriculture impact assessment, to conduct a spatial and temporal review of rapid onset drought metrics across the se using regionally derived datasets to compare characteristics and be correlated with crop calendars to assess the highest risk potential of flash drought for multiple representative crops of the region, with the specific focus on the onset timing duration and impacts of flash droughts across the southeastern us this project will conduct a spatial and temporal review of rapid onset drought metrics using regionally derived datasets to compare characteristics such as timing antecedent conditions impacts and amelioration the project's outcome will be a quantitative assessment and these results will be correlated with crop calendars to assess the highest risk potential of flash drought for multiple representative crops of the region
University of Alabama in Huntsville
To conduct a spatial and temporal review of rapid onset drought metrics across the SE using regionally derived datasets to compare characteristics and be correlated with crop calendars to assess the highest risk potential of
0
Supporting NWS Post-Fire Flash-Flood Warnings with Multi-Sensor Burn Scar Mapping
Batzli
Observations
search_activity
2023
Level 3
To develop prototype near real-time, data-fused burn scar mapping products to improve wildland fire burn scar mapping during and after fire events, resulting in enhanced situational awareness.
Sam Batzli
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will leverage existing satellite sensors, including Visible Infrared Imaging Radiometer Suite, Geostationary Operational Environmental Satellite-R, Series Advanced Baseline Imager, and Sentinel-1A C-Band Synthetic Aperture Radar, and integrate the capabilities of these sensors with previous research performed by the project team to develop prototype burn scar mapping products. The burn scar mapping products will be near real-time, data-fused, gridded products that can be tested in Geographic Information Systems and web map services. Cloud computing and machine learning will be utilized to reduce data latency to deliver faster data updates to emergency managers and Incident Meteorologists for situational awareness, as well as quickly deliver model input burn scar layers to National Weather Service Weather Prediction Center and National Weather Service weather forecast office hydrologists. The objectives of this project will address the immediate need for better burn scar maps to help forecasters issue timely warnings for post-fire flash flooding.
Our vision as a team is to combine our expertise and technical skills and leverage the best qualities of each sensor by integrating our past applied research (Readiness-Level 2) to develop prototype demonstration (Readiness-Level 6) products in a the form of near real-time, data-fused, gridded products suitable for testing in Geographic Information Systems (GIS) and web map services. We will apply the power of cloud computing with Google Earth Engine (Google Cloud) and machine learning to our workflows to reduce latencies and improve quality. The focus is on reducing latency for delivering 1) prototype perimeter updates of burned areas to emergency managers and IMETS (Incident Meteorologists) for better situational awareness and delivering 2) a model input burn scar layer to NWS WPC for Mesoscale Precipitation Discussion (MPD) and to NWS WFO hydrologists responsible for recommending post-fire flash flood warnings, as an earlier input surrogate to the often delayed Burned Area Reflectance Classification (BARC) maps or US Geological Survey (USGS) Landslide Hazards Program maps currently in use.
supporting nws post fire flash flood warnings with multi sensor burn scar mapping, to develop prototype near real time data fused burn scar mapping products to improve wildland fire burn scar mapping during and after fire events resulting in enhanced situational awareness, this project will leverage existing satellite sensors including visible infrared imaging radiometer suite geostationary operational environmental satellite r series advanced baseline imager and sentinel 1a c band synthetic aperture radar and integrate the capabilities of these sensors with previous research performed by the project team to develop prototype burn scar mapping products the burn scar mapping products will be near real time data fused gridded products that can be tested in geographic information systems and web map services cloud computing and machine learning will be utilized to reduce data latency to deliver faster data updates to emergency managers and incident meteorologists for situational awareness as well as quickly deliver model input burn scar layers to national weather service weather prediction center and national weather service weather forecast office hydrologists the objectives of this project will address the immediate need for better burn scar maps to help forecasters issue timely warnings for post fire flash flooding
SSEC/CIMS/University of Wisconsin-Madison
To develop prototype near real-time, data-fused burn scar mapping products to improve wildland fire burn scar mapping during and after fire events, resulting in enhanced situational awareness.
2
Adjusting Aircraft Wind Observations to 1-minute Sustained Winds for Improved Analysis of TC Intensity and Structure
Holbach
Observations
search_activity
2023
Level 3
To produce a methodology for adjusting the various aircraft observations to statistically consistent 1-minute sustained winds, and to improve the interpretation of the various wind speed data obtained by instruments mounted on the NOAA WP-3D hurricane hunter aircraft.
Heather Holbach
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Advanced Weather Interactive Processing System (AWIPS)
This project will focus on improving the interpretation of the various wind speed data obtained by instruments mounted on the NOAA WP-3D hurricane hunter aircraft (flight-level, Stepped-Frequency Microwave Radiometer, Tail-Doppler Radar, and dropsondes). Specifically, this work will determine how the wind speeds measurements from each of these instruments can be adjusted to a 1-minute sustained wind that matches a standard. A method to average and adjust aircraft observations to consistent 1-minute sustained winds, as well as a new product to view the adjusted aircraft data in cloud Advanced Weather Interactive Processing System will be developed. The objectives of this project will allow forecasters to be able to compare the aircraft data when analyzing hurricane intensity and structure more accurately and forecast models will be able to utilize this new method by applying the adjustments to aircraft wind speed data being assimilated.
By developing a method to adjust all the aircraft wind observations to statistically consistent 1-minute sustained winds and developing a way to best implement our findings into cloud AWIPS for eventual transition to AWIPS II, forecasters at the NHC will be able to produce more internally consistent analyses of Tropical Cyclone (TC) intensity and structure. The averaging times identified for the flight-level winds and SFMR could also inform potential changes to the averaged winds reported in the High-Density Observations (HDOBs) that are defined in the National Hurricane Operations Plan (NHOP). The results from this project will also have an impact on forecast models as the methods for averaging and adjusting the aircraft wind observations to 1-min sustained winds could be implemented in the models to assimilate data that are statistically consistent, which would lead to more accurate model analyses and improved model forecasts. Improved analyses and forecasts of TC intensity and structure have numerous impacts on society as it allows forecasters to issue more accurate watches and warnings leading to a reduction of unnecessary evacuations, better guidance on potential impacts to improve preparedness by the public, and improved guidance on hazards at sea. All of these contribute to NOAA's mission of protecting life and property.
TBD
TBD
Florida State University
NA23OAR4590403
Tropical Cyclones
Florida
August 2023 - July 2026
Aircraft Reconnaissance
adjusting aircraft wind observations to 1 minute sustained winds for improved analysis of tc intensity and structure, to produce a methodology for adjusting the various aircraft observations to statistically consistent 1 minute sustained winds and to improve the interpretation of the various wind speed data obtained by instruments mounted on the noaa wp 3d hurricane hunter aircraft, this project will focus on improving the interpretation of the various wind speed data obtained by instruments mounted on the noaa wp 3d hurricane hunter aircraft flight level stepped frequency microwave radiometer tail doppler radar and dropsondes specifically this work will determine how the wind speeds measurements from each of these instruments can be adjusted to a 1 minute sustained wind that matches a standard a method to average and adjust aircraft observations to consistent 1 minute sustained winds as well as a new product to view the adjusted aircraft data in cloud advanced weather interactive processing system will be developed the objectives of this project will allow forecasters to be able to compare the aircraft data when analyzing hurricane intensity and structure more accurately and forecast models will be able to utilize this new method by applying the adjustments to aircraft wind speed data being assimilated
Florida State University
To produce a methodology for adjusting the various aircraft observations to statistically consistent 1-minute sustained winds, and to improve the interpretation of the various wind speed data obtained by instruments mounted on the NOAA WP-3D
0
Venturing into the Vertical: Optimizing Boundary Layer Profiling in Mesonets
Gebauer
Observations
search_activity
2023
Level 3
To facilitate the development of a vital boundary layer profiling mesonet that holds potential to fill the boundary layer data gap in current atmospheric observation networks, and to provide knowledge about optimal design of boundary layer profiling mesonets that will help to facilitate these networks.
Joshua Gebauer, Tyler Bell, Antonio Segales, Elizabeth Smith
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Observing System Simulation Experiments (OSSEs), Surface Observing Networks, Uncrewed Systems & Vehicles
Obs
This project will facilitate the development of a vital boundary layer profiling mesonet, i.e. 3D mesonet, that holds potential to fill the boundary layer data gap in current atmospheric observation networks. This project will perform observing system simulation experiments to study the optimal design of a 3D mesonet and will use the results of the observing system simulation experiments to design and test field deployments of existing boundary layer profiling instrumentation, i.e. CopterSonde uncrewed aerial system and Collaborative Lower Atmospheric Mobile Profiling System 2, to prototype a 3D mesonet during high-impact weather. The project objectives will provide insight into the optimal design for 3D mesonets to add to existing mesonets that could be of valuable use for forecasting/nowcasting and boundary layer research.
This project will provide insights about optimal design of 3D mesonets and value-added products that can be used when adding boundary-layer profiling capabilities to existing mesonets. The preparation for and use of the CopterSondes during the field deployments increase knowledge about applicability and requirements of weather-sensing UAS in mesonet configurations, which is crucial information as the weather community moves towards the adoption of these systems in formal networks.
TBD
TBD
CIWRO/University of Oklahoma
NA23OAR4590406
Severe Weather
Oklahoma
August 2023 - July 2026
Observing System Simulation Experiments (OSSEs), Surface Observing Networks, Uncrewed Systems & Vehicles
venturing into the vertical optimizing boundary layer profiling in mesonets, to facilitate the development of a vital boundary layer profiling mesonet that holds potential to fill the boundary layer data gap in current atmospheric observation networks and to provide knowledge about optimal design of boundary layer profiling mesonets that will help to facilitate these networks, this project will facilitate the development of a vital boundary layer profiling mesonet i e 3d mesonet that holds potential to fill the boundary layer data gap in current atmospheric observation networks this project will perform observing system simulation experiments to study the optimal design of a 3d mesonet and will use the results of the observing system simulation experiments to design and test field deployments of existing boundary layer profiling instrumentation i e coptersonde uncrewed aerial system and collaborative lower atmospheric mobile profiling system 2 to prototype a 3d mesonet during high impact weather the project objectives will provide insight into the optimal design for 3d mesonets to add to existing mesonets that could be of valuable use for forecasting nowcasting and boundary layer research
CIWRO/University of Oklahoma
To facilitate the development of a vital boundary layer profiling mesonet that holds potential to fill the boundary layer data gap in current atmospheric observation networks, and to provide knowledge about optimal design of boundary
2
Application of Machine Learning Techniques to the Automated Quality Control of
Airborne Doppler Radar Data Used in Operational Analysis of Hurricane Winds and Reflectivity
Fischer
Observations
search_activity
2023
Level 3
To develop a better, automated quality control procedure that is also more adaptable to current and future airborne radar systems that will deliver improved velocity and precipitation observations for model assimilation and forecaster displays
Michael Fischer, Michael Bell
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Advanced Weather Interactive Processing System (AWIPS), Global Forecast System (GFS), Hurricane Analysis and Forecast System (HAFS), Hurricane Weather Research and Forecasting (HWRF), Adva
This project aims to develop a better, automated quality control procedure that is also more adaptable to current and future airborne radar systems that will deliver improved velocity and precipitation observations for model assimilation and forecaster displays. The project will train the radar machine learning quality control methods to find the best approach to improve present NOAA Tail Doppler Radar data, and to find a method that can most rapidly be applied to new radar systems, in particular the Airborne Phased Array Radar that is the proposed radar to be mounted on the aircraft replacement of the NOAA WP-3D aircraft. The objectives of this proposal are vital as it is crucial that NOAA adapt quickly to avoid temporary loss of this important operational data set when new radar systems, such as the possible Airborne Phased Array Radar system, come online when the NOAA WP-3D are retired and replaced.
The end output of this research project will be QCed radials transmitted to EMC with greater coverage of meteorological data (MD), and less non-meteorological data (NMD), than currently provided. NHC will receive reflectivity and wind analyses with greater overall coverage at 0.5-km altitude and in the outflow layer. There will be no changes to transmitted file forms, so implementation of MLQC, in terms of the operational dataflow, will be transparent to both EMC and NHC, but they will receive improved products.
application of machine learning techniques to the automated quality control of airborne doppler radar data used in operational analysis of hurricane winds and reflectivity, to develop a better automated quality control procedure that is also more adaptable to current and future airborne radar systems that will deliver improved velocity and precipitation observations for model assimilation and forecaster displays, this project aims to develop a better automated quality control procedure that is also more adaptable to current and future airborne radar systems that will deliver improved velocity and precipitation observations for model assimilation and forecaster displays the project will train the radar machine learning quality control methods to find the best approach to improve present noaa tail doppler radar data and to find a method that can most rapidly be applied to new radar systems in particular the airborne phased array radar that is the proposed radar to be mounted on the aircraft replacement of the noaa wp 3d aircraft the objectives of this proposal are vital as it is crucial that noaa adapt quickly to avoid temporary loss of this important operational data set when new radar systems such as the possible airborne phased array radar system come online when the noaa wp 3d are retired and replaced
University of Miami/CIMAS
To develop a better, automated quality control procedure that is also more adaptable to current and future airborne radar systems that will deliver improved velocity and precipitation observations for model assimilation and forecaster displays
2
Pilot Upgrade of NOAA NDBC Coastal Weather Buoys for Improved Monitoring of Weather and Climate
Serra
Observations
search_activity
2023
Level 3
To provide a prototype operational NDBC CWB with additional subsurface and surface sensors to better characterize air-sea interaction and subsurface ocean heat content and mixed layer variability in the Gulf Stream extension region, and to provide two-years of real-time and post-processed data from one enhanced site along the NEUS continental shelf region.
Yolande Serra, Karen Grissom, Meghan Cronin
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
This project aims to design a prototype operational NOAA National Data Buoy Center Coastal Weather Buoy that measures collocated subsurface and surface variables in real-time and provides quality controlled data capable of characterizing air-sea interaction and subsurface ocean heat content and mixed layer variability in the Gulf Stream extension region. The project objectives include advancing the operational readiness of the Coastal Weather Buoy prototype that is anticipated to be adopted by the National Data Buoy Center to upgrade the current buoy network, real-time, post-processed quality controlled data, as well as the display and delivery, from the upgraded Coastal Weather Buoy, and published manuscripts outlining the prototype capabilities and importance. The measurements from the upgraded Coastal Weather Buoy will be useful for verifying satellite retrievals near the United States coastline and the next generation of coupled assimilative models, and for research to improve long-term forecasts that depend upon the ocean influence on weather and climate.
The NOAA National Data Buoy Center (NDBC) has fourteen Coastal Water Buoys (CWBs) along the NEUS coastline, and an additional nineteen CWBs along the Gulf of Mexico and southeast U.S. coastlines east to Bermuda. Enhancing NDBC CWBs leverages these existing NOAA observational platforms to better support advancements in modeling and real-time weather forecasting practices. The prototype all-in-one CWB design resulting from this project permits NDBC to deploy full air-sea flux and subsurface observations where they are most needed. The benefits of these added capabilities are primarily to the meteorological and climate communities, by adding both real-time data for improved short term weather and ocean forecasting and situational awareness, as well as by providing high-quality post-processed data for monitoring the ocean state, improving coupled data assimilation techniques, and supporting atmosphere-ocean research of the Gulf Stream frontal region.
pilot upgrade of noaa ndbc coastal weather buoys for improved monitoring of weather and climate, to provide a prototype operational ndbc cwb with additional subsurface and surface sensors to better characterize air sea interaction and subsurface ocean heat content and mixed layer variability in the gulf stream extension region and to provide two years of real time and post processed data from one enhanced site along the neus continental shelf region, this project aims to design a prototype operational noaa national data buoy center coastal weather buoy that measures collocated subsurface and surface variables in real time and provides quality controlled data capable of characterizing air sea interaction and subsurface ocean heat content and mixed layer variability in the gulf stream extension region the project objectives include advancing the operational readiness of the coastal weather buoy prototype that is anticipated to be adopted by the national data buoy center to upgrade the current buoy network real time post processed quality controlled data as well as the display and delivery from the upgraded coastal weather buoy and published manuscripts outlining the prototype capabilities and importance the measurements from the upgraded coastal weather buoy will be useful for verifying satellite retrievals near the united states coastline and the next generation of coupled assimilative models and for research to improve long term forecasts that depend upon the ocean influence on weather and climate
CICOES/University of Washington
To provide a prototype operational NDBC CWB with additional subsurface and surface sensors to better characterize air-sea interaction and subsurface ocean heat content and mixed layer variability in the Gulf Stream extension region, and to
0
Building a Comprehensive Capability in Operational HAFS to Assimilate All Available Tropical Cyclone Inner-Core Observations
Aksoy
Observations
search_activity
2023
Level 3
To develop a comprehensive capability to acquire, preprocess, quality control, and assimilate all tropical cyclone inner-core aircraft observations in the NOAA Hurricane Analysis and Forecasting System.
Altug Aksoy
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Dec 31 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Aircraft Reconnaissance, Data Assimilation and Ensembles, Preprocessing, Radar, Uncrewed Systems & Vehicles
Obs
Hurricane Analysis and Forecast System (HAFS)
This project aims to develop a comprehensive capability to acquire,
preprocess, quality control, and assimilate all tropical cyclone inner-core aircraft observations in the NOAA Hurricane Analysis and Forecasting System. The objectives of this project include improving existing preprocessing, quality control, and thinning/superobbing capabilities to ultimately improve the NOAA Hurricane Analysis and Forecasting System tropical cyclone inner-core data assimilation capabilities and working to add a new capacity to NOAA Hurricane Analysis and Forecasting System, which includes preliminary work on quality control and assimilation of data from newer tropical cyclone observing systems, e.g. Tail Doppler Radar reflectivity, land-based non-United States radar, Imaging Wind and Rain Airborne Profiler, and small Uncrewed Aircraft System observations. This work is critical as the current NOAA operational model Hurricane Weather and Research Forecast system will be replaced with the NOAA Hurricane Analysis and Forecasting System in 2023, so it is vital to ensure that as much of the tropical cyclone inner-core observations are assimilated in NOAA Hurricane Analysis and Forecasting System as possible, and in the most optimal configuration.
The primary impact of this project will be on Hyrricane Analysis and Forecast System(HAFS)- Data Assimilation (DA) and its capability to assimilate all Tropical Cyclone (TC) inner-core observations in real time. This will include improving existing capabilities as well as adding new capabilities that are currently not available but expected to be critical for the HAFS- DA system to provide the best possible high-resolution analyses in the TC inner core for improved TC forecasts. The impact of these additional capabilities will be demonstrated through a thorough investigation of analysis and forecast performance both in observation and model space. All of these tools are already available to us to carry out such investigations. The ultimate recipient of the project products will be the NOAA EMC.
TBD
TBD
CIMAS/University of Miami
NA23OAR4590407
Tropical Cyclones
Florida
August 2023 - December 2025
Aircraft Reconnaissance, Data Assimilation and Ensembles, Preprocessing, Radar, Uncrewed Systems & Vehicles
building a comprehensive capability in operational hafs to assimilate all available tropical cyclone inner core observations, to develop a comprehensive capability to acquire preprocess quality control and assimilate all tropical cyclone inner core aircraft observations in the noaa hurricane analysis and forecasting system, this project aims to develop a comprehensive capability to acquire preprocess quality control and assimilate all tropical cyclone inner core aircraft observations in the noaa hurricane analysis and forecasting system the objectives of this project include improving existing preprocessing quality control and thinning superobbing capabilities to ultimately improve the noaa hurricane analysis and forecasting system tropical cyclone inner core data assimilation capabilities and working to add a new capacity to noaa hurricane analysis and forecasting system which includes preliminary work on quality control and assimilation of data from newer tropical cyclone observing systems e g tail doppler radar reflectivity land based non united states radar imaging wind and rain airborne profiler and small uncrewed aircraft system observations this work is critical as the current noaa operational model hurricane weather and research forecast system will be replaced with the noaa hurricane analysis and forecasting system in 2023 so it is vital to ensure that as much of the tropical cyclone inner core observations are assimilated in noaa hurricane analysis and forecasting system as possible and in the most optimal configuration
CIMAS/University of Miami
To develop a comprehensive capability to acquire, preprocess, quality control, and assimilate all tropical cyclone inner-core aircraft observations in the NOAA Hurricane Analysis and Forecasting System.
0
Real-time Visualization and Interpretation of the Hurricane Wind Structure and Intensity: From Flight Level to the Surface
Jelenak
Observations
search_activity
2023
Level 3
To address the challenges operational forecasters face that arise when interpreting tropical cyclone observations collected from the various sensors mounted on the NOAA WP-3D aircraft in the time constrained decision making process, and to create products and tools that will be directly utilized by NHC and EMC, including the generation of HAFS analysis and forecast output data that will be post-processed before archiving.
Zorana Jelenak, Heather Holbach, Altug Aksoy
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Data Assimilation and Ensembles, Dynamics and Nesting, Radar, Satellite & Remote Sensing Technologies, Visualizations, Decision-Making
Obs
Advanced Weather Interactive Processing System (AWIPS)
This project aims to address the challenges operational forecasters face that arise when interpreting tropical cyclone observations collected from the various sensors mounted on the NOAA WP-3D aircraft in the time constrained decision making process. The project team will utilize the Imaging Wind and Rain Airborne Profiler 3D wind and reflectivity measurements together with the Flight level, Stepped-Frequency Microwave Radiometer, Tail Doppler Radar, and Global Positioning System dropsonde wind observations to to improve estimates of storm structure and intensity by 1) providing near real time tools to assess the differences, uncertainties and quality of data between sensors 2) providing new rain estimates and 3) quantify the impact of the unique fine resolution Imaging Wind and Rain Airborne Profiler wind and reflectivity 3-dimensional profiles within both the NOAA National Hurricane Center operational environment and within NOAA Environmental Modeling Center data assimilation systems. This work is critical as it will not only directly benefit operational forecaster data interpretation and usage, but it will also provide understanding of the impact of the new observational data on NOAA forecast models and data assimilation systems.
A multitude of sensors provide a variety of observations within the tropical cyclone environment from the NOAA P-3 aircraft, but the interpretation of all of these measurements in the time constrained decision making process of an operational forecaster can be challenging. This research seeks to address these challenges by utilizing the Imaging Wind and Rain Airborne Profiler (IWRAP) 3D wind and reflectivity measurements together with the Flight level (FL), Stepped-Frequency Microwave Radiometer (SFMR), tail Doppler Radar (TDR) and Global Positioning System (GPS) dropsonde wind observations to improve available data.
TBD
TBD
University Corporation for Atmospheric Reseraech
Florida State University
CIMAS/University of Miami
NA23OAR4590412, NA23OAR4590402, NA23OAR4590400
Tropical Cyclones
Colorado, Florida
August 2023 - July 2025
Aircraft Reconnaissance, Data Assimilation and Ensembles, Dynamics and Nesting, Radar, Satellite & Remote Sensing Technologies, Visualizations, Decision-Making
real time visualization and interpretation of the hurricane wind structure and intensity from flight level to the surface, to address the challenges operational forecasters face that arise when interpreting tropical cyclone observations collected from the various sensors mounted on the noaa wp 3d aircraft in the time constrained decision making process and to create products and tools that will be directly utilized by nhc and emc including the generation of hafs analysis and forecast output data that will be post processed before archiving, this project aims to address the challenges operational forecasters face that arise when interpreting tropical cyclone observations collected from the various sensors mounted on the noaa wp 3d aircraft in the time constrained decision making process the project team will utilize the imaging wind and rain airborne profiler 3d wind and reflectivity measurements together with the flight level stepped frequency microwave radiometer tail doppler radar and global positioning system dropsonde wind observations to to improve estimates of storm structure and intensity by 1 providing near real time tools to assess the differences uncertainties and quality of data between sensors 2 providing new rain estimates and 3 quantify the impact of the unique fine resolution imaging wind and rain airborne profiler wind and reflectivity 3 dimensional profiles within both the noaa national hurricane center operational environment and within noaa environmental modeling center data assimilation systems this work is critical as it will not only directly benefit operational forecaster data interpretation and usage but it will also provide understanding of the impact of the new observational data on noaa forecast models and data assimilation systems
University Corporation for Atmospheric Reseraech, Florida State University, CIMAS/University of Miami
To address the challenges operational forecasters face that arise when interpreting tropical cyclone observations collected from the various sensors mounted on the NOAA WP-3D aircraft in the time constrained decision making process, and to create
1
Evaluating and Integrating Black Globe Temperature Observations into Operational WBGT Forecasts
Glotfelty
Observations
search_activity
2023
Level 3
To illustrate the added value of deploying black globe temperature sensors to strengthen NWS heat forecasting tools.
Timothy Glotfelty, Kathie D. Dello
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Surface Observing Networks
Obs
National Mesonet Program (NMP)
This project aims to improve the regional National Weather Service forecasts and National Digital Forecast Database wet bulb globe temperature forecasts to ultimately enhance extreme heat risk forecasts across the United States. The project objective include leveraging existing state mesonet resources, e.g. North Carolina Environment and Climate Observing Network, to evaluate wet bulb globe temperature forecast products against observations for systemic biases and to develop an integrated wet bulb globe temperature forecast guidance prototype for National Weather Service operations. The outcomes and products from this work will improve heat risk forecasts for existing end users and new end users.
Extreme heat events and associated heat-related illness and death are an emerging issue nationally and in the Southeast. To advance forecasting for extreme heat events, the National Weather Service (NWS) weather forecast offices (WFOs) in North Carolina and beyond are considering adding wet-bulb globe temperature (WBGT) as a key metric for extreme heat forecast guidance. WBGT evaluations and analysis using ECONet observations across North Carolina for this project will provide further knowledge to forecasters and benefit extreme heat forecast operations directly.
TBD
TBD
North Carolina State University (NC State)
NA23OAR4590410
Extreme Temperatures
North Carolina
August 2023 - July 2026
Model & Forecast Guidance, Surface Observing Networks
evaluating and integrating black globe temperature observations into operational wbgt forecasts, to illustrate the added value of deploying black globe temperature sensors to strengthen nws heat forecasting tools, this project aims to improve the regional national weather service forecasts and national digital forecast database wet bulb globe temperature forecasts to ultimately enhance extreme heat risk forecasts across the united states the project objective include leveraging existing state mesonet resources e g north carolina environment and climate observing network to evaluate wet bulb globe temperature forecast products against observations for systemic biases and to develop an integrated wet bulb globe temperature forecast guidance prototype for national weather service operations the outcomes and products from this work will improve heat risk forecasts for existing end users and new end users
North Carolina State University (NC State)
To illustrate the added value of deploying black globe temperature sensors to strengthen NWS heat forecasting tools.
0
Development of Real-Time Multistatic Passive Radar Networks for Severe Weather Prediction
Skinner
Observations
search_activity
2023
Level 3
To demonstrate improved capabilities for detecting and issuing warnings to protect life and property from severe weather using a low cost method, utilizing innovative multistatic technology.
Patrick Skinner
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Advanced Weather Interactive Processing System (AWIPS)
This project aims to leverage recent technological advances for producing low-cost passive bistatic radar receivers to deploy two 5-receiver, multistatic radar networks with the Norman and Tulsa Weather Surveillance Radar-1988 Doppler sites in Oklahoma. The two demonstration networks will enable observation and retrieval of the 3D wind field in convective storms over a broad region, greatly expanding the observing capabilities of the Weather Surveillance Radar-1988 Doppler sites. The real-time observations from these demonstration networks will be disseminated to National Weather Service operational forecasters to allow research-to-operations-to-research feedbacks to both optimize visualization and delivery of 3D wind retrievals and identify initial best practices for using multistatic retrievals in National Weather Service operations. The proposed research will demonstrate improved capabilities for detecting and issuing warnings to protect life and property from severe weather using a low cost method.
Observations from the WSR-88D network are the principal tool for the detection and short-term prediction of convective storm hazards, such as extreme rainfall, severe hail, damaging straight-line winds, and tornadoes in the United States. However, WSR-88D observations are limited by an inability to observe the full three-dimensional (3D) wind field in thunderstorms. This project will demonstrate improved capabilities for detecting and issuing Warnings to protect life and property from severe weather using a low cost method that dramatically expands the WSR-88D network's observational capacity.
TBD
TBD
CIWRO/University of Oklahoma
NA23OAR4590398
Severe Weather
Oklahoma
August 2023 - July 2026
Radar, Visualizations
development of real time multistatic passive radar networks for severe weather prediction, to demonstrate improved capabilities for detecting and issuing warnings to protect life and property from severe weather using a low cost method utilizing innovative multistatic technology, this project aims to leverage recent technological advances for producing low cost passive bistatic radar receivers to deploy two 5 receiver multistatic radar networks with the norman and tulsa weather surveillance radar 1988 doppler sites in oklahoma the two demonstration networks will enable observation and retrieval of the 3d wind field in convective storms over a broad region greatly expanding the observing capabilities of the weather surveillance radar 1988 doppler sites the real time observations from these demonstration networks will be disseminated to national weather service operational forecasters to allow research to operations to research feedbacks to both optimize visualization and delivery of 3d wind retrievals and identify initial best practices for using multistatic retrievals in national weather service operations the proposed research will demonstrate improved capabilities for detecting and issuing warnings to protect life and property from severe weather using a low cost method
CIWRO/University of Oklahoma
To demonstrate improved capabilities for detecting and issuing warnings to protect life and property from severe weather using a low cost method, utilizing innovative multistatic technology.
2
Assessment of the impact of New York State Mesonet profiler network and new profiling instruments on the skill of high impact weather predictions in New York State
Weckwerth
Observations
search_activity
2023
Level 3
To evaluate how the current NYSM impacts the skill of high impact weather prediction and to explore the potential benefits of adding new profiling capabilities and/or new stations to the existing NYSM profiler network.
Tammy Weckwerth, Junhong Wang
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Observing System Simulation Experiments (OSSEs), Surface Observing Networks
Obs
National Mesonet Program (NMP), Rapid Refresh Forecast System (RRFS)
This project will demonstrate the potential of a nationwide network of profiling systems using the New York State Mesonet as a testbed. The objectives of this work are to conduct a series of Observing System Experiments and Observing System Simulation Experiments to evaluate how the current New York State Mesonet impacts the skill of high impact weather prediction and to explore the potential benefits of adding new profiling capabilities (e.g., MicroPulse Differential absorption lidar and Uncrewed Aircraft Systems) and/or new stations to the existing New York State Mesonet profiler network. This project is vital as it will demonstrate a possible solution to fill critical gaps in observational capabilities, specifically for the lower atmosphere, for high impact weather forecasting, which will benefit operational forecasters, as well as data assimilation for the forecast models.
One of the critical gaps in observational capabilities for high impact weather forecasting is the lack of observations in the lower atmosphere. Vertical profiles of thermodynamic and kinematic states in the lower atmosphere are critical for understanding and predicting atmospheric processes within and above the planetary boundary layer (PBL). This project will assess the impact of real NYSM profiler data, as well as simulated MPD and UAS data and simulated data from additional NYSM profiler sites, through OSEs and OSSEs.
TBD
TBD
National Center for Atmospheric Research
State University of New York University at Albany
NA23OAR4590399, NA23OAR4590402
Severe Weather
Colorado, New York
August 2023 - July 2026
Data Assimilation and Ensembles, Observing System Simulation Experiments (OSSEs), Surface Observing Networks
assessment of the impact of new york state mesonet profiler network and new profiling instruments on the skill of high impact weather predictions in new york state, to evaluate how the current nysm impacts the skill of high impact weather prediction and to explore the potential benefits of adding new profiling capabilities and or new stations to the existing nysm profiler network, this project will demonstrate the potential of a nationwide network of profiling systems using the new york state mesonet as a testbed the objectives of this work are to conduct a series of observing system experiments and observing system simulation experiments to evaluate how the current new york state mesonet impacts the skill of high impact weather prediction and to explore the potential benefits of adding new profiling capabilities e g micropulse differential absorption lidar and uncrewed aircraft systems and or new stations to the existing new york state mesonet profiler network this project is vital as it will demonstrate a possible solution to fill critical gaps in observational capabilities specifically for the lower atmosphere for high impact weather forecasting which will benefit operational forecasters as well as data assimilation for the forecast models
National Center for Atmospheric Research, State University of New York University at Albany
To evaluate how the current NYSM impacts the skill of high impact weather prediction and to explore the potential benefits of adding new profiling capabilities and/or new stations to the existing NYSM profiler network.
6
Maturation of a Standalone Aircraft Derived Atmospheric Observation System for Improved Operational Forecasting
McPartland
Observations
search_activity
2023
Level 3
To develop and demonstrate a low-cost, standalone Mode S EHS aircraft derived atmospheric observation system for enhanced weather forecasting and improved and expanded Boundary Layer Observations.
Michael McPartland, Jason English
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Aircraft Reconnaissance, Data Assimilation and Ensembles
Obs
High-Resolution Rapid Refresh (HRRR), Rapid Refresh Forecast System (RRFS)
This project will build upon the accomplishments of a previously NOAA-funded effort to develop and demonstrate a low-cost, standalone Mode S Enhanced Surveillance aircraft derived atmospheric observation system for enhanced weather forecasting and improved and expanded boundary layer observations. This award will continue to support all systems deployed in the field, develop and test advanced antennas to be mounted on new systems that will be deployed in the field, and build a valuable observation data set. The project objectives will advance the capabilities of the observation technology and meet operational needs, including the operational needs necessary to meet NOAA's mission.
Aircraft derived observations (ADOs) of atmospheric measurements of parameters such as wind speed/direction and temperature from aircraft provide the highest value inputs to Numerical Weather Prediction (NWP) models. These aircraft data are significantly more beneficial than those from ground-based weather stations, radiosondes, or satellites. Better representation of the initial conditions using the dense ADOs collected from these prototype systems in this project are expected to provide improvements to atmospheric structure in NWP analyses, and yield more accurate wind, temperature, and convective storm forecasts. This technology could lead to a network of systems to collect and distribute data and provide improvements to NWP models. Model improvements would be useful to WFOs located near PADWOS units, and possibly many WFOs at the national level if sufficient systems are deployed. Improved forecast accuracy will benefit users that rely on the forecasts, including the aviation industry.
NWS Office of Observations
Curtis Marshall
Massachusetts Institute of Technology Lincoln Laboratory
CIRES/University of Colorado Boulder
NA23OAR4590397, NA23OAR4590405
Severe Weather
Colorado, Massachusetts
August 2023 - July 2026
Aircraft Reconnaissance, Data Assimilation and Ensembles
maturation of a standalone aircraft derived atmospheric observation system for improved operational forecasting, to develop and demonstrate a low cost standalone mode s ehs aircraft derived atmospheric observation system for enhanced weather forecasting and improved and expanded boundary layer observations, this project will build upon the accomplishments of a previously noaa funded effort to develop and demonstrate a low cost standalone mode s enhanced surveillance aircraft derived atmospheric observation system for enhanced weather forecasting and improved and expanded boundary layer observations this award will continue to support all systems deployed in the field develop and test advanced antennas to be mounted on new systems that will be deployed in the field and build a valuable observation data set the project objectives will advance the capabilities of the observation technology and meet operational needs including the operational needs necessary to meet noaa's mission
Massachusetts Institute of Technology Lincoln Laboratory, CIRES/University of Colorado Boulder
To develop and demonstrate a low-cost, standalone Mode S EHS aircraft derived atmospheric observation system for enhanced weather forecasting and improved and expanded Boundary Layer Observations.
0
Anonymization, Bias Correction, and Assimilation of Smartphone Pressure Observations for Use in Numerical Weather Prediction in NOAA
Weygandt
Observations
search_activity
2023
Level 3
The goal of this project is to bring bias corrected, anonymized smartphone pressure data into NOAA operations and research. Specifically, this project will evaluate the use of voluminous smartphone pressure data for the initialization of NOAA numerical weather prediction models and for application in NOAA and other atmospheric research.
Stephen Weygandt, Greg Pratt
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Verification & Validation
Obs
This project will utilize pressure sensors within smartphones and collect smartphone pressure data to bring into the operational environment. The team will assess the impact of the large number of smartphone pressure measurements on operational forecasting and numerical weather prediction.
Pressure data is a valuable surface parameter and is crucial for mesoscale weather prediction, one of the key challenges facing NOAA. With billions of smartphones now capable of measuring atmospheric pressure, the utilization of this bias corrected, anonymized smartphone pressure data into NOAA operations and research will provide the ability to define important mesoscale weather features in regions of sparse operational observations.
TBD
NOAA/OAR/GSL
Relevant to All Hazards
Colorado
August 2023 - July 2024
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Verification & Validation
anonymization bias correction and assimilation of smartphone pressure observations for use in numerical weather prediction in noaa, the goal of this project is to bring bias corrected anonymized smartphone pressure data into noaa operations and research specifically this project will evaluate the use of voluminous smartphone pressure data for the initialization of noaa numerical weather prediction models and for application in noaa and other atmospheric research, this project will utilize pressure sensors within smartphones and collect smartphone pressure data to bring into the operational environment the team will assess the impact of the large number of smartphone pressure measurements on operational forecasting and numerical weather prediction
NOAA/OAR/GSL
The goal of this project is to bring bias corrected, anonymized smartphone pressure data into NOAA operations and research. Specifically, this project will evaluate the use of voluminous smartphone pressure data for the initialization of
0
Lake Tahoe Basin Instrumentation and Data Sharing
Albano
Observations
search_activity
2023
Level 3
To create a transparent and accessible set of tools to better utilize data and insights coming from ongoing scientific and management efforts to protect Lake Tahoe.
Christine Albano, Sundeep Chandra, Dan McEvoy, Chris Pearson, Rosemary Carroll, Adrian Harpold, Scott Allen, Joanna Blaszczak
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Water in the West
Earmark
The Desert Research Institute (DRI) will assist with the creation of a transparent and accessible set of tools to better utilize data and insights coming from ongoing scientific and management efforts to protect Lake Tahoe. With building tools for improved and sustained integration of a collection of Lake Tahoe Basin studies through an accessible data platform, a well-instrumented watershed and analysis of remotely sensed data, the DRI will utilize these tools to enable improved understanding of local watershed-scale processes as they are impacted by basin-scale forcing, such as wildfires and sustained drought.
This project will build the tools for improved and sustained integration of these data through an accessible data platform, a well-instrumented watershed and analysis of remotely sensed data to enable scaling of watershed processes to the full basin. These tools will enable improved understanding of local watershed-scale processes as they are impacted by basin-scale forcing (e.g., wildfires, sustained drought). This project seeks to create a transparent and accessible set of tools to better utilize data and insights coming from ongoing scientific and management efforts to protect Lake Tahoe.
TBD
TBD
Desert Research Institute (DRI)
University of Nevada Reno (UNR)
NA23OAR4690428
Water Extremes
Nevada
September 2023 - August 2025
Water in the West
lake tahoe basin instrumentation and data sharing, to create a transparent and accessible set of tools to better utilize data and insights coming from ongoing scientific and management efforts to protect lake tahoe, the desert research institute dri will assist with the creation of a transparent and accessible set of tools to better utilize data and insights coming from ongoing scientific and management efforts to protect lake tahoe with building tools for improved and sustained integration of a collection of lake tahoe basin studies through an accessible data platform a well instrumented watershed and analysis of remotely sensed data the dri will utilize these tools to enable improved understanding of local watershed scale processes as they are impacted by basin scale forcing such as wildfires and sustained drought
Desert Research Institute (DRI), University of Nevada Reno (UNR)
To create a transparent and accessible set of tools to better utilize data and insights coming from ongoing scientific and management efforts to protect Lake Tahoe.
0
An Analysis of Opportunities for Weather Enterprise Collaboration to Deliver Improved Weather Radar Information to the American Public
Kurdzo
Observations
search_activity
2024
Level 3
To provide knowledge and data for potential improved weather radar information opportunities for the American public, underserved communities, and both the federal and commercial sectors of the weather enterprise.
This project will analyze potential opportunities for federal and commercial collaboration to provide improved weather radar information to the American public.
This project aims to provide Information for the weather enterprise on opportunities for improving the national weather radar network through the potential use of gap-filling radars and siting of future radars.
NWS ROC
Jessica Schulz
MIT Lincoln Laboratory
NA24OARX459C0009-T1-01
Severe Weather
Massachusetts
April 2024 - September 2025
Radar, Social Vulnerability
an analysis of opportunities for weather enterprise collaboration to deliver improved weather radar information to the american public, to provide knowledge and data for potential improved weather radar information opportunities for the american public underserved communities and both the federal and commercial sectors of the weather enterprise, this project will analyze potential opportunities for federal and commercial collaboration to provide improved weather radar information to the american public
MIT Lincoln Laboratory
To provide knowledge and data for potential improved weather radar information opportunities for the American public, underserved communities, and both the federal and commercial sectors of the weather enterprise.
0
A Unified Alabama Mesonet for Climate-, Fire-, and Weather-Related Decision Support
Ellenburg
Observations
search_activity
2024
Level 3
To produce a unified mesonet and improve the data network to improve forecasts and save lives.
Tue Jun 30 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Surface Observing Networks, Decision Support
Obs
This project aims to investigate the development and test application of a "network of networks" with sensors and observations that, for the most part, already exist, and to produce the foundation of a Unified Mesonet, which when fully implemented will utilize innovate hydroinformatics approaches to provide a ground-truthed data stream for research studies to investigate flash flood and flash drought events, and to support broader drought monitoring and modeling applications, with a focus in Alabama. The outcome of the research will be tested approaches to overcome challenges to integrate 6 independent existing networks in Alabama (a total of 96 stations) into a coordinated, streamlined, and open-source database. The potential of the database will be the ability to provide robust coverage across the state and enable new dataflows for decision-making.
This research will investigate the integration of Alabama's hydrometeorological observations into a mesonet, and in the process also discover new dataflows for research and decision-making. An improved data network will improve forecasts and save lives.
TBD
TBD
CIROH/University of Alabama in Huntsville
NA22NWS4320003-T3-01S090
Relevant to All Hazards
Alabama
July 2024 - June 2026
Surface Observing Networks, Decision Support
a unified alabama mesonet for climate fire and weather related decision support, to produce a unified mesonet and improve the data network to improve forecasts and save lives, this project aims to investigate the development and test application of a "network of networks" with sensors and observations that for the most part already exist and to produce the foundation of a unified mesonet which when fully implemented will utilize innovate hydroinformatics approaches to provide a ground truthed data stream for research studies to investigate flash flood and flash drought events and to support broader drought monitoring and modeling applications with a focus in alabama the outcome of the research will be tested approaches to overcome challenges to integrate 6 independent existing networks in alabama a total of 96 stations into a coordinated streamlined and open source database the potential of the database will be the ability to provide robust coverage across the state and enable new dataflows for decision making
CIROH/University of Alabama in Huntsville
To produce a unified mesonet and improve the data network to improve forecasts and save lives.
0
Calibration and Model Application of Crowdsourced Surface Pressure Observations
Mass
Observations
search_activity
2024
Level 3
To improve NOAA RRFS forecasts, expanding the data for surface pressure assimiliation to NOAA.
Mon Jun 30 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Obs
Rapid Refresh Forecast System (RRFS)
This project aims to expanded testing of crowdsourced pressure data in the NOAA RRFS system and the solution of any additional problems, including the testing for morecases and regions. It will include the evaluation of the impacts of all three currently acquired crowdsourced data sources of RRFS forecasts. If the crowdsourced data are of benefit for the test cases, the effort will turn to operationalizing the transferring of the data acquisition and calibration effort, and work to expand the sources of crowdsourced pressure observations toother networks and sources.
This research aims to alleviate the problem of insufficient weather obseravations at the surface and in the lower troposphere for the high-resolution initialization necessary for skillful mesoscale numerical weather predicition for features such as convective outflow boundaries, gust fronts, and col pools, or terrain-related features such as downslope windstorms.
NWS Obs
Curtis Marshall
CICOES/University of Washington
NA20OAR4320271-T3-01S126
Severe Weather
Washington
July 2024 - June 2025
calibration and model application of crowdsourced surface pressure observations, to improve noaa rrfs forecasts expanding the data for surface pressure assimiliation to noaa, this project aims to expanded testing of crowdsourced pressure data in the noaa rrfs system and the solution of any additional problems including the testing for morecases and regions it will include the evaluation of the impacts of all three currently acquired crowdsourced data sources of rrfs forecasts if the crowdsourced data are of benefit for the test cases the effort will turn to operationalizing the transferring of the data acquisition and calibration effort and work to expand the sources of crowdsourced pressure observations toother networks and sources
CICOES/University of Washington
To improve NOAA RRFS forecasts, expanding the data for surface pressure assimiliation to NOAA.
0
Global GSB-Deployed Dropsondes and Mesh Networking
Hutchinson
Observations
search_activity
2024
Level 3
To develop the ability for Global Sounding Balloons to remotely deploy dropsondes while in flight to enable the collection of magnitudes more ins-situ data in areas of environmental interest.
Todd Hutchinson
Active
Sun Sep 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors
Obs
This project aims to develop the ability for Global Sounding Balloons (GSBs) to remotely deploy as many as 10 lightweight dropsondes while in flight. A GSB is a long-duration sensor-carrying balloon that collects and transmits real-time weather data throughout weeks-long flights. These dropsondes will carry a custom-built meteorological sensor suite similar to that which our GSBs are outfitted with and will communicate in-situ data back to their parent GSB in real-time via a closed radio network.
A key component of improving weather prediction is a dramatic increase in available data. By equipping the WindBorne GSBs with as many as ten remotely deployed dropsondes per unit, this will enable NOAA and other leading industry partners to collect magnitudes more data in the most unreachable and high-risk environments without any additional risk to lives or property. This capability could mean hundreds of additional soundings of tropical cyclones, for example, and in turnmore accurate predictions for cyclone behavior. Importantly, the WindBorne GSB is relatively low cost, especially when compared to manned aircraft, and GSB dropsondes will also adhere to a sustainable cost model.
NWS Obs
Jordan Gerth
Windborne Systems Inc.
NA24OARX459C0008-T1-01
Relevant to All Hazards
California
September 2024 - August 2025
In-situ Observation Technologies and Sensors
global gsb deployed dropsondes and mesh networking, to develop the ability for global sounding balloons to remotely deploy dropsondes while in flight to enable the collection of magnitudes more ins situ data in areas of environmental interest, this project aims to develop the ability for global sounding balloons gsbs to remotely deploy as many as 10 lightweight dropsondes while in flight a gsb is a long duration sensor carrying balloon that collects and transmits real time weather data throughout weeks long flights these dropsondes will carry a custom built meteorological sensor suite similar to that which our gsbs are outfitted with and will communicate in situ data back to their parent gsb in real time via a closed radio network
Windborne Systems Inc.
To develop the ability for Global Sounding Balloons to remotely deploy dropsondes while in flight to enable the collection of magnitudes more ins-situ data in areas of environmental interest.
0
Demonstrate and Expand a 21st Century Radar, Modeling and Decision Support System to Improve Environmental Equity and Resilience to Extreme Precipitation in a Mountainous, West-Coast, Urban Area: Phase 2 of the Advanced Quantitative Precipitation Information (AQPI) System
Ralph
Observations
search_activity
2024
Level 3
To demonstrate the value of gap-filling radars in providing better observations and forecasts across a major urban area embedded with complex topography, of which there are many across the U.S.
Mon Jun 30 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Radar, Community Preparedness & Resilience, Decision Support
Obs
This project will apply and advance the recently established AQPI system in the San Francisco Bay Area and demonstrate how the AQPI network of 5 gap-filling radars can be integrated with high-resolution modeling and novel decision support tools to increase resilience to extreme precipitation and related hazards across the San Francisco Bay Area. This project will address gaps in current meteorological observations that are characteristic of west-coast urban areas to improve the prediction of extreme precipitation in these areas.
This project will demonstrate how the Advanced Quantitative Precipitation Information (AQPI) network of 5 gap-filling radars can be integrated with high-resolution modeling and novel decision support tools to increase resilience to extreme precipitation and related hazards across the San Francisco Bay Area. The project will partner with local, regional, and national NWS offices, as well as local entities, to pave the way for longer-term implementation where warranted.
TBD
TBD
CIMEAS/Scripps Institute of Oceanography
NA20OAR4320278-T3-01S063
Water Extremes
California
July 2024 - June 2025
Dynamics and Nesting, Radar, Community Preparedness & Resilience, Decision Support
demonstrate and expand a 21st century radar modeling and decision support system to improve environmental equity and resilience to extreme precipitation in a mountainous west coast urban area phase 2 of the advanced quantitative precipitation information aqpi system, to demonstrate the value of gap filling radars in providing better observations and forecasts across a major urban area embedded with complex topography of which there are many across the u s, this project will apply and advance the recently established aqpi system in the san francisco bay area and demonstrate how the aqpi network of 5 gap filling radars can be integrated with high resolution modeling and novel decision support tools to increase resilience to extreme precipitation and related hazards across the san francisco bay area this project will address gaps in current meteorological observations that are characteristic of west coast urban areas to improve the prediction of extreme precipitation in these areas
CIMEAS/Scripps Institute of Oceanography
To demonstrate the value of gap-filling radars in providing better observations and forecasts across a major urban area embedded with complex topography, of which there are many across the U.S.
0
Radar Engineering, Artificial Intelligence for Numerical Weather Prediction, and Socio-Economic Research
Chandrasekar
Observations
search_activity
2024
Level 3
To increase resilience to extreme precipitation and related hazards across the San Francisco Bay Area by developing an AQPI network of 5 gap-filling radars.
This project addresses gaps in current meteorological observations, especially radar, that are characteristic of west-coast urban areas, and hence improves the prediction of extreme precipitation in these areas, reducing the underlying environmental justice inequities created by the observational gaps. To do so, this project will apply and advance the recently established AQPI system in the San Francisco Bay Area - a densely urban area embedded within complex and mountainous topography that faces increasing risk from hydrometeorological geohazards such as post-wildfire debris flows. This project will contribute to the further development of the AQPI network of 5 gap-filling radars can be integrated with support tools to increase resilience to extreme precipitation and related hazards across the San Francisco Bay Area
This project will contribute to the further development of the AQPI network of 5 gap-filling radars can be integrated with support tools to increase resilience to extreme precipitation and related hazards across the San Francisco Bay Area. The project will partner with local, regional, and national NWS offices, as well as local entities.
radar engineering artificial intelligence for numerical weather prediction and socio economic research, to increase resilience to extreme precipitation and related hazards across the san francisco bay area by developing an aqpi network of 5 gap filling radars, this project addresses gaps in current meteorological observations especially radar that are characteristic of west coast urban areas and hence improves the prediction of extreme precipitation in these areas reducing the underlying environmental justice inequities created by the observational gaps to do so this project will apply and advance the recently established aqpi system in the san francisco bay area - a densely urban area embedded within complex and mountainous topography that faces increasing risk from hydrometeorological geohazards such as post wildfire debris flows this project will contribute to the further development of the aqpi network of 5 gap filling radars can be integrated with support tools to increase resilience to extreme precipitation and related hazards across the san francisco bay area
CIRA/Colorado State University
To increase resilience to extreme precipitation and related hazards across the San Francisco Bay Area by developing an AQPI network of 5 gap-filling radars.
0
Creating Digital Infrastructure to Expand the Healthy Air Network
Hudson
Observations
search_activity
2024
Level 3
To improve and expand the reach of life-saving air quality information across Massachusetts.
Sarita Hudson
Active
Sun Sep 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon Aug 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry
Obs
This community driven project aims to expand the reach of life-saving air quality information across Massachusetts through the development of an app, website upgrades, and the addition of 20 air sensors through the Healthy Air Network. Updates to the website and developing the app will allow community members, decision-makers and stakeholders the ability to more easily access, utilize and analyze the hyperlocal data being collected on the site.
As warming climate projections are expected to worsen air quality in communities, with already limited data available in Massachusetts, this project will provide the public with more accessible and actionable information on air pollution through development of an app and improved digital infrastructure. With this new infrastructure, residents can avoid outdoor time on high-risk air quality days, thereby avoiding direct health impacts. Increasing the number of sensors and towns will allow for increased accuracy of tracking the spatial and temporal variation of ozone at a finer resolution across the geographic area.
TBD
TBD
Partners for a Healthier Community/Public Health Institute of Western Massachusetts
NA24OARX469G0010-T1-01
Extreme Temperatures
Massachusetts
September 2024 - August 2026
Atmospheric Composition and Chemistry
creating digital infrastructure to expand the healthy air network, to improve and expand the reach of life saving air quality information across massachusetts, this community driven project aims to expand the reach of life saving air quality information across massachusetts through the development of an app website upgrades and the addition of 20 air sensors through the healthy air network updates to the website and developing the app will allow community members decision makers and stakeholders the ability to more easily access utilize and analyze the hyperlocal data being collected on the site
Partners for a Healthier Community/Public Health Institute of Western Massachusetts
To improve and expand the reach of life-saving air quality information across Massachusetts.
0
Lake Tahoe Basin Changing Snow and Water Quality Dynamics
Arienzo
Observations
search_activity
2024
Level 3
To enhance the understanding and management of environmental challenges faced within the Lake Taho Basin, including wildfires, rain or snow events, drought stress, and vehicle impact.
Monica Arienzo
Active
Sun Sep 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Dynamics and Nesting
Obs
This research aims to enhance the understanding and management of environmental challenges in the Basin. Task 1focuses on assessing the impact of wildfires and vehicle emissions on snowpack black carbonparticulate loading, utilizing remote sensing and fieldwork across Tahoe's watersheds. Task 2aims to assess plastic pollution and the chemical 6PPD-Q derived from tire rubber to betterunderstand sources and transport in the Basin. Task 3 expands the Tahoe EnvironmentalObservatory Network to monitor climate change impacts on terrestrial and aquatic ecosystems,including real-time data sharing. Task 4 involves detailed hydrological modeling in selectwatersheds to explore first-order controls on streamflow sensitivity to changes in albedo relatedto climate, land cover change, and aerosol deposition. Scaling motifs are proposed to transferlearned information across the Tahoe basin to identify those watersheds most sensitive to change.Together, these tasks aim to advance scientific knowledge and inform sustainable managementpractices of the Tahoe Basin.
This research is broken down into 4 separate tasks. (1) Provide insights into the impact of wildfires, vehicle traffic, and wood fire emissions on snowpack and water originating as snowmelt. Results will help local land managers and planners better understand thebenefits of policies to reduce wildfire risk and local emissions, and communicate these benefits to the public. (2) This information will aid water managers, such as the Tahoe Regional Planning Authority, Tahoe Water Suppliers, public utilities, and municipalities when assessing potential sources and next steps to address these contaminants. (3) The expansion of the Tahoe Environmental Observatory Network will engage the public and agencies for understanding real time change related to disturbances influencing the basin like wildfires, rain or snow events, and drought stress. (4) Sensitivity metrics will be developed to extend numerical modeling results across the Lake Tahoe basin to identify those watersheds at greatest potential risk to changes in snowpack.
TBD
TBD
Desert Research Institute
NA24OARX469G0009-T1-01
Water Extremes
Nevada
September 2024 - August 2025
Atmospheric Composition and Chemistry, Dynamics and Nesting
lake tahoe basin changing snow and water quality dynamics, to enhance the understanding and management of environmental challenges faced within the lake taho basin including wildfires rain or snow events drought stress and vehicle impact, this research aims to enhance the understanding and management of environmental challenges in the basin task 1focuses on assessing the impact of wildfires and vehicle emissions on snowpack black carbonparticulate loading utilizing remote sensing and fieldwork across tahoe's watersheds task 2aims to assess plastic pollution and the chemical 6ppd q derived from tire rubber to betterunderstand sources and transport in the basin task 3 expands the tahoe environmentalobservatory network to monitor climate change impacts on terrestrial and aquatic ecosystems including real time data sharing task 4 involves detailed hydrological modeling in selectwatersheds to explore first order controls on streamflow sensitivity to changes in albedo relatedto climate land cover change and aerosol deposition scaling motifs are proposed to transferlearned information across the tahoe basin to identify those watersheds most sensitive to change together these tasks aim to advance scientific knowledge and inform sustainable managementpractices of the tahoe basin
Desert Research Institute
To enhance the understanding and management of environmental challenges faced within the Lake Taho Basin, including wildfires, rain or snow events, drought stress, and vehicle impact.
0
Next-Generation Airborne Doppler Radar Processing for Tropical Cyclone Reconnaissance Data Advancement
Bell
Observations
search_activity
2025
Level 3
This project aims to develop a next-generation software framework to improve three essential components in the airborne Doppler radar data pipeline. This framework is intended to support current NOAA aircraft radars and meet the needs of the future pipeline for the C-130 Vertically Scanning Doppler Radar (VSDR) in 2030 and beyond.
Michael Bell
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2027 03:00:00 GMT-0400 (Eastern Daylight Time)
This project aims to improve the initialization of tropical cyclone numerical models for better forecasts, provide forecasters with a clearer understanding of tropical cyclone wind and precipitation structure, and enable faster adaptation of the airborne radar data pipeline to next-generation aircraft observations. The project will also produce QCed, dealiased radar data and 3D wind and precipitation analyses, with all developed software being open-source. 1) Improved radar data quality control with machine learning, 2) Improved velocity dealiasing and super-obbing with machine learning, and 3) Improved threedimensional wind and precipitation synthesis.
Improved initialization of tropical cyclone numerical models for better forecasts, better understanding by forecasters of the present structure of tropical cyclone winds and precipitation, and faster adaptation of the airborne radar data pipeline to next-generation aircraft observations from the NOAA G-550 jet and C-130 VSDR.
next generation airborne doppler radar processing for tropical cyclone reconnaissance data advancement, this project aims to develop a next generation software framework to improve three essential components in the airborne doppler radar data pipeline this framework is intended to support current noaa aircraft radars and meet the needs of the future pipeline for the c 130 vertically scanning doppler radar vsdr in 2030 and beyond, this project aims to improve the initialization of tropical cyclone numerical models for better forecasts provide forecasters with a clearer understanding of tropical cyclone wind and precipitation structure and enable faster adaptation of the airborne radar data pipeline to next generation aircraft observations the project will also produce qced dealiased radar data and 3d wind and precipitation analyses with all developed software being open source 1 improved radar data quality control with machine learning 2 improved velocity dealiasing and super obbing with machine learning and 3 improved threedimensional wind and precipitation synthesis
Colorado State University
This project aims to develop a next-generation software framework to improve three essential components in the airborne Doppler radar data pipeline. This framework is intended to support current NOAA aircraft radars and meet the needs
0
Stepped Frequency Microwave Radiometer Calibration and Re-calibration Tool
Jelenak
Observations
search_activity
2025
Level 3
This project aims to develop a robust calibration approach that can be applied
across "all SFMR instruments on reconnaissance aircraft, improving the SFMR unit calibration and cross-unit calibration.
Zorana Jelenak
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2027 03:00:00 GMT-0400 (Eastern Daylight Time)
This project will result in significant cross-governmental cost savings by improving and correcting Stepped Frequency Microwave Radiometer (SFMR) units (3 NOAA owned, 15-20 Air Force owned). Additionally, this research will avert the need to replace these one-of-a-kind hurricane surface wind measuring instruments onboard the NOAA Hurricane Hunter P-3 aircraft and the Air Force's 53rd Weather Reconnaissance Squadron aircraft fleet.
Enhanced calibration and drift detection will ensure consistent SFMR performance across all units. Improved data quality control will support
better data assimilation. This, in turn, will enhance forecasters' ability to more reliably estimate storm intensity and structure from the SFMR. The project outcome will impact the historical SFMR dataset's integrity.
stepped frequency microwave radiometer calibration and re calibration tool, this project aims to develop a robust calibration approach that can be applied across "all sfmr instruments on reconnaissance aircraft improving the sfmr unit calibration and cross unit calibration, this project will result in significant cross governmental cost savings by improving and correcting stepped frequency microwave radiometer sfmr units 3 noaa owned 15 20 air force owned additionally this research will avert the need to replace these one of a kind hurricane surface wind measuring instruments onboard the noaa hurricane hunter p 3 aircraft and the air force's 53rd weather reconnaissance squadron aircraft fleet
UCAR/CPAESS
This project aims to develop a robust calibration approach that can be applied
across "all SFMR instruments on reconnaissance aircraft, improving the SFMR unit calibration and cross-unit calibration.
0
Improving Specific Differential Phase and Rain Estimation through Optimized
Use of Polarimetric Radar Measurements
Zhang
Observations
search_activity
2025
Level 3
This project aims to optimally utilize polarimetric radar data (PRD) and physical constraints
to improve estimates of specific differential phase (KDP), enhance rainfall rate (R) products, and
provide novel estimates of differential backscattering phase (d) and other rain microphysical
parameters.
Guifu Zhang
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2027 03:00:00 GMT-0400 (Eastern Daylight Time)
Numerical Weather Prediction (NWP), Radar
Obs
This project aims to improve precipitation estimation and forecasting to inform better decision-making to minimize economic losses, such as damages to agricultural yields and disruption to transportation operations. This will enable the dissemination of more accurate, reliable radar data and products utilized by NOAA's National Weather Service to forecast precipitation events.
The development of KDP and rain estimation algorithms and programs/codes that (1) are novel and more accurate than existing methods, (2) are at a finer resolution than current products, and (3) produce new data products of d and other rain microphysical parameters.
TBD
TBD
University of Oklahoma
NA25OARX459C0232-T1-01
Severe Weather, Water Extremes, Winter Weather
Oklahoma
August 2025 - July 2027
Numerical Weather Prediction (NWP), Radar
improving specific differential phase and rain estimation through optimized use of polarimetric radar measurements, this project aims to optimally utilize polarimetric radar data prd and physical constraints to improve estimates of specific differential phase kdp enhance rainfall rate r products and provide novel estimates of differential backscattering phase d and other rain microphysical parameters, this project aims to improve precipitation estimation and forecasting to inform better decision making to minimize economic losses such as damages to agricultural yields and disruption to transportation operations this will enable the dissemination of more accurate reliable radar data and products utilized by noaa's national weather service to forecast precipitation events
University of Oklahoma
This project aims to optimally utilize polarimetric radar data (PRD) and physical constraints to improve estimates of specific differential phase (KDP), enhance rainfall rate (R) products, and provide novel estimates of differential backscattering phase (d)
0
Developing an Uncrewed Surface Vehicle for Weather Buoy Mitigation
Chiodi
Observations
search_activity
2025
Level 3
This project aims to develop, test, and demonstrate a high-endurance, low-cost Uncrewed Surface Vehicle (USV) specifically designed to mitigate outages in weather buoys.
Andrew Chiodi
Active
Fri Aug 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2027 03:00:00 GMT-0400 (Eastern Daylight Time)
Surface Observing Networks, Weather Buoy Systems
Obs
This project aims to provide a continuous stream of data collection for weather forecast needs and public safety, this project will demonstrate a low-cost, commercial uncrewed surface vehicle technology that will mitigate both routine and unplanned outages in NOAA's operational moored buoys. Additionally, this project will reduce financial and operational risks as the data is critical for marine navigation, search and rescue, natural disaster response, and infrastructure and energy planning.
A versatile tool for continuous weather monitoring that addresses gaps from outages in standard buoy systems and, ultimately, helps provide the observational basis needed for enhancing weather forecast quality and maritime situational awareness.
TBD
TBD
University of Washington/CICOES
NA25OARX459C0256-T1-01
Water Extremes, Tropical Cyclones
Washington
August 2025 - July 2027
Surface Observing Networks, Weather Buoy Systems
developing an uncrewed surface vehicle for weather buoy mitigation, this project aims to develop test and demonstrate a high endurance low cost uncrewed surface vehicle usv specifically designed to mitigate outages in weather buoys, this project aims to provide a continuous stream of data collection for weather forecast needs and public safety this project will demonstrate a low cost commercial uncrewed surface vehicle technology that will mitigate both routine and unplanned outages in noaa's operational moored buoys additionally this project will reduce financial and operational risks as the data is critical for marine navigation search and rescue natural disaster response and infrastructure and energy planning
University of Washington/CICOES
This project aims to develop, test, and demonstrate a high-endurance, low-cost Uncrewed Surface Vehicle (USV) specifically designed to mitigate outages in weather buoys.
0
MJO and QBO Contributions to U.S. Precipitation Skill at S2S Leads
Barnes
S2S
check_circle
2019
Level 3
Improve precipitation forecasts at S2S timescales by understanding the role of MJO and QBO in such precipitation
Elizabeth Barnes, Juliana Dias, Eric Maloney, Stefan Tulich, George Kiladis
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Forecast Skill Metrics, Teleconnections
S2S
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Unified Forecast System (UFS)
The main goals of the proposed work are to (1) understand the role of the MJO and QBO in driving skillful precipitation forecasts, (2) diagnose the related processes that drive biases within FV3 that lead to low precipitation skill, and (3) improve precipitation forecasts at S2S leads by using information from the UFS and empirically-derived relationships between the MJO and QBO. To achieve these goals, we propose three distinct activities: A1: design, perform and analyze FV3 relaxation experiments to quantify the importance of the MJO and QBO in FV3 precipitation/AR forecast skill, A2: diagnose biases within dynamical models via process-oriented diagnostics, and A3: develop a skillful hybrid empirical-dynamical prediction scheme of North American precipitation using UFS forecasts of the MJO.
This work will assess the importance of MJO teleconnections as mediated by the QBO for skillful forecasts of precipitation within FV3, and quantify model biases in the MJO and the stratospheric circulation and how they impact the prediction skill of wintertime precipitation over the United States at S2S leads. In addition to precipitation, this work also places an emphasis on forecasts of atmospheric rivers as a means of evaluating the ability of FV3 to simulate a fundamental process driving precipitation along the west coast of the U.S.
N/A
N/A
Colorado State University,
CIRES/University of Colorado,
NOAA/OAR/PSL
NA19OAR4590151
Water Extremes
Colorado
Subseasonal to Seasonal Program (S2S)
September 2019 - August 2024
Dynamics and Nesting, Forecast Skill Metrics, Teleconnections
mjo and qbo contributions to u s precipitation skill at s2s leads, improve precipitation forecasts at s2s timescales by understanding the role of mjo and qbo in such precipitation, the main goals of the proposed work are to 1 understand the role of the mjo and qbo in driving skillful precipitation forecasts 2 diagnose the related processes that drive biases within fv3 that lead to low precipitation skill and 3 improve precipitation forecasts at s2s leads by using information from the ufs and empirically derived relationships between the mjo and qbo to achieve these goals we propose three distinct activities a1 design perform and analyze fv3 relaxation experiments to quantify the importance of the mjo and qbo in fv3 precipitation ar forecast skill a2 diagnose biases within dynamical models via process oriented diagnostics and a3 develop a skillful hybrid empirical dynamical prediction scheme of north american precipitation using ufs forecasts of the mjo
Not Applicable (N/A)
Colorado State University, CIRES/University of Colorado, NOAA/OAR/PSL
Improve precipitation forecasts at S2S timescales by understanding the role of MJO and QBO in such precipitation
18
S2S Forecasting of North American Precipitation Anomalies: Using Empirical Forecasts to Challenge Dynamical Forecasts
Randall
S2S
search_activity
2019
Level 3
Reveal strengths/weaknesses of the two UFS models studied and bring about more skillful dynamical forecasts of S2S precipitation
David Randall, Elizabeth Barnes, Wanqiu Wang, Stefan Tulich
Active
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Teleconnections
S2S
Unified Forecast System (UFS)
We will evaluate the S2S predictability as simulated by two enhanced versions of the UFS, already developed under prior NOAA support, with an emphasis on the influence of the Madden- Julian Oscillation (MJO) on North American precipitation anomalies. We will compare with the corresponding results from the current operational version of the model
The results of this study will reveal strengths and weaknesses of the models studied, and will help to bring about more skillful dynamical forecasts of precipitation on S2S time scales.
N/A
N/A
Colorado State University,
NOAA/NWS/NCEP/CPC,
NOAA/OAR/PSL
NA19OAR4590155
Water Extremes
Colorado, Maryland
Subseasonal to Seasonal Program (S2S)
September 2019 - August 2024
Dynamics and Nesting, Teleconnections
s2s forecasting of north american precipitation anomalies using empirical forecasts to challenge dynamical forecasts, reveal strengths weaknesses of the two ufs models studied and bring about more skillful dynamical forecasts of s2s precipitation, we will evaluate the s2s predictability as simulated by two enhanced versions of the ufs already developed under prior noaa support with an emphasis on the influence of the madden julian oscillation mjo on north american precipitation anomalies we will compare with the corresponding results from the current operational version of the model
Not Applicable (N/A)
Colorado State University, NOAA/NWS/NCEP/CPC, NOAA/OAR/PSL
Reveal strengths/weaknesses of the two UFS models studied and bring about more skillful dynamical forecasts of S2S precipitation
10
Benefits of stochastic parameterization on the sub-seasonal to seasonal timescales in hindcasts with CESM and UFS
Berner
S2S
search_activity
2019
Level 3
Assess the benefits
of a stochastic parameterization suite for the subseasonal-to-seasonal temp/precip forecast range of 2 to 6
weeks.
Judith Berner, Philip Pegion, Jadwiga Richter, Jeffrey Whitaker
Active
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Parameterization
S2S
Community Earth System Model (CESM), Unified Forecast System (UFS)
By comparing stochastically perturbed hindcasts to the unperturbed ones, we will assess the benefits of a stochastic parameterization suite for the subseasonal-to-seasonal forecast range of 2 to 6 weeks. Emphasis will be given to surface temperature and precipitation skill, but also dynamically important fields such as geopotential height at 500 hPa. Additionally, flow-dependent subseasonal predictability will be assessed by comparing the skill of forecasts projecting at initial time on extra-tropical low-frequency regimes.
The proposed work will foster the development of a seamless prediction system by enabling to test and augment uncertainty schemes (e.g. SPP), which are currently being developed separately for the convective-permitting and global scale and accelerate the timeline towards defining an optimal configuration for NCEP's next-generation, unified, global ensemble system.
N/A
N/A
University Corporation for Atmospheric Research/CGD,
CIRES/University of Colorado Boulder,
NOAA/OAR/PSL
NA19OAR4590156,
NA19OAR4590157
Relevant to All Hazards
Colorado
Subseasonal to Seasonal Program (S2S)
September 2019 - August 2024
Dynamics and Nesting, Parameterization
benefits of stochastic parameterization on the sub seasonal to seasonal timescales in hindcasts with cesm and ufs, assess the benefits of a stochastic parameterization suite for the subseasonal to seasonal temp precip forecast range of 2 to 6 weeks, by comparing stochastically perturbed hindcasts to the unperturbed ones we will assess the benefits of a stochastic parameterization suite for the subseasonal to seasonal forecast range of 2 to 6 weeks emphasis will be given to surface temperature and precipitation skill but also dynamically important fields such as geopotential height at 500 hpa additionally flow dependent subseasonal predictability will be assessed by comparing the skill of forecasts projecting at initial time on extra tropical low frequency regimes
Not Applicable (N/A)
University Corporation for Atmospheric Research/CGD, CIRES/University of Colorado Boulder, NOAA/OAR/PSL
Assess the benefits
of a stochastic parameterization suite for the subseasonal-to-seasonal temp/precip forecast range of 2 to 6
weeks.
1
Diagnosing and Improving Unified Forecast System precipitation through observationally constrained stochastic parameterizations
Sardeshmukh
S2S
search_activity
2019
Level 3
Increase S2S precip predictive capability through improved stochastic parameterizations of chaotic
small-scale diabatic processes in the developing Unified Forecast System.
Prashant Sardeshmukh, Gilbert P. Compo, Cecile Penland, Daryl Kleist
Active
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Data Assimilation and Ensembles, Forecast Skill Metrics, Parameterization
S2S
Unified Forecast System (UFS)
Develop observationally-constrained specifications of r through two very different and complementary approaches: 1) by using the error statistics of initial tendency and short-term UFS atmospheric component [FV3GFS] forecasts, with the errors defined with respect to reanalyses generated using the same UFS atmospheric component model as in NCEP's newly developed coupled data assimilation system, and 2) by optimizing the performance of simple linear inverse models (LIMs) of the coupled tropical climate system through experimentation with various specifications of r. Our third goal is to assess the impacts of such observationally-constrained (1+r)P(x) types of parameterizations on the mean climate, climate variability, and forecast skill of the atmospheric FV3GFS and coupled UFS models.
The primary beneficiary of an improved UFS will be the NOAA/NWS Environmental Modeling Center and all centers that rely on the UFS forecasts for ancillary products. Additionally, many operational and research centers are struggling with incorporating stochastic physics in their climate and weather models.
N/A
N/A
CIRES/University of Colorado,
NOAA/OAR/PSL,
NOAA/NWS/NCEP/EMC
NA19OAR4590158
Water Extremes
Colorado, Maryland
Subseasonal to Seasonal Program (S2S)
September 2019 - August 2024
Coupled Models & Techniques, Data Assimilation and Ensembles, Forecast Skill Metrics, Parameterization
diagnosing and improving unified forecast system precipitation through observationally constrained stochastic parameterizations, increase s2s precip predictive capability through improved stochastic parameterizations of chaotic small scale diabatic processes in the developing unified forecast system, develop observationally constrained specifications of r through two very different and complementary approaches 1 by using the error statistics of initial tendency and short term ufs atmospheric component [fv3gfs] forecasts with the errors defined with respect to reanalyses generated using the same ufs atmospheric component model as in ncep's newly developed coupled data assimilation system and 2 by optimizing the performance of simple linear inverse models lims of the coupled tropical climate system through experimentation with various specifications of r our third goal is to assess the impacts of such observationally constrained 1+r p x types of parameterizations on the mean climate climate variability and forecast skill of the atmospheric fv3gfs and coupled ufs models
Not Applicable (N/A)
CIRES/University of Colorado, NOAA/OAR/PSL, NOAA/NWS/NCEP/EMC
Increase S2S precip predictive capability through improved stochastic parameterizations of chaotic
small-scale diabatic processes in the developing Unified Forecast System.
2
Assessment and calibration of extreme precipitation probabilities in S2S forecast models
Lepore
S2S
search_activity
2019
Level 3
Assess the extent to which the observed dependence of extreme rainfall on large-scale environments such as near-surface temperature, dew-point temperature and convective available potential energy (CAPE) is represented in submonthly (week-2 to week-4) forecasts.
Chiara Lepore, Michael Tippett, Melissa Ou, Michelle L'Heureux
Active
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Verification & Validation
S2S
Unified Forecast System (UFS)
Here we propose to assess the extent to which the observed dependence of extreme rainfall on large-scale environments such as near-surface temperature, dew-point temperature and convective available potential energy (CAPE) is represented in submonthly (week-2 to week-4) forecasts.
The results should enable better probabilistic precipitation week-2 to week-4 forecasts.
N/A
N/A
Columbia University,
NOAA/NWS/NCEP/CPC
NA19OAR4590159
Water Extremes
Maryland, New York
Subseasonal to Seasonal Program (S2S)
September 2019 - August 2024
Verification & Validation
assessment and calibration of extreme precipitation probabilities in s2s forecast models, assess the extent to which the observed dependence of extreme rainfall on large scale environments such as near surface temperature dew point temperature and convective available potential energy cape is represented in submonthly week 2 to week 4 forecasts, here we propose to assess the extent to which the observed dependence of extreme rainfall on large scale environments such as near surface temperature dew point temperature and convective available potential energy cape is represented in submonthly week 2 to week 4 forecasts
Not Applicable (N/A)
Columbia University, NOAA/NWS/NCEP/CPC
Assess the extent to which the observed dependence of extreme rainfall on large-scale environments such as near-surface temperature, dew-point temperature and convective available potential energy (CAPE) is represented in submonthly (week-2 to week-4) forecasts.
3
Ensemble Prediction and Predictability of Extreme Weather via Circulation Regimes
Straus
S2S
search_activity
2019
Level 3
Advance the predictive capability of extreme weather, and in particular storminess and precipitation on the subseasonal to seasonal time scales, using multi-model ensembles of reforecasts and forecasts from the SubX multi-model subseasonal prediction experiment, the North American Multi-Model Ensemble, and from NOAA's Unified Forecast System ensemble forecasts.
David Straus, Kathleen Pegion, Stephen Baxter
Active
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Reanalysis & Reforecasting
S2S
North American Multi-Model Ensemble (NMME), Unified Forecast System (UFS)
What this proposal does is to use circulation regimes to extend predictions of extreme weather into the subseasonal (beyond two weeks) to seasonal time scales. This requires a methodology of compositing observed metrics of extreme weather by regime, (that is the regime-dependence of the probability of extreme weather), as well as a way to characterize ensemble forecasts in terms of the probability of regime occurrence.
The storm diagnostics for these forecasts will give further dynamical information, which can be incorporated into (for example) the "Detailed Summary" currently given on the CPC Probabilistic Hazards Outlook web product
N/A
N/A
George Mason University, NOAA/NCEP/CPC
NA19OAR4590153
Water Extremes
Maryland, Virginia
Subseasonal to Seasonal Program (S2S)
September 2019 - August 2024
Data Assimilation and Ensembles, Reanalysis & Reforecasting
ensemble prediction and predictability of extreme weather via circulation regimes, advance the predictive capability of extreme weather and in particular storminess and precipitation on the subseasonal to seasonal time scales using multi model ensembles of reforecasts and forecasts from the subx multi model subseasonal prediction experiment the north american multi model ensemble and from noaa's unified forecast system ensemble forecasts, what this proposal does is to use circulation regimes to extend predictions of extreme weather into the subseasonal beyond two weeks to seasonal time scales this requires a methodology of compositing observed metrics of extreme weather by regime that is the regime dependence of the probability of extreme weather as well as a way to characterize ensemble forecasts in terms of the probability of regime occurrence
Not Applicable (N/A)
George Mason University, NOAA/NCEP/CPC
Advance the predictive capability of extreme weather, and in particular storminess and precipitation on the subseasonal to seasonal time scales, using multi-model ensembles of reforecasts and forecasts from the SubX multi-model subseasonal prediction experiment, the
2
Enhancing NOAA UFS subseasonal to seasonal predictions of precipitation and drought via improved representation of snowpack processes
He
S2S
search_activity
2022
Level 3
Enhance NOAA UFS capabilities of subseasonal to seasonal (S2S) forecasts of precipitation and drought through improving snowpack processes and their coupling with the atmosphere, with a focus on snowpack-precipitation feedback.
Cenlin He, Fei Chen, Ronnie Abolafia-Rosenzweig, Michael Barlage
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Dynamics and Nesting, Parameterization
S2S
Noah Multiparameterization Land Surface Model (Noah-MP LSM), Unified Forecast System (UFS)
The overarching goal of this proposal is to enhance NOAA UFS capabilities of subseasonal
to seasonal (S2S) forecasts of precipitation and drought through improving snowpack processes
and their coupling with the atmosphere, with a focus on snowpack-precipitation feedback. We
propose to incorporate, evaluate, and improve subgrid snowpack parameterizations in the NoahMP land surface model (LSM) within NOAA UFS to better capture snowpack evolution through
improved representations of snowpack properties and snowpack-radiation-vegetation interactions.
Current S2S precipitation predictions in the NOAA UFS are still subject to large uncertainties,
further affecting drought predictions. One important contributor to precipitation prediction
uncertainty is the snowpack-precipitation feedback, which alters precipitation characteristics over
timescales of days to months. However, the impacts of snowpack-precipitation feedbacks on S2S
predictions of precipitation and drought in the NOAA UFS have not been thoroughly assessed and
quantified. Moreover, S2S snowpack modeling in Noah-MP, a land component of NOAA UFS,
still suffers from large biases. This leads to crucial deficiencies in representing land-atmosphere
interactions and feedbacks in the coupled weather and climate prediction system, and calls for a
close examination of subgrid snowpack processes in S2S snowpack-precipitation feedbacks.
The primary planned outcomes include: (1) improved knowledge of snowpack-precipitation feedbacks and their role in S2S precipitation and drought predictions; (2) improved S2S forecast skills and accuracies for precipitation and drought by the NOAA UFS system.
EMC
Mike Barlage (EMC)
National Center for Atmospheric Research, NOAA/EMC
NA22OAR4590503
Water Extremes
Colorado
Subseasonal to Seasonal Program (S2S)
August 2022 - July 2025
Coupled Models & Techniques, Dynamics and Nesting, Parameterization
enhancing noaa ufs subseasonal to seasonal predictions of precipitation and drought via improved representation of snowpack processes, enhance noaa ufs capabilities of subseasonal to seasonal s2s forecasts of precipitation and drought through improving snowpack processes and their coupling with the atmosphere with a focus on snowpack precipitation feedback, the overarching goal of this proposal is to enhance noaa ufs capabilities of subseasonal to seasonal s2s forecasts of precipitation and drought through improving snowpack processes and their coupling with the atmosphere with a focus on snowpack precipitation feedback we propose to incorporate evaluate and improve subgrid snowpack parameterizations in the noahmp land surface model lsm within noaa ufs to better capture snowpack evolution through improved representations of snowpack properties and snowpack radiation vegetation interactions current s2s precipitation predictions in the noaa ufs are still subject to large uncertainties further affecting drought predictions one important contributor to precipitation prediction uncertainty is the snowpack precipitation feedback which alters precipitation characteristics over timescales of days to months however the impacts of snowpack precipitation feedbacks on s2s predictions of precipitation and drought in the noaa ufs have not been thoroughly assessed and quantified moreover s2s snowpack modeling in noah mp a land component of noaa ufs still suffers from large biases this leads to crucial deficiencies in representing land atmosphere interactions and feedbacks in the coupled weather and climate prediction system and calls for a close examination of subgrid snowpack processes in s2s snowpack precipitation feedbacks
Environmental Modeling Center (EMC)
National Center for Atmospheric Research, NOAA/EMC
Enhance NOAA UFS capabilities of subseasonal to seasonal (S2S) forecasts of precipitation and drought through improving snowpack processes and their coupling with the atmosphere, with a focus on snowpack-precipitation feedback.
5
Assessing the impact of dynamic vegetation on drought forecasts
Otkin
S2S
search_activity
2022
Level 3
Enhance the capabilities of METplus
for land verification and provide valuable information regarding the impact of various treatments
of vegetation in Noah-MP on the predictability of drought over sub-seasonal time scales.
Jason Otkin, Michael Ek, Tara Jensen
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Model Evaluation Tools Plus (METPlus), Noah Multiparameterization Land Surface Model (Noah-MP LSM), Unified Forecast System (UFS), UFS-Season to Subseasonal (UFS-S2S)
The proposed project has three components that together will lead to increased understanding of
the impact of predicted vegetation in the Noah-MP land surface model (LSM) on the predictability
of drought over S2S time scales and that will enhance the capabilities of the Model Evaluation
Tools (METplus) verification system for coupled land-atmosphere process studies and land model
verification. The first component focuses on assessing the accuracy of the land surface and landatmosphere coupling in reforecasts from UFS S2S prototypes performed by the UFS Research to
Operations project team to determine if the ability of the prototypes to accurately predict drought
and land-atmosphere coupling is improving with each model upgrade. The second component will
use results from the preceding analysis to guide development of new model experiments that will
allow us to dig deeper into the behavior of the Noah-MP LSM during drought when using predicted
vegetation that actively grows or senesces in response to changes in atmospheric and soil moisture
conditions. Different scenarios (predicted vs climatological vegetation) and parameter choices will
be used in Noah-MP simulations run offline over large regions in an uncoupled mode forced by
observations or in a single column model setting at specific grid points to examine atmospheric
response in a cost-effective manner. The third research component will add new verification tools
and use-cases to METplus to support efforts within the community to evaluate the accuracy of the
land surface and land-atmosphere coupling in the UFS.
We anticipate the following outputs and products during this 3-year project:
Detailed analysis of how different vegetation treatments in the Noah-MP LSM impact the evolution of soil moisture, surface fluxes, and land-atmosphere coupling during drought using output from the UFS S2S prototypes as well as offline and SCM experiments. This work should also lead to reduced model error during periods of vegetation green-up and senescence.
Enhancements to METplus through implementation of new verification and visualization tools focusing on the land surface and land-atmosphere coupling, along with new use-cases and tutorial documentation demonstrating how to use these tools to assess the accuracy of the UFS.
Dissemination of guidance to model developers regarding the ability of the UFS and Noah-MP to accurately depict land surface conditions and land-atmosphere coupling during drought events.
UCAR
N/A
University of Wisconsin-Madison,
National Center for Atmospheric Research (NCAR), University Corporation for Atmospheric Research (UCAR), George Mason University, NOAA EMC, NOAA CPC, NOAA PSL
assessing the impact of dynamic vegetation on drought forecasts, enhance the capabilities of metplus for land verification and provide valuable information regarding the impact of various treatments of vegetation in noah mp on the predictability of drought over sub seasonal time scales, the proposed project has three components that together will lead to increased understanding of the impact of predicted vegetation in the noah mp land surface model lsm on the predictability of drought over s2s time scales and that will enhance the capabilities of the model evaluation tools metplus verification system for coupled land atmosphere process studies and land model verification the first component focuses on assessing the accuracy of the land surface and landatmosphere coupling in reforecasts from ufs s2s prototypes performed by the ufs research to operations project team to determine if the ability of the prototypes to accurately predict drought and land atmosphere coupling is improving with each model upgrade the second component will use results from the preceding analysis to guide development of new model experiments that will allow us to dig deeper into the behavior of the noah mp lsm during drought when using predicted vegetation that actively grows or senesces in response to changes in atmospheric and soil moisture conditions different scenarios predicted vs climatological vegetation and parameter choices will be used in noah mp simulations run offline over large regions in an uncoupled mode forced by observations or in a single column model setting at specific grid points to examine atmospheric response in a cost effective manner the third research component will add new verification tools and use cases to metplus to support efforts within the community to evaluate the accuracy of the land surface and land atmosphere coupling in the ufs
University of Wisconsin-Madison, National Center for Atmospheric Research (NCAR), University Corporation for Atmospheric Research (UCAR), George Mason University, NOAA EMC, NOAA CPC, NOAA PSL
Enhance the capabilities of METplus
for land verification and provide valuable information regarding the impact of various treatments
of vegetation in Noah-MP on the predictability of drought over sub-seasonal time scales.
0
The Road to the Seasonal Forecast System: Improving Prediction of Precipitation Extremes
Cash
S2S
search_activity
2022
Level 3
Deliver an extensive suite of seasonal integrations with prototypes of the SFSv1, as well as detailed suggestions for the optimal choice for ensemble size, data source for initial conditions, processes to target for model improvement, and multi-model configuration
Benjamin Cash, James Kinter, David Straus, Chul-Su Shin
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Teleconnections, Verification & Validation
S2S
North American Multi-Model Ensemble (NMME), Seasonal Forecast System (SFS), Unified Forecast System (UFS)
Our proposed work is comprised of three focus areas that directly address the challenges to the development and deployment of the SFSv1. Focus 1: We will conduct ensembles of seasonal integrations with candidate configurations of the SFSv1. We will assess the impact on prediction skill of ensemble size, model configuration, and source of initial conditions. The experiment suite will be designed in consultation with our NOAA partners, including members of the ensemble team, to be complementary to their integrations. Model skill will be assessed using key seasonal metrics defined by the UFS community1, with an emphasis on precipitation and its extremes. Focus 2: We will conduct detailed analyses of the relationship between errors in these critical metrics and process-level model diagnostics. We will also test the impact of intervening in the model to reduce errors in the three-dimensional atmospheric heating field, particularly with respect to the association with tropical sources of predictability such as ENSO. Focus 3: We will conduct rigorous statistical comparisons of the relatively skill of our integrations and existing seasonal data from NMME and any available additional runs performed with the UFS.
Each focus area directly corresponds to one of the three focus areas of the S2S competition. At the end of the project, we will deliver and extensive suite of seasonal integration with prototypes of the SFSv1, as well as detailed suggestions for the optimal choice of ensemble size, data source for initial conditions, processes to target for model improvement, and multi-model configuration. Our project will bring to bear significant computational and human resources from outside of NOAA to supplement the existing SFSv1 development effort and help to ensure its ultimate success.
N/A
N/A
George Mason University
NA22OAR4590506
Water Extremes
Virginia
Subseasonal to Seasonal Program (S2S)
August 2022 - July 2025
Data Assimilation and Ensembles, Teleconnections, Verification & Validation
the road to the seasonal forecast system improving prediction of precipitation extremes, deliver an extensive suite of seasonal integrations with prototypes of the sfsv1 as well as detailed suggestions for the optimal choice for ensemble size data source for initial conditions processes to target for model improvement and multi model configuration, our proposed work is comprised of three focus areas that directly address the challenges to the development and deployment of the sfsv1 focus 1 we will conduct ensembles of seasonal integrations with candidate configurations of the sfsv1 we will assess the impact on prediction skill of ensemble size model configuration and source of initial conditions the experiment suite will be designed in consultation with our noaa partners including members of the ensemble team to be complementary to their integrations model skill will be assessed using key seasonal metrics defined by the ufs community1 with an emphasis on precipitation and its extremes focus 2 we will conduct detailed analyses of the relationship between errors in these critical metrics and process level model diagnostics we will also test the impact of intervening in the model to reduce errors in the three dimensional atmospheric heating field particularly with respect to the association with tropical sources of predictability such as enso focus 3 we will conduct rigorous statistical comparisons of the relatively skill of our integrations and existing seasonal data from nmme and any available additional runs performed with the ufs
Not Applicable (N/A)
George Mason University
Deliver an extensive suite of seasonal integrations with prototypes of the SFSv1, as well as detailed suggestions for the optimal choice for ensemble size, data source for initial conditions, processes to target for model improvement,
0
Characterizing the impact of UFS model error and bias on S2S CONUS forecast skill using a hybrid UFS-machine learning approach
Gehne
S2S
search_activity
2022
Level 3
(1) establish a upper bound on the downstream CONUS forecast skill that might be realized if each individual, tropically-based, dynamical mode was perfectly simulated in the UFS, and (2) quantify how the inaccurate simulation of these dynamical modes by the current generation UFS causes errors and biases that degrade CONUS forecast skill.
Maria Gehne, Juliana Dias, Emerson LaJoie, Matt Newman, Yan Wang, John Albers
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Dynamics and Nesting, Forecast Skill Metrics, Model & Forecast Guidance, Teleconnections
S2S
Unified Forecast System (UFS)
Tropical variability (e.g., the MJO and ENSO) and tropical-extratropical teleconnections must
be simulated accurately to issue reliable subseasonal-to-seasonal (S2S) forecast guidance.
Unfortunately, systematic model errors, both in the representation of tropical variability and the
extratropical background state through which teleconnections propagate, consistently limit our
ability to issue skillful S2S guidance. This proposal will identify how model bias and forecast error
associated with the simulation of specific dynamical modes of climate variability in the Unified
Forecast System (UFS) degrade forecasts of temperature and precipitation events over the CONUS
on subseasonal timescales. We seek to answer two fundamental questions: 1) to what extent could
weeks 2-6 CONUS precipitation and 2m temperature forecasts become more skillful by improving
the UFS representation potentially predictable forms of tropical subseasonal variability; and 2)
what are the relative contributions of tropical error sources and extratropical model biases for
degrading CONUS subseasonal precipitation and 2m temperature forecast skill?
We propose to answer these questions via a novel approach merging machine learning
techniques (developed at NOAA PSL) with nudged hindcast experiments conducted using research
and prototype versions of the UFS. We will employ a machine learning technique called a linear
inverse model (LIM), which can be used both as a subseasonal forecast model and as a dynamical
filter that isolates specific dynamical modes of climate variability. We have already constructed
LIMs with subseasonal forecast skill comparable to current ECMWF and NCEP operational
models (at and beyond 3-week forecast leads), and used the LIM as a dynamical filter to identify
the MJO, ENSO, stratospheric processes, and other modes of atmospheric internal variability.
Merging these two aspects of the LIM with the UFS will allow us to (1) establish a upper bound
on the downstream CONUS forecast skill that might be realized if each individual, tropicallybased, dynamical mode was perfectly simulated in the UFS, and (2) quantify how the inaccurate
simulation of these dynamical modes by the current generation UFS causes errors and biases that
degrade CONUS forecast skill.
Operational subseasonal forecasters regularly utilize information about process-based sources of forecast skill. For example, during the weekly operational CPC weeks 3-4 forecast discussions, the MJO RMM index and ENSO forecasts (via the CPC multiple linear regression tool) are reviewed to infer sources of predictability. Thus, our work will help forecasters understand the errors and biases inherent to key subseasonal forecasting predictors. In addition, a dynamical process-based filter will inform which model improvements will yield the most return on investment. For example, properly simulating the QBO requires significant investments in tuning gravity wave schemes (Bushell et al. 2020) and substantial computational resources due to the high vertical resolutions required (Holt et al. 2020, Richter et al. 2020). Given limited resources to
improve and run the models, does fixing the QBO yield the best return on investment versus improving atmosphere-ocean coupling (ENSO) or convective processes (MJO)? Our proposal therefore directly addresses S2S Competition Goal 2: To improve model capabilities, it is crucial to identify and address sources of model bias across NOAA's modeling suite. This involves understanding the source of the biases such as issues in models' physical process representationvia a systematic process-oriented evaluation of the biases
CPC
Emerson LaJoi
CIRES/University of Colorado,
NOAA/OAR/PSL,
NOAA/NWS/CPC
NA22OAR4590507
Relevant to All Hazards
Colorado
Subseasonal to Seasonal Program (S2S)
August 2022 - July 2025
Artificial Intelligence (AI) / Machine Learning (ML), Dynamics and Nesting, Forecast Skill Metrics, Model & Forecast Guidance, Teleconnections
characterizing the impact of ufs model error and bias on s2s conus forecast skill using a hybrid ufs machine learning approach, 1 establish a upper bound on the downstream conus forecast skill that might be realized if each individual tropically based dynamical mode was perfectly simulated in the ufs and 2 quantify how the inaccurate simulation of these dynamical modes by the current generation ufs causes errors and biases that degrade conus forecast skill, tropical variability e g the mjo and enso and tropical extratropical teleconnections must be simulated accurately to issue reliable subseasonal to seasonal s2s forecast guidance unfortunately systematic model errors both in the representation of tropical variability and the extratropical background state through which teleconnections propagate consistently limit our ability to issue skillful s2s guidance this proposal will identify how model bias and forecast error associated with the simulation of specific dynamical modes of climate variability in the unified forecast system ufs degrade forecasts of temperature and precipitation events over the conus on subseasonal timescales we seek to answer two fundamental questions 1 to what extent could weeks 2 6 conus precipitation and 2m temperature forecasts become more skillful by improving the ufs representation potentially predictable forms of tropical subseasonal variability and 2 what are the relative contributions of tropical error sources and extratropical model biases for degrading conus subseasonal precipitation and 2m temperature forecast skill? we propose to answer these questions via a novel approach merging machine learning techniques developed at noaa psl with nudged hindcast experiments conducted using research and prototype versions of the ufs we will employ a machine learning technique called a linear inverse model lim which can be used both as a subseasonal forecast model and as a dynamical filter that isolates specific dynamical modes of climate variability we have already constructed lims with subseasonal forecast skill comparable to current ecmwf and ncep operational models at and beyond 3 week forecast leads and used the lim as a dynamical filter to identify the mjo enso stratospheric processes and other modes of atmospheric internal variability merging these two aspects of the lim with the ufs will allow us to 1 establish a upper bound on the downstream conus forecast skill that might be realized if each individual tropicallybased dynamical mode was perfectly simulated in the ufs and 2 quantify how the inaccurate simulation of these dynamical modes by the current generation ufs causes errors and biases that degrade conus forecast skill
Climate Prediction Center (CPC)
CIRES/University of Colorado, NOAA/OAR/PSL, NOAA/NWS/CPC
(1) establish a upper bound on the downstream CONUS forecast skill that might be realized if each individual, tropically-based, dynamical mode was perfectly simulated in the UFS, and (2) quantify how the inaccurate simulation of
0
Integrated surface physics for coupled hydrometeorology in the UFS for S2S prediction
Gochis
S2S
search_activity
2022
Level 3
Extend a high-resolution, hydrologically-
enhanced land-hydrology modeling system to the global domain.
David Gochis, Paul Dirmeyer, Michael Ek
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
National Water Model (NWM), Noah Multiparameterization Land Surface Model (Noah-MP LSM), Unified Forecast System (UFS), Weather Research and Forecasting Model (WRF), Weather Research and Forecasting Model - Hydro (WRF-Hydro)
Given the documented importance of the terrestrial system in weather and climate prediction for
subseasonal to seasonal time scales, this project will extend a high-resolution, hydrologicallyenhanced land-hydrology modeling system to the global domain. In doing so, this work will
directly leverage prior work that has been completed which connects the current NOAA National
Water Model configuration of the NoahMP/WRF-Hydro model with the emerging NOAA/NCEP
Unified Forecast System (UFS), and assesses the impact of this land-hydrology system in 2-week
to 3 month predictions, which will ultimately include other Earth system components (ocean, seaice, etc.). The proposed work represents a significant improvement in land surface model
sophistication, land surface hydrology representation, and the representation of terrestrial
processes interacting with the atmosphere, all aimed toward improved operational forecast skill
and model fidelity on a spectrum of time scales. Core hydrologic enhancements to the land model
will include subsurface lateral routing, surface water ponding and inundation, direct evaporation
of ponded water and river routing. The research team is comprised of partners with expertise in
land-hydrology, land-atmosphere interaction, NWP and Earth system process understanding,
modeling and validation on time scales ranging from diurnal to seasonal. Results from this
development-to-demonstration project (readiness levels from 2 to 4) will advance the current
efforts in modeling areas that benefit from improved terrestrial hydrometeorology higher within
the research-to-operations paradigm, helping to set the stage for future operations with fullycoupled Earth system models, namely the UFS for NWP and S2S time scales.
The technology outcome is a coupled Earth system with enhanced interactive land-hydrology components at the Development-to-Demonstration Readiness Level (2-4), for a complete water cycle (ocean to atmosphere to land to hydrology to ocean), that is, the Unified Forecast System (UFS) with Noah-MP plus key capabilities from the WRF-hydro land-hydrology model.
Specifically:
Substantive progress toward development of the next-generation integrated land-hydrology system in UFS, appropriate for coupling with atmosphere and ocean component models that will ultimately include a fully-coupled ESM capability, improving prediction of precipitation, temperature and other variables.
A framework for assessing scale-dependencies of land surface flux representation in these models and how to best connect this higher-resolution land-hydrology system with a coarser atmospheric model to exploit predictability in the land surface.
A physically consistent process representation and calibration of land and hydrology in weather, climate and hydrological prediction, applicable on NWP to S2S time scales.
N/A
N/A
National Center for Atmospheric Research (NCAR),
George Mason University
integrated surface physics for coupled hydrometeorology in the ufs for s2s prediction, extend a high resolution hydrologically enhanced land hydrology modeling system to the global domain, given the documented importance of the terrestrial system in weather and climate prediction for subseasonal to seasonal time scales this project will extend a high resolution hydrologicallyenhanced land hydrology modeling system to the global domain in doing so this work will directly leverage prior work that has been completed which connects the current noaa national water model configuration of the noahmp wrf hydro model with the emerging noaa ncep unified forecast system ufs and assesses the impact of this land hydrology system in 2 week to 3 month predictions which will ultimately include other earth system components ocean seaice etc the proposed work represents a significant improvement in land surface model sophistication land surface hydrology representation and the representation of terrestrial processes interacting with the atmosphere all aimed toward improved operational forecast skill and model fidelity on a spectrum of time scales core hydrologic enhancements to the land model will include subsurface lateral routing surface water ponding and inundation direct evaporation of ponded water and river routing the research team is comprised of partners with expertise in land hydrology land atmosphere interaction nwp and earth system process understanding modeling and validation on time scales ranging from diurnal to seasonal results from this development to demonstration project readiness levels from 2 to 4 will advance the current efforts in modeling areas that benefit from improved terrestrial hydrometeorology higher within the research to operations paradigm helping to set the stage for future operations with fullycoupled earth system models namely the ufs for nwp and s2s time scales
Not Applicable (N/A)
National Center for Atmospheric Research (NCAR), George Mason University
Extend a high-resolution, hydrologically-
enhanced land-hydrology modeling system to the global domain.
2
Identifying efficient perturbations for initializing subseasonal-to-seasonal ensemble forecasts with the UFS
Penny
S2S
search_activity
2023
Level 3
Determine the potential for an eigenfunctions-based initial model perturbations approach in a more realistic setting by scaling to higher resolutions, introducing model errors, and then testing its effectiveness when applied to the NOAA Unified Forecast System (UFS) coupled model.
Stephen Penny, Arun Kumar, Stephane Vannitsem, Jonathan Demaeyer
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting, Forecast Skill Metrics
S2S
Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
A recent breakthrough discovery has been made by the PI and collaborators of this proposal that indicates that ensemble prediction skill scores (which combine prediction accuracy and uncertainty) can be significantly improved at timescales of 1-8 weeks by ensuring that the initial ensemble perturbations project onto the oscillating eigenfunctions of the Koopman (KM) and Perron-Frobenius (PF) operators. These eigenfunctions provide the basis for a reduced-order representation, specifically for coupled atmosphere-ocean dynamics, of the key probability distributions related to subseasonal variability that would otherwise need to be estimated using a very large ensemble forecast. Unlike the typical perturbations used for medium-range ensembles, which emphasize mid-latitude synoptic and sub-synoptic variability, these perturbations form a compact representation of the forecast probability distribution itself as it evolves in time. One of the most exciting features of this finding is that these eigenfunctions are not simply theoretical idealized structures, but can be estimated directly from data by using an algorithm called dynamic mode decomposition (DMD), which is closely related to the Linear Inverse Model (LIM) already being used experimentally for prediction at the NOAA Climate Prediction Center (CPC). To date, we have demonstrated this method using a low-order coupled atmosphere-ocean quasi-geostrophic (QG) model. The proposed project will determine the potential for this approach in a more realistic setting by scaling to higher resolutions, introducing model errors, and then testing its effectiveness when applied to the NOAA Unified Forecast System (UFS) coupled model. Practical aspects of applying the method in an operational context will be investigated by incorporating this approach in a simplified cycled coupled data assimilation application, first with the coupled QG model and then with a low-resolution UFS coupled model. This activity supports NOAA by addressing the need for initialization methods for the NOAA UFS coupled model, specifically for forecasts targeting subseasonal-to-seasonal (S2S) timescales. The outcome of this work will be a proof-of-concept and evaluation of the proposed initialization technique applied to the UFS at low resolutions, comparing the forecast skill using conventional initialization versus using a projection onto the approximate KM and PF eigenfunctions. The methods and outcomes of this work will be described in detail in published peer-reviewed journal articles. This work will validate the approach and prepare prototype software for a future transition to operational-scale testing and implementation, where the ultimate goal is to improve the performance of operational S2S forecasts provided by NOAA to the general public.
A comprehensive initialization strategy for the Unified Forecast System (UFS) coupled atmosphere-ocean numerical forecast model will ultimately improve forecast capabilities at S2S time scales that will better inform all public and private sector decision makers that require information at these lead times.
N/A
N/A
Sofar Ocean Technologies, NOAA CPC, RMI-Belgium
NA23OAR4590196,
(old NA22OAR4590510)
Relevant to All Hazards
California
Subseasonal to Seasonal Program (S2S)
August 2023 - July 2026
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting, Forecast Skill Metrics
identifying efficient perturbations for initializing subseasonal to seasonal ensemble forecasts with the ufs, determine the potential for an eigenfunctions based initial model perturbations approach in a more realistic setting by scaling to higher resolutions introducing model errors and then testing its effectiveness when applied to the noaa unified forecast system ufs coupled model, a recent breakthrough discovery has been made by the pi and collaborators of this proposal that indicates that ensemble prediction skill scores which combine prediction accuracy and uncertainty can be significantly improved at timescales of 1 8 weeks by ensuring that the initial ensemble perturbations project onto the oscillating eigenfunctions of the koopman km and perron frobenius pf operators these eigenfunctions provide the basis for a reduced order representation specifically for coupled atmosphere ocean dynamics of the key probability distributions related to subseasonal variability that would otherwise need to be estimated using a very large ensemble forecast unlike the typical perturbations used for medium range ensembles which emphasize mid latitude synoptic and sub synoptic variability these perturbations form a compact representation of the forecast probability distribution itself as it evolves in time one of the most exciting features of this finding is that these eigenfunctions are not simply theoretical idealized structures but can be estimated directly from data by using an algorithm called dynamic mode decomposition dmd which is closely related to the linear inverse model lim already being used experimentally for prediction at the noaa climate prediction center cpc to date we have demonstrated this method using a low order coupled atmosphere ocean quasi geostrophic qg model the proposed project will determine the potential for this approach in a more realistic setting by scaling to higher resolutions introducing model errors and then testing its effectiveness when applied to the noaa unified forecast system ufs coupled model practical aspects of applying the method in an operational context will be investigated by incorporating this approach in a simplified cycled coupled data assimilation application first with the coupled qg model and then with a low resolution ufs coupled model this activity supports noaa by addressing the need for initialization methods for the noaa ufs coupled model specifically for forecasts targeting subseasonal to seasonal s2s timescales the outcome of this work will be a proof of concept and evaluation of the proposed initialization technique applied to the ufs at low resolutions comparing the forecast skill using conventional initialization versus using a projection onto the approximate km and pf eigenfunctions the methods and outcomes of this work will be described in detail in published peer reviewed journal articles this work will validate the approach and prepare prototype software for a future transition to operational scale testing and implementation where the ultimate goal is to improve the performance of operational s2s forecasts provided by noaa to the general public
Not Applicable (N/A)
Sofar Ocean Technologies, NOAA CPC, RMI-Belgium
Determine the potential for an eigenfunctions-based initial model perturbations approach in a more realistic setting by scaling to higher resolutions, introducing model errors, and then testing its effectiveness when applied to the NOAA Unified Forecast
3
Advancing UFS forecast model evaluation and improvement for S2S
hydrometeorological prediction in the Western States
Newman
S2S
search_activity
2023
Level 3
We propose to improve
the UFS land model and land-atmosphere interactions to improve precipitation and surface
hydrology (snowpack, near surface soil moisture, and streamflow) forecasts across the Western
US at the weeks 2-5 subseasonal scale.
Andrew Newman , Yifan Cheng, Kathryn Newman, Ethan Gutmann, Andrew Wood, Arezoo Rafieei Nasab, Ross Mower, David Gochis, Andrew Bennett
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Verification & Validation, Water in the West
INNOVATIONS
Climate Forecast System (CFS), Coupled Forecast System (CFSv2), Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
Extratropical cyclones give rise to most of the high impact weather in the mid- to high- latitudes, including heavy precipitation and strong winds. In particular, the Alaskan coastal region is frequently affected by strong cyclones over the Gulf of Alaska, the Bering Sea, and the Chukchi Sea. Thus it is important for many stakeholders, including emergency management, water resource management, transportation, and utilities, to name a few, to be warned of approaching periods of increased or decreased potential of storm activities. While individual cyclone tracks can be predicted out to about a week or so, from week 2 on, individual tracks become less predictable, and statistics summarizing cyclone activity, or storminess, are more useful instead. Currently, NOAA Climate Prediction Center (CPC) does not provide guidance on subseasonal variability of storminess. Recent studies by the PIs have shown that NCEP CFS and GEFS ensemble forecasts exhibit significant skills in predicting subseasonal variability of storminess in the Alaska region. This project will extend results from these studies to develop and further assess subseasonal storminess guidance products for the Alaska region.
Storminess can be defined based on Lagrangian cyclone tracking or by Eulerian variance statistics. We propose using a combination of both methods. Lagrangian cyclone tracks provide information about where cyclones pass through and is more intuitive to users, while Eulerian variance statistics has been shown to be more predictable and highly correlated with cyclone related weather. Forecast tracks can be displayed on maps while track statistics (frequency, amplitude, accumulated cyclone track activity which is a combination of track frequency and amplitude) can be accumulated at each grid point to more quantitatively assess variations in cyclone activity. We will also make use of sea level pressure variance which can be computed at each grid point as a complementary metric to quantify cyclone activity. Cyclone related weather impacts (precipitation and high wind anomalies) will be extracted from the forecast ensemble to inform stakeholders of the potential impacts of the predicted storminess variations. These statistically post-processed model predictions will be fully assessed by comparing model hindcasts with observations. The developed tools will be tested for near real-time monitoring and forecast. A publicly accessible web page will be developed to display the subseasonal predictions in real time. The web page will also contain climatological information as well as information on forecast verification to enable users to make more informed use of the forecasts.
Hydrometeorological extremes threaten our interconnected food, water, and energy security and currently cause hundreds of deaths and billions of US dollars in damage annually. Improving our predictions of such events at subseasonal timescales of weeks 2-5 offers an opportunity to enhance our early warning planning to minimize extreme event impacts. We propose to improve the UFS land model and land-atmosphere interactions to improve precipitation and surface hydrology (snowpack, near surface soil moisture, and streamflow) forecasts across the Western US at the weeks 2-5 subseasonal scale. We will do this by leveraging novel statistical evaluation, model optimization techniques and the Hierarchical System Development (HSD) paradigm for the UFS. Thus, our proposal responds to the Innovations for Community Modeling - Priority Area II: Western States Hydrology, S2SIC-1: 'Community-based approaches to improve Earth system models via development and evaluation of individual model components and coupling within the community-based UFS S2S prototypes crucial for improved prediction of western states hydrometeorology.' Our primary products will consist of peer-reviewed publications, method documentation, guidance and evaluation presentations to NOAA/NWS/NCEP/EMC and the scientific community, and public releases of data and analysis code. Our publications will document process level coupling biases within and between Noah-Multiparameterization (Noah-MP) and the atmospheric model, with connections back to forecast errors for UFS S2S configurations; and describe our Noah-MP optimization methodology. We will document Noah-MP configurations and their performance across increasing model complexity ranging from land-only to regional land-atmosphere reforecast simulations; release Noah-MP model improvements and other project code to GitHub; and release regional reforecast datasets generated during the project on the NCAR Research Data Archive. Finally, we will provide recommendations on Noah-MP configurations and enhancements to NCEP/EMC. This project will impact the greater weather and land modeling enterprise. We expect to demonstrate improvements to UFS subseasonal precipitation (primarily spring and summer precipitation) and year-round hydrology forecast skill across the Western US. We will also demonstrate deeper process level understanding within Noah-MP and land-atmosphere coupling, including improved understanding of causality within the coupled model, that is, answering 'why?'. The primary target for the forecast innovations is the NOAA/NWS/NCEP/EMC land group, with Noah-MP improvements advancing the greater land-modeling community. Improved weeks 2-5 UFS forecast skill benefits the Climate Prediction Center (CPC) and all users of NOAA forecasts, bettering the nation's economy and individual's livelihoods. More broadly, our publicly available code, documentation, and reforecast datasets will be readily adoptable by the UFS community. Our methods are applicable to other parts of the UFS (e.g., diagnosing process coupling biases and causality within the atmospheric model). We also plan to work with the METplus team to identify future hydrologic, land-atmosphere coupling, and additional information theory metric implementation into METplus. We request $499.6K in year 1 and $489K in year 2, for a total of $988.6K.
N/A
N/A
National Center for Atmospheric Research, University of Arizona
NA23OAR4590381, NA23OAR4590382
Water Extremes
Colorado
Subseasonal to Seasonal Program (S2S)
August 2023 - July 2025
Data Assimilation and Ensembles, Dynamics and Nesting, Model & Forecast Guidance, Verification & Validation, Water in the West
advancing ufs forecast model evaluation and improvement for s2s hydrometeorological prediction in the western states, we propose to improve the ufs land model and land atmosphere interactions to improve precipitation and surface hydrology snowpack near surface soil moisture and streamflow forecasts across the western us at the weeks 2 5 subseasonal scale, extratropical cyclones give rise to most of the high impact weather in the mid to high latitudes including heavy precipitation and strong winds in particular the alaskan coastal region is frequently affected by strong cyclones over the gulf of alaska the bering sea and the chukchi sea thus it is important for many stakeholders including emergency management water resource management transportation and utilities to name a few to be warned of approaching periods of increased or decreased potential of storm activities while individual cyclone tracks can be predicted out to about a week or so from week 2 on individual tracks become less predictable and statistics summarizing cyclone activity or storminess are more useful instead currently noaa climate prediction center cpc does not provide guidance on subseasonal variability of storminess recent studies by the pis have shown that ncep cfs and gefs ensemble forecasts exhibit significant skills in predicting subseasonal variability of storminess in the alaska region this project will extend results from these studies to develop and further assess subseasonal storminess guidance products for the alaska region storminess can be defined based on lagrangian cyclone tracking or by eulerian variance statistics we propose using a combination of both methods lagrangian cyclone tracks provide information about where cyclones pass through and is more intuitive to users while eulerian variance statistics has been shown to be more predictable and highly correlated with cyclone related weather forecast tracks can be displayed on maps while track statistics frequency amplitude accumulated cyclone track activity which is a combination of track frequency and amplitude can be accumulated at each grid point to more quantitatively assess variations in cyclone activity we will also make use of sea level pressure variance which can be computed at each grid point as a complementary metric to quantify cyclone activity cyclone related weather impacts precipitation and high wind anomalies will be extracted from the forecast ensemble to inform stakeholders of the potential impacts of the predicted storminess variations these statistically post processed model predictions will be fully assessed by comparing model hindcasts with observations the developed tools will be tested for near real time monitoring and forecast a publicly accessible web page will be developed to display the subseasonal predictions in real time the web page will also contain climatological information as well as information on forecast verification to enable users to make more informed use of the forecasts
Not Applicable (N/A)
National Center for Atmospheric Research, University of Arizona
We propose to improve
the UFS land model and land-atmosphere interactions to improve precipitation and surface
hydrology (snowpack, near surface soil moisture, and streamflow) forecasts across the Western
US at the weeks 2-5 subseasonal scale.
0
Exploring concerted integration of alternative snow parameterization and novel interactive vegetation phenology to improve UFS S2S predictions of snowpack and drought termination over the western US
Zhang
S2S
search_activity
2023
Level 3
1: Enhance Noah-MP, 2: Develop ML-based prognostic LAI forecast model to interact with UFS, 3: Discern impacts of these enhancements on UFS S2S model w/r/t predicting US hydrometeorological events
Yu Zhang, Lixin Lu
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Coupled Models & Techniques, Dynamics and Nesting, Forecast Skill Metrics, Parameterization, Water in the West
INNOVATIONS
Noah Multiparameterization Land Surface Model (Noah-MP LSM), Unified Forecast System (UFS), UFS-Season to Subseasonal (UFS-S2S)
Over the past decade alone, the western and southwestern US experienced intense droughts and devastating floods that have profound economic consequences for the nation. Accurate predictions of these events at subseasonal-to-seasonal (S2S) scale have far reaching implications for water management over these regions, and yet have been challenging to attain. Of the many contributors to the lack of S2S forecast skills, inadequacy in the representation of surface conditions by land surface models (LSMs), as well as its downstream impacts, have yet to gain adequate attention. Past research by the project team on Noah-MP, the designated LSM for UFS, has underscored several critical deficiencies, including artificially fast snow sublimation and melt, and timing errors in the seasonal cycle in vegetation phenology and soil water uptake. The former leads to persistent underestimation of snowpack, and the latter distorts root-zone soil moisture dynamics, evapotranspiration, and moisture recycling. In aggregate, these limitations may hinder the ability of coupled models to realistically capture the land-atmospheric interactions and thereby limit their skills to predict hydrometeorological extremes at the S2S range. The overarching goals of the proposed project are threefold. The first is to introduce enhancements to Noah-MP to remedy the key deficiencies of Noah-MP LSM identified by the UTA team. The second is to develop a novel, machine-learning based, prognostic LAI forecast model which can be two-way interactive with UFS forecast model in lieu of UFS prescribed static LAI distributions (not changing between years) or dynamic vegetation model. The third is to discern the individual and aggregate impacts of these enhancements on the skills of UFS S2S model for predicting major hydrometerological events over the western US.
The proposed initiative will benefit NOAA and water enterprise over the western US in the following respects. First, the introduction of new snow parameterization schemes has the potential to improve S2S forecasts of snowpack, and springtime runoff for states heavily reliant on snowmelt runoff. Second, the hindcast experiments, to be conducted for two recent episodes for which operational models performed poorly, will help determine the predictability of these events that can be gleaned through the improvements. The outcome will guide the official configuration of the UFS S2S forecast system, and any improvements to S2S forecasts will ultimately contribute to proactive water resources planning.
N/A
N/A
University of Texas Arlington,
Colorado State University
NA23OAR4590385, NA23OAR4590386
Water Extremes
Colorado, Texas
Subseasonal to Seasonal Program (S2S)
August 2023 - July 2025
Artificial Intelligence (AI) / Machine Learning (ML), Coupled Models & Techniques, Dynamics and Nesting, Forecast Skill Metrics, Parameterization, Water in the West
exploring concerted integration of alternative snow parameterization and novel interactive vegetation phenology to improve ufs s2s predictions of snowpack and drought termination over the western us, 1 enhance noah mp 2 develop ml based prognostic lai forecast model to interact with ufs 3 discern impacts of these enhancements on ufs s2s model w r t predicting us hydrometeorological events, over the past decade alone the western and southwestern us experienced intense droughts and devastating floods that have profound economic consequences for the nation accurate predictions of these events at subseasonal to seasonal s2s scale have far reaching implications for water management over these regions and yet have been challenging to attain of the many contributors to the lack of s2s forecast skills inadequacy in the representation of surface conditions by land surface models lsms as well as its downstream impacts have yet to gain adequate attention past research by the project team on noah mp the designated lsm for ufs has underscored several critical deficiencies including artificially fast snow sublimation and melt and timing errors in the seasonal cycle in vegetation phenology and soil water uptake the former leads to persistent underestimation of snowpack and the latter distorts root zone soil moisture dynamics evapotranspiration and moisture recycling in aggregate these limitations may hinder the ability of coupled models to realistically capture the land atmospheric interactions and thereby limit their skills to predict hydrometeorological extremes at the s2s range the overarching goals of the proposed project are threefold the first is to introduce enhancements to noah mp to remedy the key deficiencies of noah mp lsm identified by the uta team the second is to develop a novel machine learning based prognostic lai forecast model which can be two way interactive with ufs forecast model in lieu of ufs prescribed static lai distributions not changing between years or dynamic vegetation model the third is to discern the individual and aggregate impacts of these enhancements on the skills of ufs s2s model for predicting major hydrometerological events over the western us
Not Applicable (N/A)
University of Texas Arlington, Colorado State University
1: Enhance Noah-MP, 2: Develop ML-based prognostic LAI forecast model to interact with UFS, 3: Discern impacts of these enhancements on UFS S2S model w/r/t predicting US hydrometeorological events
0
Use of Analysis Increments to Advance S2S Predictions of Western States Precipitation Through Targeted Process-Level Improvement of the MJO in the Next-Generation NOAA GEFS
Tulich
S2S
search_activity
2023
Level 3
Guide targeted refinements in the treatment
of unresolved moist processes that minimize the GEFSv13's short-term MJO prediction errors.
Stefan Tulich, Jian-Web Bao, Lisa Bengtsson, Phil Pegion
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC), Reanalysis & Reforecasting, Teleconnections
INNOVATIONS
Global Ensemble Forecast System (GEFS), Global Forecast System (GFS), Unified Forecast System (UFS)
The challenge of reliably predicting precipitation patterns and their extremes across the western US at lead times beyond two weeks is of obvious interest to stakeholders in the region. A major obstacle in this regard, however, is the inability of current operational models to faithfully capture known sources of potential prediction skill. These sources include the 30-60-day Madden-Julian Oscillation (MJO), whose poor simulation in the NOAA Global Forecast System version 15 (GFSv15) has been linked to errors in that model's forecasts of precipitation along the US West Coast at Weeks 3-4. Here we propose to address this issue in the context of the next- generation NOAA Global Ensemble Forecast System version 13 (GEFSv13), which is currently under development and will be a fully coupled atmosphere-ocean model, unlike its predecessor. The idea is to take advantage of the "replay" capability of the model, which enables a relatively inexpensive form of cycled data assimilation, where the analysis increments are calculated as the difference between the model's first-guess (6-hr) forecast and a given reanalysis product. The innovation will be to use the analysis increments to guide targeted refinements in the treatment of unresolved moist processes that minimize the model's short-term MJO prediction errors. Specific processes that will be targeted include the evaporation of convective rain unsaturated air, as well as the entrainment of unsaturated air in convective updrafts, both of which are important for determining how convection and moisture interact with one another. Completion of this model optimization step will set the stage for more standard S2S hindcast experiments, to evaluate impacts on coupled predictions of the MJO and its associated teleconnection patterns affecting US West Coast precipitation at Weeks 3-4. The proposal fits well within the scope of the Western States Hydrology Program of the FY23 NOAA/OAR/WPO Innovations for Community Modeling Competition. In particular, by using analysis increments to advance S2S predictions of western states precipitation through the targeted process-level improvement of the MJO in the prototype coupled GEFS, the project will "identify and address the sources of model biases" in order to "better harness predictability sources" that are "most relevant in the Western States Hydrology program". The total requested funds for the project are $581,354.00, which will provide partial salary support for two university research scientists.
The main question that will be addressed in this final stage of the project is whether the steps
taken in Stage 2 to optimize the GEFSv13 in terms of its first-guess MJO prediction skill lead to
any tangible benefits at longer leads, including in terms of predicting precipitation patterns over
the western US at Weeks 3-4? To address this question, we will use the optimized version of the
model to repeat the 35-day ensemble hindcast integrations of the MJO from Stage 1. Changes in
model performance will be assessed using not only measures of skill relevant to the Western
States Hydrology Program, but also those of more general importance, such as the 500-mb
anomaly pattern correlation in the midlatitudes. In cases where results are negative, we will look
at when problems first start to develop and try to relate these problems back to changes in the
physics that were seen to be beneficial at the start of the hindcast. Regardless of the outcome, we
believe that the findings of this innovative project will serve to identify best practices for
improving the next-generation coupled UFS in terms of its ability to simulate known sources of
S2S prediction skill.
N/A
N/A
CIRES/University of Colorado Boulder,
NOAA PSL
NA23OAR4590389
Water Extremes
Colorado
Subseasonal to Seasonal Program (S2S)
August 2023 - July 2025
Coupled Models & Techniques, Data Assimilation and Ensembles, Earth Prediction Innovation Center (EPIC), Reanalysis & Reforecasting, Teleconnections
use of analysis increments to advance s2s predictions of western states precipitation through targeted process level improvement of the mjo in the next generation noaa gefs, guide targeted refinements in the treatment of unresolved moist processes that minimize the gefsv13's short term mjo prediction errors, the challenge of reliably predicting precipitation patterns and their extremes across the western us at lead times beyond two weeks is of obvious interest to stakeholders in the region a major obstacle in this regard however is the inability of current operational models to faithfully capture known sources of potential prediction skill these sources include the 30 60 day madden julian oscillation mjo whose poor simulation in the noaa global forecast system version 15 gfsv15 has been linked to errors in that model's forecasts of precipitation along the us west coast at weeks 3-4 here we propose to address this issue in the context of the next generation noaa global ensemble forecast system version 13 gefsv13 which is currently under development and will be a fully coupled atmosphere ocean model unlike its predecessor the idea is to take advantage of the "replay" capability of the model which enables a relatively inexpensive form of cycled data assimilation where the analysis increments are calculated as the difference between the model's first guess 6 hr forecast and a given reanalysis product the innovation will be to use the analysis increments to guide targeted refinements in the treatment of unresolved moist processes that minimize the model's short term mjo prediction errors specific processes that will be targeted include the evaporation of convective rain unsaturated air as well as the entrainment of unsaturated air in convective updrafts both of which are important for determining how convection and moisture interact with one another completion of this model optimization step will set the stage for more standard s2s hindcast experiments to evaluate impacts on coupled predictions of the mjo and its associated teleconnection patterns affecting us west coast precipitation at weeks 3-4 the proposal fits well within the scope of the western states hydrology program of the fy23 noaa oar wpo innovations for community modeling competition in particular by using analysis increments to advance s2s predictions of western states precipitation through the targeted process level improvement of the mjo in the prototype coupled gefs the project will "identify and address the sources of model biases" in order to "better harness predictability sources" that are "most relevant in the western states hydrology program" the total requested funds for the project are $581 354 00 which will provide partial salary support for two university research scientists
Not Applicable (N/A)
CIRES/University of Colorado Boulder, NOAA PSL
Guide targeted refinements in the treatment
of unresolved moist processes that minimize the GEFSv13's short-term MJO prediction errors.
0
Impact of Stochastic Parameterizations on UFS Biases and Seasonal Forecast Errors
Sardeshmukh
S2S
search_activity
2023
Level 3
1: generate additional seasonal forecast ensembles with observed initial conditions to investigate the impacts of the stochastic perturbations on initialized seasonal forecast skill. 2: develop a more robust framework of stochastic parameterizations.
Atmospheric Physics, Data Assimilation and Ensembles, Forecast Skill Metrics, Parameterization, Visualizations
INNOVATIONS
Unified Forecast System (UFS)
This project focuses on assessing and reducing UFS biases and seasonal forecast errors by improving the representation
of chaotic physical processes in the atmosphere through stochastic perturbations of the parameterized diabatic tendencies in the FV3GFS model (the Medium-Range-Weather/Subseasonal-to-Seasonal or "MRF/S2S Application" of the UFS). Specifically, we will assess to what extent the impacts of such stochastic perturbations on the model's mean climate, climate variability, and seasonal predictions are beneficial. We will also develop an innovative method to improve the impacts by using perturbations whose space-time covariance statistics are consistent with the geographically non-uniform short-range forecast error statistics of the FV3GFS model
without stochastic perturbations, thus also allowing the perturbation statistics to be geographically non-uniform (in contrast to the uniform statistics specified in most current implementations).
The primary beneficiary of an improved UFS will be the NOAA/NWS Environmental Modeling
Center and all centers that rely on the UFS forecasts for ancillary products. Additionally, many
operational and research centers are struggling with incorporating stochastic physics in their
climate and weather models. The beneficial impacts of the random rP (x) tendencies on reducing
UFS biases and seasonal forecast errors, and also the effectiveness in this regard of using the
approximate rP tendencies, will be of interest to all such members of the modeling community.
The developed methodologies of constraining the covariance statistics of r by the covariance
statistics of short-range forecast errors will be readily applicable to the entire UFS coupled model
state vector and not just the atmospheric state vector investigated in this project.
N/A
N/A
CIRES/University of Colorado Boulder,
NOAA/OAR/PSL
NA23OAR4590376
Water Extremes
Colorado
Subseasonal to Seasonal Program (S2S)
August 2023 - July 2025
Atmospheric Physics, Data Assimilation and Ensembles, Forecast Skill Metrics, Parameterization, Visualizations
impact of stochastic parameterizations on ufs biases and seasonal forecast errors, 1 generate additional seasonal forecast ensembles with observed initial conditions to investigate the impacts of the stochastic perturbations on initialized seasonal forecast skill 2 develop a more robust framework of stochastic parameterizations, this project focuses on assessing and reducing ufs biases and seasonal forecast errors by improving the representation of chaotic physical processes in the atmosphere through stochastic perturbations of the parameterized diabatic tendencies in the fv3gfs model the medium range weather subseasonal to seasonal or "mrf s2s application" of the ufs specifically we will assess to what extent the impacts of such stochastic perturbations on the model's mean climate climate variability and seasonal predictions are beneficial we will also develop an innovative method to improve the impacts by using perturbations whose space time covariance statistics are consistent with the geographically non uniform short range forecast error statistics of the fv3gfs model without stochastic perturbations thus also allowing the perturbation statistics to be geographically non uniform in contrast to the uniform statistics specified in most current implementations
Not Applicable (N/A)
CIRES/University of Colorado Boulder, NOAA/OAR/PSL
1: generate additional seasonal forecast ensembles with observed initial conditions to investigate the impacts of the stochastic perturbations on initialized seasonal forecast skill. 2: develop a more robust framework of stochastic parameterizations.
1
Enhancing the Realism of MOM6-SIS2 Simulations with Ocean Tides
Zaron
S2S
search_activity
2022
Level 3
Advance the capabilities of MOM6-based modeling systems, making them more useful for applications ranging from operational ocean forecasting to coupled earth system reanalysis and prediction. This project expects to achieve centimeter-level accuracy of the main tides in MOM6 and to catch-up with previous efforts based on other high-resolution simulations.
Edward D. Zaron, , Santha Akella, Richard D. Ray, Dimitris Menemenlis, Robert Hallberg, Brian K. Arbic, Eric Chassignet
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Modular Ocean Model (MOM), Unified Forecast System (UFS)
This project aims to enhance the realism of global ocean and sea ice simulations based on the
coupled MOM6-SIS2 ocean modeling system. We propose to run the MOM6-SIS2 system to simulate the joint atmospherically- and tidally-forced ocean circulation, and then refine the resolution
of this system to create skillfully accurate predictions of the barotropic and low-mode baroclinic
tides. Our targeted horizontal grid, a nominal (1/48)-degree tripolar grid, is chosen to capture the
topographic gradients responsible for generating the low-mode baroclinic tides. While this system
represents the state-of-the-art among global modeling components for Earth system prediction, future progress in computing capabilities, numerical methods, and subgrid-scale parameterizations
will supersede MOM6-SIS2 with more capable systems. Our goal is to implement a methodology
for calibrating the resolution-dependent parameterizations of MOM6-SIS2 to yield accurate predictions of barotropic and baroclinic tides, and to enable the efficient calibration of future systems
after MOM6-SIS2 becomes obsolete.
This project has the potential to make significant impacts across the oceanographic research community. For example, in the coming "SWOT era" - in which swath observations of ocean surface topography become available for synergistic combination with other high-resolution ocean observations - high-resolution global ocean simulations will be used to plan new satellite missions, generate testable hypotheses concerning climate dynamics, and envision future scenarios. Simulations with the proposed MOM6-SIS2 system can provide boundary conditions to regional ocean forecast systems developed by the U.S. Navy and other end-users. The funded and unfunded collaborators of this project are ideally positioned to integrate the project outcomes throughout the research community.
N/A
N/A
Oregon State University,
NASA/GSFC,
NASA/JPL,
University of Michigan,
Florida State University
enhancing the realism of mom6 sis2 simulations with ocean tides, advance the capabilities of mom6 based modeling systems making them more useful for applications ranging from operational ocean forecasting to coupled earth system reanalysis and prediction this project expects to achieve centimeter level accuracy of the main tides in mom6 and to catch up with previous efforts based on other high resolution simulations, this project aims to enhance the realism of global ocean and sea ice simulations based on the coupled mom6 sis2 ocean modeling system we propose to run the mom6 sis2 system to simulate the joint atmospherically and tidally forced ocean circulation and then refine the resolution of this system to create skillfully accurate predictions of the barotropic and low mode baroclinic tides our targeted horizontal grid a nominal 1 48 degree tripolar grid is chosen to capture the topographic gradients responsible for generating the low mode baroclinic tides while this system represents the state of the art among global modeling components for earth system prediction future progress in computing capabilities numerical methods and subgrid scale parameterizations will supersede mom6 sis2 with more capable systems our goal is to implement a methodology for calibrating the resolution dependent parameterizations of mom6 sis2 to yield accurate predictions of barotropic and baroclinic tides and to enable the efficient calibration of future systems after mom6 sis2 becomes obsolete
Not Applicable (N/A)
Oregon State University, NASA/GSFC, NASA/JPL, University of Michigan, Florida State University
Advance the capabilities of MOM6-based modeling systems, making them more useful for applications ranging from operational ocean forecasting to coupled earth system reanalysis and prediction. This project expects to achieve centimeter-level accuracy of the main
4
Improving CPC's Empirical Forecast Tools
Halpert
S2S
check_circle
2018
Level 3
This project was developed to refresh and improve CPC's suite of empirical forecast tools in support of the operational seasonal temperature and precipitation outlooks. Key elements that were missing from the legacy tools, largely developed in the 1990s, include probabalistic information and readily-accessible attribution of forecast anomalies to underlying predictors.
Mike Halpert
Complete
Tue May 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Teleconnections
Climate SLA
This project was developed to refresh and improve CPC's suite of empirical forecast tools in support of the operational seasonal temperature and precipitation outlooks. Key elements that were missing from the legacy tools, largely developed in the 1990s, include probabalistic information and readily-accessible attribution of forecast anomalies to underlying predictors.
-Wrap up process to generate probabilistic guidance from CCA and SST-CA.
-Implement experimentally empirical process that isolates long-term trends from decadal and interannual variability.
-Refinement of the canonical correlation process (CCA) to include predictor variables most relevant to the seasonal prediction problem.
-Develop an objec
CPC
N/A
NOAA/NWS/NCEP/CPC
WPO-NWS-SLA 01.FY18.5
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2018 - May 2019
Model & Forecast Guidance, Teleconnections
improving cpc's empirical forecast tools, this project was developed to refresh and improve cpc's suite of empirical forecast tools in support of the operational seasonal temperature and precipitation outlooks key elements that were missing from the legacy tools largely developed in the 1990s include probabalistic information and readily accessible attribution of forecast anomalies to underlying predictors, this project was developed to refresh and improve cpc's suite of empirical forecast tools in support of the operational seasonal temperature and precipitation outlooks key elements that were missing from the legacy tools largely developed in the 1990s include probabalistic information and readily accessible attribution of forecast anomalies to underlying predictors
Climate Prediction Center (CPC)
NOAA/NWS/NCEP/CPC
This project was developed to refresh and improve CPC's suite of empirical forecast tools in support of the operational seasonal temperature and precipitation outlooks. Key elements that were missing from the legacy tools, largely developed
1
Development of a Probabilistic Drought Outlook
Mo
S2S
check_circle
2018
Level 3
At this moment, the Drought Outlook forecasters have information on drought indices such as standardized precipitation indices delivered from precipitation (P) forecasts from the National Multi-Model Ensemble (NMME). They do not have information on uncertainties. We propose to forecast the mean state of drought and the probability for drought to occur in D0 to D4 categories.
Kingtse Mo
Complete
Tue May 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Probabilistic Weather Forecasting
Climate SLA
North American Multi-Model Ensemble (NMME)
At this moment, the Drought Outlook forecasters have information on drought indices such as standardized precipitation indices delivered from precipitation (P) forecasts from the National Multi-Model Ensemble (NMME). They do not have information on uncertainties. We propose to forecast the mean state of drought and the probability for drought to occur in D0 to D4 categories.
Jan-Mar 2018 :
To compute SPIs for all members of the NMME hindcasts initialized from January and July and to obtain the grand mean and verify.
April-June 2018:
To obtain and evaluate the probabilistic hindcasts from the NMME initialized in January and July and verify
July- December 2018:
To obtain and evaluate the probabilistic hindcasts and the grand mean for hindcasts from the NMME initialized in all other months.
January -July 2019:
To obtain data and evaluate the probabilistic hindcasts and the grand mean for hindcasts from the NMME P forecasts from 2012-2016. The scripts and verification schemes can be applied to the real time forecasts
August-November 2019:
To demonstrate the probabilistic drought forecasts in real time
December 2019
The probabilistic drought forecasts will be performed in real time operationally
CPC
N/A
NOAA/NWS/NCEP/CPC
WPO-NWS-SLA 05.FY18.2
Water Extremes
Maryland
Subseasonal to Seasonal Program (S2S)
May 2018 - May 2019
Data Assimilation and Ensembles, Probabilistic Weather Forecasting
development of a probabilistic drought outlook, at this moment the drought outlook forecasters have information on drought indices such as standardized precipitation indices delivered from precipitation p forecasts from the national multi model ensemble nmme they do not have information on uncertainties we propose to forecast the mean state of drought and the probability for drought to occur in d0 to d4 categories, at this moment the drought outlook forecasters have information on drought indices such as standardized precipitation indices delivered from precipitation p forecasts from the national multi model ensemble nmme they do not have information on uncertainties we propose to forecast the mean state of drought and the probability for drought to occur in d0 to d4 categories
Climate Prediction Center (CPC)
NOAA/NWS/NCEP/CPC
At this moment, the Drought Outlook forecasters have information on drought indices such as standardized precipitation indices delivered from precipitation (P) forecasts from the National Multi-Model Ensemble (NMME). They do not have information on uncertainties.
1
Improving Dissemination and Usability of CPC Drought Products and Services via GIS Related Methods.
Updated Tittle: GIS-Based Improvements to CPC Drought Products and Services
Allgood
S2S
check_circle
2018
Level 3
The aim of this proposed work is to document and prioritize the key products and datasets used by CPC's drought outlook forecasters, convert them to GIS-friendly formats, either in-house or provided by the dataset owners, incorporate them into the forecasting interface in an organized fashion, and disseminate any CPC-owned publicly available dataset in this format.
Adam Allgood
Complete
Tue May 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Forecast Product Usability & Design
Climate SLA
USDM
The aim of this proposed work is to document and prioritize the key products and datasets used by CPC's drought outlook forecasters, convert them to GIS-friendly formats, either in-house or provided by the dataset owners, incorporate them into the forecasting interface in an organized fashion, and disseminate any CPC-owned publicly available dataset in this format.
Deliverable 1: A spreadsheet containing the critical tools identified as candidates for conversion into GIS-friendly formats, ranked by priority.
Deliverable 2: A written plan of action for each tool, outlining the necessary steps for completing the transition, and identifying any resource or knowledge gaps.
Deliverable 3: A revised product transition priority list and timeline, accounting for time needed to acquire resources and taking advantage of any identified reusability.
Deliverable 4: A project repository with access for all team members and instructions for use.
Deliverable 5: Scheduled training, if needed, for project participants in order to complete the transition tasks.
Deliverable 6: Creation of the first four top-priority drought outlook tools in GIS-friendly formats, running operationally.
Deliverable 7: A redesigned forecaster ArcGIS interface with layers organized by tool type, time period, or other criteria in a dev environment that includes the newly delivered tools.
Deliverable 8: Feedback from CPC's drought outlook forecasting team regarding the current status of the dev interface.
Deliverable 9: A plan to address the feedback.
Deliverable 10: Migration of the remaining tools into GIS-friendly formats and incorporated into the ArcGIS interface. Product delivery should follow the timeline (Deliverable 3).
Deliverable 11: Implement the new forecaster ArcGIS interface into operations.
Deliverable 12: A version of the project software intended for use by CPC's core partners to convert the publicly available tools and datasets into GIS-friendly formats.
Deliverable 13: Begin exploring the potential for creating blends of related products, or other means of tool consolidation.
CPC
N/A
NOAA/NWS/NCEP/CPC
WPO-NWS-SLA 06.FY18.2
Water Extremes
Maryland
Subseasonal to Seasonal Program (S2S)
May 2018 - May 2019
Forecast Product Usability & Design
improving dissemination and usability of cpc drought products and services via gis related methods updated tittle gis based improvements to cpc drought products and services, the aim of this proposed work is to document and prioritize the key products and datasets used by cpc's drought outlook forecasters convert them to gis friendly formats either in house or provided by the dataset owners incorporate them into the forecasting interface in an organized fashion and disseminate any cpc owned publicly available dataset in this format, the aim of this proposed work is to document and prioritize the key products and datasets used by cpc's drought outlook forecasters convert them to gis friendly formats either in house or provided by the dataset owners incorporate them into the forecasting interface in an organized fashion and disseminate any cpc owned publicly available dataset in this format
Climate Prediction Center (CPC)
NOAA/NWS/NCEP/CPC
The aim of this proposed work is to document and prioritize the key products and datasets used by CPC's drought outlook forecasters, convert them to GIS-friendly formats, either in-house or provided by the dataset owners,
1
Development of Fire Weather Reladed 8-14 Day Forecast Products for CPC
Hamill
S2S
check_circle
2018
Level 3
The objective of this project is the development and subsequent tech transition to CPC of algorithms that provide state-of-the-science, reliable, skillful medium-range (6-10 and 8-14 day) guidance on fire hazards.
Thomas Hamill, Dave Dewitt
Complete
Tue May 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance
Climate SLA
Global Ensemble Forecast System (GEFS), National Fire Danger Rating System (NFDRS)
The objective of this project is the development and subsequent tech transition to CPC of algorithms that provide state-of-the-science, reliable, skillful medium-range (6-10 and 8-14 day) guidance on fire hazards.
Final version of code operational at CPC
CPC
N/A
NOAA/OAR/PSL,
NOAA/NWS/NCEP/CPC,
CIRES/University of Colorado
WPO-NWS-SLA 07.FY18.3
Fire Weather
Colorado, Maryland
Subseasonal to Seasonal Program (S2S)
May 2018 - May 2019
Model & Forecast Guidance
development of fire weather reladed 8 14 day forecast products for cpc, the objective of this project is the development and subsequent tech transition to cpc of algorithms that provide state of the science reliable skillful medium range 6 10 and 8 14 day guidance on fire hazards, the objective of this project is the development and subsequent tech transition to cpc of algorithms that provide state of the science reliable skillful medium range 6 10 and 8 14 day guidance on fire hazards
Climate Prediction Center (CPC)
NOAA/OAR/PSL, NOAA/NWS/NCEP/CPC, CIRES/University of Colorado
The objective of this project is the development and subsequent tech transition to CPC of algorithms that provide state-of-the-science, reliable, skillful medium-range (6-10 and 8-14 day) guidance on fire hazards.
1
Climate Information for Global Heat-Health and Malaria in Africa
Thiaw
S2S
check_circle
2018
Level 3
The objective was to learn more about the health priorities in the countries or sub-regions, to assess current capacity in climate and health, and to work with the Meteorological Services and the Ministries of Health to develop climate products relevant to health early warning.
Wassila Thiaw, David Dewitt
Complete
Tue May 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Community Preparedness & Resilience, Decision Support, Risk Communication, Risk Perception & Response, Social Vulnerability, Societal Impacts & Outcomes
Climate SLA
The objective was to learn more about the health priorities in the countries or sub-regions, to assess current capacity in climate and health, and to work with the Meteorological Services and the Ministries of Health to develop climate products relevant to health early warning.
Heat Health
Q1: Continue to develop heat wave products; work with health institutions identified above and/or experts to validate heat-health information, and present results in the form of a report or a PPT brief.
Q2: Continue to develop heat wave products. Work with international institutions and project team members to test heat-health products globally, including the Caribbean, Central America, South America, and South Asia.
Q3: Continue to develop heat wave products. Continue to work with project members to improve heat-health information and begin posting operational heat-health information on the CPC website. Begin exploring opportunities to develop heat-health early warning systems for target areas.
Q4: Continue to develop heat wave products. Provide access to operational heat wave information, and continue to explore opportunities to develop heat-health early warning systems for target areas. Present results by briefing project team via joint NWS/OAR webinar hosted by NIHHIS.
Malaria
Q1: Work with tea members to refine methodologies for malaria predictions if necessary, assess and present results in the form of a report or a PPT brief.
Q2: Work with team members to run experimental malaria predictions and validate predictions. Present progress in the form of a report or a PPT brief.
Q3: Continue to work with team members in the health sector to run experimental malaria predictions and validate predictions. Present progress in the form of a PPT brief.
Q4: Work with team members to run first real time malaria forecasts. Forecast will be verified in Q1 FY2020. Explore opportunities to develop malaria early warning systems for West Africa and East Africa. Present results in the form of a report or a PPT brief.
CPC
N/A
NOAA/NWS/NCEP/CPC
WPO-NWS-SLA 08.FY18.4
Extreme Temperatures
Maryland
Subseasonal to Seasonal Program (S2S)
May 2018 - May 2019
Community Preparedness & Resilience, Decision Support, Risk Communication, Risk Perception & Response, Social Vulnerability, Societal Impacts & Outcomes
climate information for global heat health and malaria in africa, the objective was to learn more about the health priorities in the countries or sub regions to assess current capacity in climate and health and to work with the meteorological services and the ministries of health to develop climate products relevant to health early warning, the objective was to learn more about the health priorities in the countries or sub regions to assess current capacity in climate and health and to work with the meteorological services and the ministries of health to develop climate products relevant to health early warning
Climate Prediction Center (CPC)
NOAA/NWS/NCEP/CPC
The objective was to learn more about the health priorities in the countries or sub-regions, to assess current capacity in climate and health, and to work with the Meteorological Services and the Ministries of Health
1
Accumulated Winter Season Severity Index (AWSSI) Predictive Capability
Wolf
S2S
check_circle
2018
Level 3
Create a predictive component for the Accumulated Winter Season Severity Index (AWSSI) using Climate Forecast System version 2 (CFSv2) output for weeks 2-4, with access to projections available in tandem with the real-time AWSSI analysis available on the Midwestern Regional Climate Center (MRCC) website (http://mrcc.isws.illinois.edu/research/awssi/indexAwssi.jsp). Create a graphical display, likely to be hosted on the MRCC website and automatically updated daily with most recent CFSv2 output, that includes the AWSSI curve to date and a plume of potential outcomes through the forecast period.
Ray Wolf
Complete
Tue May 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Visualizations, "Risk Categories, Indices, or Levels"
Climate SLA
Coupled Forecast System (CFSv2)
Create a predictive component for the Accumulated Winter Season Severity Index (AWSSI) using Climate Forecast System version 2 (CFSv2) output for weeks 2-4, with access to projections available in tandem with the real-time AWSSI analysis available on the Midwestern Regional Climate Center (MRCC) website (http://mrcc.isws.illinois.edu/research/awssi/indexAwssi.jsp). Create a graphical display, likely to be hosted on the MRCC website and automatically updated daily with most recent CFSv2 output, that includes the AWSSI curve to date and a plume of potential outcomes through the forecast period.
After analyzing results from the winter, the algorithm should be extended to all AWSSI sites. The process should be complete before the start of the 2018 winter season.
CPC
N/A
NWS/Central Region
WPO-NWS-SLA 10.FY18.1
Winter Weather
Missouri
Subseasonal to Seasonal Program (S2S)
May 2018 - May 2019
Visualizations, "Risk Categories, Indices, or Levels"
accumulated winter season severity index awssi predictive capability, create a predictive component for the accumulated winter season severity index awssi using climate forecast system version 2 cfsv2 output for weeks 2 4 with access to projections available in tandem with the real time awssi analysis available on the midwestern regional climate center mrcc website http mrcc isws illinois edu research awssi indexawssi jsp create a graphical display likely to be hosted on the mrcc website and automatically updated daily with most recent cfsv2 output that includes the awssi curve to date and a plume of potential outcomes through the forecast period, create a predictive component for the accumulated winter season severity index awssi using climate forecast system version 2 cfsv2 output for weeks 2 4 with access to projections available in tandem with the real time awssi analysis available on the midwestern regional climate center mrcc website http mrcc isws illinois edu research awssi indexawssi jsp create a graphical display likely to be hosted on the mrcc website and automatically updated daily with most recent cfsv2 output that includes the awssi curve to date and a plume of potential outcomes through the forecast period
Climate Prediction Center (CPC)
NWS/Central Region
Create a predictive component for the Accumulated Winter Season Severity Index (AWSSI) using Climate Forecast System version 2 (CFSv2) output for weeks 2-4, with access to projections available in tandem with the real-time AWSSI analysis
1
Incorporate Coastal Data into LCAT for Regional and Local Decision Support
Churma
S2S
check_circle
2018
Level 3
This project will enable the LCAT to provide regional analyses to address coastal concerns such as trends in flood inundation, storm surges, and tide.
Michael Churma, Stephan B. Smith
Complete
Tue May 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Decision Support
Climate SLA
Local Climate Analysis Tool (LCAT)
This project will enable the LCAT to provide regional analyses to address coastal concerns such as trends in flood inundation, storm surges, and tide.
Milestone (Q1): Requirements specified by Science Advisory Team
Work with LCAT Science Advisory Team (SAT) members routinely to obtain their guidance for implementation of coastal capabilities within LCAT, including preparation for meetings, follow-up actions, and routine communication (multiple agencies/organizations)
Milestone (Q2): LCAT linked with new data sources relevant for coastal phoenomena (NWS/OSTI/MDL, NWS/AFSO, NCEI)
Develop LCAT specification for grid specific and/or areal coverage
Include additional climate variability indices as advised by LCAT SAT
Test coastal-relevant data sets
Milestone (Q3): New LCAT methods implemented for coastal data analysis (NWS/OSTI/MDL - development, NWS/AFSO and SAT -- evaluation)
Evaluate existing LCAT method for applicability in coastal regions.
Test new methods for data analysis as recommended by LCAT SAT
Provide test results on the tested methods to Science Advisory Team for further guidance and feedback.
Develop scientific routines and integrate them into the LCAT user interface
Milestone(Q4): New LCAT routines tested and validated (NFS/AFS, NWS/OSTI/MDL and SAT)
NWS/OSTI/MDL will create and implement regression tests to ensure the stability of the new software.
NWS/AFSO and the SAT will perform tests in coordination with NWS/OSTI/MDL to verify output validity.
Milestone (Q4): Documentation Updated (NWS/OSTI/MDL, NWS/AFSO).
Technical documentation updated by MDL (with focus new data set format and update strategy).
User Guide and other user materials updated by AFSO and OSTI.
Milestone (Q1-Q4): Quarterly reports will be delivered to update progress.
N/A
N/A
NOAA/NWS/MDL
WPO-NWS-SLA 11.FY18.2
Tropical Cyclones
Maryland
Subseasonal to Seasonal Program (S2S)
May 2018 - May 2019
Decision Support
incorporate coastal data into lcat for regional and local decision support, this project will enable the lcat to provide regional analyses to address coastal concerns such as trends in flood inundation storm surges and tide, this project will enable the lcat to provide regional analyses to address coastal concerns such as trends in flood inundation storm surges and tide
Not Applicable (N/A)
NOAA/NWS/MDL
This project will enable the LCAT to provide regional analyses to address coastal concerns such as trends in flood inundation, storm surges, and tide.
1
Application of National Water Model for Drought Monitoring and Nowcasting
Dewitt
S2S
check_circle
2018
Level 3
This project will advance drought monitoring using analyses and predictions of land surface and hydrologic process outputs from the state-of-the-art NWM to calculate soil moisture, evapotranspiration, and runoff.
David Dewitt, Robin Webb, Ed Clark
Complete
Tue May 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Decision Support
Climate SLA
National Water Model (NWM)
This project will advance drought monitoring using analyses and predictions of land surface and hydrologic process outputs from the state-of-the-art NWM to calculate soil moisture, evapotranspiration, and runoff.
The plan for Year-2 is 1) for NIDIS, ESRL/PSD, NWS/CPC, and NWS/OWP to engage drought monitor authors to assess the utility of the experimental drought monitoring and nowcasting service in data sparse and data rich areas, 2) building on feedback received in Year-1, produce and evaluate soil moisture anomaly products in multiple NIDIS pilot basins (Colorado, upper Missouri, ACF), 3) coordinate with National Drought Mitigation Center, NIDIS, and Drought Monitor authors to assess utility of the NWM products in NIDIS pilot basins, 4 ) evaluate streamflow anomaly products as an alternative proxy for drought monitoring pending initial feedback from Drought Monitor authors in Year-1, 5) start work on a prototype system to generate NWM soil moisture anomaly products across CONUS.
N/A
N/A
NOAA/NWS/NCEP/CPC,
NOAA/OAR/PSL,
NOAA/OWP
WPO-NWS-SLA 12.FY18.2
Water Extremes
Alabama, Colorado, Maryland
Subseasonal to Seasonal Program (S2S)
May 2018 - May 2019
Decision Support
application of national water model for drought monitoring and nowcasting, this project will advance drought monitoring using analyses and predictions of land surface and hydrologic process outputs from the state of the art nwm to calculate soil moisture evapotranspiration and runoff, this project will advance drought monitoring using analyses and predictions of land surface and hydrologic process outputs from the state of the art nwm to calculate soil moisture evapotranspiration and runoff
Not Applicable (N/A)
NOAA/NWS/NCEP/CPC, NOAA/OAR/PSL, NOAA/OWP
This project will advance drought monitoring using analyses and predictions of land surface and hydrologic process outputs from the state-of-the-art NWM to calculate soil moisture, evapotranspiration, and runoff.
1
Support real-time climate monitoring at Climate Prediction Center (CPC)
FY17 Tittle: Transition Climate Re-analysis for monitoring
Kumar
S2S
check_circle
2019
Level 3
The scope of the proposed work is to eventually implement an operational climate monitoring reanalysis system based on CORe, i.e., a reanalysis based on conventional data alone.
Arun Kumar
Complete
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, High-Performance Computing (HPC), Reanalysis & Reforecasting
Climate SLA
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Forecast System (GFS), Global Spectral Model (GSM)
The implementation will require (a) completion of reanalysis over the historical period starting from 1950-present, and (b) implementation for its real-time continuation. The data assimilation system that was initially tested as part of the CORe effort was based on the current operational Global Spectral Model (GSM).
FY19/Q1: Test the FV3GFS CORe infrastructure on the HPC research computing (Gaea)
FY19/Q2: Initiate two streams of CORe reanalysis; one in 2000 and one in 2016
FY19/Q3: Initiate two more streams of CORe reanalysis
FY19/Q4: Complete approximately 25-30 years of historical reanalysis based on FV3GFS CORe
FY20/Q2: Complete the reanalysis for the remaining period
FY20/Q3: Port FV3GFS CoRE scripts on NCEP's operational HPC
FY20/Q4: Implement real-time extension of FV3GFS Core on NCEP's operational HPC
FY20/Q4: Assess the performance of CORe against other contemporary reanalysis including CFSR, JRA-55, MERRA2, and ERA5
N/A
N/A
NOAA/NWS/NCEP/CPC
WPO-NWS-SLA 02.FY18.4
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2019 - May 2020
Data Assimilation and Ensembles, High-Performance Computing (HPC), Reanalysis & Reforecasting
support real time climate monitoring at climate prediction center cpc fy17 tittle transition climate re analysis for monitoring, the scope of the proposed work is to eventually implement an operational climate monitoring reanalysis system based on core i e a reanalysis based on conventional data alone, the implementation will require a completion of reanalysis over the historical period starting from 1950 present and b implementation for its real time continuation the data assimilation system that was initially tested as part of the core effort was based on the current operational global spectral model gsm
Not Applicable (N/A)
NOAA/NWS/NCEP/CPC
The scope of the proposed work is to eventually implement an operational climate monitoring reanalysis system based on CORe, i.e., a reanalysis based on conventional data alone.
1
Aviation-centric hazard outlooks for Week 2 that support aviation Decision Support Systems (DSS), especially in planning high impact events.
Updated Title: Extending the temporal range for aviation wind guidance
Rosencrans
S2S
check_circle
2019
Level 3
Wind speed and direction play a critical role in aviation safety and efficiency. Currently, terminal aerodrome forecasts (TAFs) go out as far as 36 hours. Internal to the FAA ATCSCC, National Aviation Meteorologists (NAMs) brief upcoming conditions out to 7 days as part of a PERTI briefing, but there is no official, calibrated, probabilistic wind product covering much of that outlook period. Additionally, CWSU staff, who service the FAA's Core Airports, routinely get asked about upcoming conditions for planning purposes, and would benefit from the creation of a tool that displayed calibrated, probabilistic wind information for planning high traffic, high impact events. Methods used in short-term climate variability might be applicable to generating reliable wind outlooks, improving lead time for wind shifts that require changes to optimal traffic flow.
Matthew Rosencrans
Complete
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Aviation, Model & Forecast Guidance, Probabilistic Weather Forecasting, Decision Support
Climate SLA
Wind speed and direction play a critical role in aviation safety and efficiency. Currently, terminal aerodrome forecasts (TAFs) go out as far as 36 hours. Internal to the FAA ATCSCC, National Aviation Meteorologists (NAMs) brief upcoming conditions out to 7 days as part of a PERTI briefing, but there is no official, calibrated, probabilistic wind product covering much of that outlook period. Additionally, CWSU staff, who service the FAA's Core Airports, routinely get asked about upcoming conditions for planning purposes, and would benefit from the creation of a tool that displayed calibrated, probabilistic wind information for planning high traffic, high impact events. Methods used in short-term climate variability might be applicable to generating reliable wind outlooks, improving lead time for wind shifts that require changes to optimal traffic flow.
FY19Q4:
Meeting with NAMs, CWSU Representatives and FAA partners
Key result: Requirements of an outlook/tool for wind guidance.
Identification and scoping for datasets that could support the requirements
FY20Q1:
Identification and attainment of IT resources
Could stretch to Q2 if resources are exceptional
Gathering of datasets
FY20Q2
Understand skill of forecasts of surface wind parameter probabilities for periods as specified by partners (FAA, CWSU Representatives and NAMs).
Understand raw model skill attributes
Verification metrics
Brier skill score
Relative Operating Characteristics
Heidke skill score of critical thresholds
Calibrated, probabilistic forecasts of surface wind parameter probabilities for periods as specified by partners (FAA, CWSU Representatives and NAMs).
Apply bias correction and calibration post processing to model output, testing various methods to possibly increase skill
Method 1
Verification metrics
Brier skill score
Relative Operating Characteristics
Heidke Skill Score for critical thresholds
Reliability
Grids and Points
FY20Q3:
Calibrated, probabilistic forecasts of surface wind parameter probabilities for periods as specified by partners (FAA, CWSU Representatives and NAMs).
Apply correction and calibration methods to determine the best method.
Method 2
Verification metrics
Brier skill score
Relative Operating Characteristics
Hiedke Skill Score for critical thresholds
Reliability diagrams
Grids and Points
Intercomparison and methods selection
FY20Q4
Expansion to wind direction
Refinement and operationalization of codes to make the wind speed product/tool
FY21
Refinement and operationalization of code to make the wind direction product tool
Display tool that meets user requirements as identified in FY19, contingent on FY20 skill assessments.
Potential expansion to winds in the vertical, up to 15k feet.
N/A
N/A
NOAA/NWS/NCEP/CPC
WPO-NWS-SLA 02.FY19.2
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2019 - May 2020
Aviation, Model & Forecast Guidance, Probabilistic Weather Forecasting, Decision Support
aviation centric hazard outlooks for week 2 that support aviation decision support systems dss especially in planning high impact events updated title extending the temporal range for aviation wind guidance, wind speed and direction play a critical role in aviation safety and efficiency currently terminal aerodrome forecasts tafs go out as far as 36 hours internal to the faa atcscc national aviation meteorologists nams brief upcoming conditions out to 7 days as part of a perti briefing but there is no official calibrated probabilistic wind product covering much of that outlook period additionally cwsu staff who service the faa's core airports routinely get asked about upcoming conditions for planning purposes and would benefit from the creation of a tool that displayed calibrated probabilistic wind information for planning high traffic high impact events methods used in short term climate variability might be applicable to generating reliable wind outlooks improving lead time for wind shifts that require changes to optimal traffic flow, wind speed and direction play a critical role in aviation safety and efficiency currently terminal aerodrome forecasts tafs go out as far as 36 hours internal to the faa atcscc national aviation meteorologists nams brief upcoming conditions out to 7 days as part of a perti briefing but there is no official calibrated probabilistic wind product covering much of that outlook period additionally cwsu staff who service the faa's core airports routinely get asked about upcoming conditions for planning purposes and would benefit from the creation of a tool that displayed calibrated probabilistic wind information for planning high traffic high impact events methods used in short term climate variability might be applicable to generating reliable wind outlooks improving lead time for wind shifts that require changes to optimal traffic flow
Not Applicable (N/A)
NOAA/NWS/NCEP/CPC
Wind speed and direction play a critical role in aviation safety and efficiency. Currently, terminal aerodrome forecasts (TAFs) go out as far as 36 hours. Internal to the FAA ATCSCC, National Aviation Meteorologists (NAMs) brief
1
Optimization of operational river models.
Updated Title: Consumptive Use Modeling in the Colorado River Basin for CBRFC
Miller
S2S
check_circle
2019
Level 3
The goal of the proposed work is to leverage the State's modeling efforts to improve the Colorado Basin River Forecast Center (CBRFC) forecasts.
W. Paul Miller
Complete
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Decision Support
Climate SLA
Colorado River Decision Support System (CRDSS)
In the Western United States, understanding and modeling irrigation diversions and the resultant crop water consumption are critical to accurately predicting streamflow and water supplies. As part of Colorado's Decision Support Systems (CDSS), the State has developed a set of generalized modeling tools and a centralized database (HydroBase) to help water managers make timely, informed decisions about historical and future water use. Within the CDSS, the StateMod and StateCU models can be run at a monthly or daily timestep to produce estimates of historical or future water allocation and use. For the Colorado River basin, the Colorado River Decision Support System (CRDSS) has been developed to assist the State in simulating the impacts of water management policies and practices on river flows.
FY19/20 Milestones
Monthly reports detailing progress and summarizing activities accomplished
Development of the CHPS-FEWS stand-along
Implementation of the stand-along CHPS configuration at CBRFC
Task 5 Memorandum
N/A
N/A
NOAA/NWS/Colorado Basin RFC
WPO-NWS-SLA 03.FY19.1
Water Extremes
Utah
Subseasonal to Seasonal Program (S2S)
May 2019 - May 2020
Decision Support
optimization of operational river models updated title consumptive use modeling in the colorado river basin for cbrfc, the goal of the proposed work is to leverage the state's modeling efforts to improve the colorado basin river forecast center cbrfc forecasts, in the western united states understanding and modeling irrigation diversions and the resultant crop water consumption are critical to accurately predicting streamflow and water supplies as part of colorado's decision support systems cdss the state has developed a set of generalized modeling tools and a centralized database hydrobase to help water managers make timely informed decisions about historical and future water use within the cdss the statemod and statecu models can be run at a monthly or daily timestep to produce estimates of historical or future water allocation and use for the colorado river basin the colorado river decision support system crdss has been developed to assist the state in simulating the impacts of water management policies and practices on river flows
Not Applicable (N/A)
NOAA/NWS/Colorado Basin RFC
The goal of the proposed work is to leverage the State's modeling efforts to improve the Colorado Basin River Forecast Center (CBRFC) forecasts.
1
Improved water supply forecast over the 1 to 5 year time.
Updated Title: Understanding and Modeling Variability of Flows in the Upper Colorado River Basin at 2 to 5 Year Timescales for Improved Water Resources Management
Miller
S2S
check_circle
2019
Level 3
To address this problem, understanding the space-time variability of flows at the 2 ~ 5 year time scale to enable skillful projections is crucial. To this end, this research proposes a systematic investigation to answer the following key research questions - (i) What are the drivers of flow at the 2 ~ 5 year time scales?
W. Paul Miller, Balaji Rajagopalan
Complete
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Community Preparedness & Resilience, Societal Impacts & Outcomes
Climate SLA
Efficient management of water resources in the Upper Colorado River Basin (UCRB) requires skillful projections of streamflow in the 'midterm' - i.e. 1 ~ 5 year time scale. There is significant skill in the seasonal forecast at during the first year of the 1 ~ 5 year horizon. Thanks to improved hydrologic and statistical models. However, the projections in the outer years (years 2 ~ 5) are poor. Thus, climatology is typically, substituted for the outer years. During dry years when the reservoirs are lower, using climatological flows, which tend to be higher relative to the current dry period, for outer years, leads to unrealistically optimistic projections of reservoir levels and, consequently sub-optimal operations and planning strategies. This issue is in sharp focus during the current prolonged unprecedented drought since early 21st century. This forms the motivation for our proposal.
FY19/20 Milestones
Quarterly reports will be made available, summarizing recent work and activities, as well as additional goals
We anticipate the publication of research papers detailing the findings of this work.
N/A
N/A
NOAA/NWS/Colorado Basin RFC,
University of Colorado
WPO-NWS-SLA 04.FY19.1
Water Extremes
Colorado, Utah
Subseasonal to Seasonal Program (S2S)
May 2019 - May 2020
Community Preparedness & Resilience, Societal Impacts & Outcomes
improved water supply forecast over the 1 to 5 year time updated title understanding and modeling variability of flows in the upper colorado river basin at 2 to 5 year timescales for improved water resources management, to address this problem understanding the space time variability of flows at the 2 ~ 5 year time scale to enable skillful projections is crucial to this end this research proposes a systematic investigation to answer the following key research questions - i what are the drivers of flow at the 2 ~ 5 year time scales?, efficient management of water resources in the upper colorado river basin ucrb requires skillful projections of streamflow in the 'midterm' - i e 1 ~ 5 year time scale there is significant skill in the seasonal forecast at during the first year of the 1 ~ 5 year horizon thanks to improved hydrologic and statistical models however the projections in the outer years years 2 ~ 5 are poor thus climatology is typically substituted for the outer years during dry years when the reservoirs are lower using climatological flows which tend to be higher relative to the current dry period for outer years leads to unrealistically optimistic projections of reservoir levels and consequently sub optimal operations and planning strategies this issue is in sharp focus during the current prolonged unprecedented drought since early 21st century this forms the motivation for our proposal
Not Applicable (N/A)
NOAA/NWS/Colorado Basin RFC, University of Colorado
To address this problem, understanding the space-time variability of flows at the 2 ~ 5 year time scale to enable skillful projections is crucial. To this end, this research proposes a systematic investigation to answer
1
Improved Statistical Model for Week 3-4 Temperature and Precipitation Outlooks based on linear inverse modeling (LIM).
Updated Title: Improved Guidance for Week 3-4 Temperature and Precipitation Outlooks
Newman
S2S
check_circle
2019
Level 3
This project is specifically aimed at improving skill for CPC Week 3-4 Temperature and Precipitation outlooks by providing improved guidance from the LIM, including a priori identification of forecasts of opportunity. We will provide operational Weeks 3 and 4 guidance by transitioning LIMs that we have constructed from global observations of the past 50 years.
Matthew Newman, Michael Alexander
Complete
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Model & Forecast Guidance
Climate SLA
Linear inverse models (LIMs) are empirical-dynamical models for which a linear approximation of the predictable dynamics of a system (including the linear parameterization of rapidly decorrelating nonlinearities) is constructed from the statistics of the system itself. For S2S time scales, LIMs are typically constructed from weekly anomalies of various atmospheric and oceanic quantities. While LIMs do not capture predictable nonlinearities, they do effectively represent other predictable dynamics, which allows LIM hindcast skill to be comparable (sometimes better, sometimes worse) to that of more sophisticated numerical models. Consequently, LIMs have been used extensively for predictability studies, to benchmark other forecast guidance (e.g., CGCM forecasts), and to make near real-time forecasts, which have been run in real-time experimental mode at PSD. A version of the tropical LIM developed at PSD is currently being used by CPC as part of its model guidance for Weeks 3 and 4 Tropical Hazards assessment.
FY19/20 Milestones
Develop LIM as a probabilistic and deterministic forecast system focused on CONUS
PSD personnel visit CPC to develop better understanding of CPC operational needs and requirements
Test and transition to an operational environment LIM deterministic and probabilistic Week 3-4 temperature forecasts
Participate in NOAA's S2S Task Force
Provide year-1 end progress report providing significant detail on completed activities and anticipated activities for year-2.
N/A
N/A
NOAA/OAR/PSL
WPO-NWS-SLA 05.FY19.3
Relevant to All Hazards
Colorado
Subseasonal to Seasonal Program (S2S)
May 2019 - May 2020
Dynamics and Nesting, Model & Forecast Guidance
improved statistical model for week 3 4 temperature and precipitation outlooks based on linear inverse modeling lim updated title improved guidance for week 3 4 temperature and precipitation outlooks, this project is specifically aimed at improving skill for cpc week 3 4 temperature and precipitation outlooks by providing improved guidance from the lim including a priori identification of forecasts of opportunity we will provide operational weeks 3 and 4 guidance by transitioning lims that we have constructed from global observations of the past 50 years, linear inverse models lims are empirical dynamical models for which a linear approximation of the predictable dynamics of a system including the linear parameterization of rapidly decorrelating nonlinearities is constructed from the statistics of the system itself for s2s time scales lims are typically constructed from weekly anomalies of various atmospheric and oceanic quantities while lims do not capture predictable nonlinearities they do effectively represent other predictable dynamics which allows lim hindcast skill to be comparable sometimes better sometimes worse to that of more sophisticated numerical models consequently lims have been used extensively for predictability studies to benchmark other forecast guidance e g cgcm forecasts and to make near real time forecasts which have been run in real time experimental mode at psd a version of the tropical lim developed at psd is currently being used by cpc as part of its model guidance for weeks 3 and 4 tropical hazards assessment
Not Applicable (N/A)
NOAA/OAR/PSL
This project is specifically aimed at improving skill for CPC Week 3-4 Temperature and Precipitation outlooks by providing improved guidance from the LIM, including a priori identification of forecasts of opportunity. We will provide operational
1
Incorporating Weather Extremes Analysis Capabilities into LCAT
Churma
S2S
check_circle
2019
Level 3
The target capability is to be able to analyze daily ACIS climatological extremes data, their climatological trends, time series analysis, and correlations to various climate variability signals. The NWS LCAT currently features most of these analysis techniques. LCAT also includes capability to link to ACIS data that are currently limited to monthly and seasonal extremes. Thus the NWS LCAT would be the application best suited to analyze the data in the context of those climate signals.
Michael Churma, Stephan B. Smith
Complete
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance
Climate SLA
Local Climate Analysis Tool (LCAT)
NWS field offices use climatological guidance from the Climate Prediction Center (CPC) and National Center for Environmental Information (NCEI) that provides only limited information beyond average temperature and total precipitation. NWS field need to have a tool that would allow them to access climatological statistics at local level, and to analyze the data in the context of climate change and variability. Such information can be obtained from Applied Climate Information System (ACIS), applied in the NWS Local Climate Analysis Tool (LCAT). LCAT currently has a capability to access monthly extremes data for a limited number of parameters.
Milestone (Q1) FY20Q2: Requirements specified by Science Advisory Team
LCAT Science Advisory Team (SAT) will identify specific ACIS data useful for integration into LCAT, and develop requirements based on those findings (NWS/AFSO, leading SAT that comprises of multiple organizations, including NWS/OSTI/MDL)
Milestone (FY20Q3): LCAT linked with additional ACIS data sources as specified by requirements
Implement ACIS data ingest needed to fulfill requirements (NWS/OSTI/MDL)
Perform unit testing to assure consistency and quality of data ingest (NWS/OSTI/MDL, NWS/AFSO).
Milestone (FY20Q4): New LCAT methods implemented for extreme weather data analysis
Evaluate existing LCAT methods for applicability in extreme weather studies (NWS/AFSO).
Develop and test new methods for data analysis as recommended by LCAT SAT (NWS/OSTI/MDL)
Provide test results on the implemented methods to Science Advisory Team for further guidance and feedback (NWS/OSTI/MDL and NWS/AFSO).
Milestone(FY21Q1): New LCAT routines tested and validated (NFS/AFS, NWS/OSTI/MDL and NWS/AFSO)
Conduct regression tests to ensure the stability of the new software (NWS/OSTI/MDL)
Perform tests in coordination to verify output validity (NWS/AFSO and NWS/OSTI/MDL).
Milestone (FY21Q2): Documentation Updated (NWS/OSTI/MDL, NWS/AFSO).
Technical documentation updated by MDL (with focus new data set format and update strategy).
User Guide and other user materials updated (NWS/AFSO and NWS/OSTI/MDL.)
Milestone (FY20Q3-FY21Q2): Quarterly reports delivered to update progress.
Provide written updates, set up meetings, and conduct demonstrations, as specified by NWS/AFSO (NWS/OSTI/MDL).
N/A
N/A
NOAA/NWS/MDL
WPO-NWS-SLA 06.FY19.2
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2019 - May 2020
Model & Forecast Guidance
incorporating weather extremes analysis capabilities into lcat, the target capability is to be able to analyze daily acis climatological extremes data their climatological trends time series analysis and correlations to various climate variability signals the nws lcat currently features most of these analysis techniques lcat also includes capability to link to acis data that are currently limited to monthly and seasonal extremes thus the nws lcat would be the application best suited to analyze the data in the context of those climate signals, nws field offices use climatological guidance from the climate prediction center cpc and national center for environmental information ncei that provides only limited information beyond average temperature and total precipitation nws field need to have a tool that would allow them to access climatological statistics at local level and to analyze the data in the context of climate change and variability such information can be obtained from applied climate information system acis applied in the nws local climate analysis tool lcat lcat currently has a capability to access monthly extremes data for a limited number of parameters
Not Applicable (N/A)
NOAA/NWS/MDL
The target capability is to be able to analyze daily ACIS climatological extremes data, their climatological trends, time series analysis, and correlations to various climate variability signals. The NWS LCAT currently features most of these
1
Regional Enhanced Decision Support Services in the Week 2 Time Frame
Murphy
S2S
check_circle
2019
Level 3
The scope of this low cost project is to develop a test/pilot product to enhance the forecasting of drought development or improvement in the Week 1/Week 2 time period for the states of TX, OK, LA, AR, MS, AL, and TN, using the US Drought Monitor (USDM) as a benchmark. The pilot would then be used to evaluate potential usage nationwide.
Victor Murphy
Complete
Wed May 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Reanalysis & Reforecasting, Decision Support
Climate SLA
Global Ensemble Forecast System (GEFS)
Current NWS Drought Forecast products are subjective in nature (i.e. done manually), and are only done twice per month. A Seasonal Drought Outlook is prepared for a 3 month period on the 3rd Thursday of each month, and a Monthly Drought Outlook is prepared on the last day of each month. Both verify versus the US Drought Monitor (USDM) at the end of the specified time period. Decision makers are in need of a shorter term (i.e. week 1 to week 2) product that is issued at least weekly. Time constraints prevent this from being done manually. However, the existing ESRL GEFSv2 Reforecast Tool produces a deterministic gridded QPF for the day 1-7 and day 8-14 time period. These grids could be overlain on the USDM each week, and compared to climatological normal for the time period. The anomalies would then be used to prepare a weekly week 1 and week 2 forecast for drought improvement, deterioration, or status quo, using the weekly USDM output as a point of reference.
FY19/20 Milestones: End of 4th Quarter of FY19: Prototype of product prepared and ready for operational review. This includes ensuring seamless ingest of ESRL gridded output, and USDM gridded output and the ability to compare the precipitation anomaly between the ESRL gridded output and climatological precipitation totals.
End of 2nd Quarter of FY20: Final product available on the SRCC website for use and review by stakeholders and decision makers.
N/A
N/A
NOAA/NWS/Southern Region
None
Relevant to All Hazards
Texas
Subseasonal to Seasonal Program (S2S)
May 2019 - May 2020
Dynamics and Nesting, Reanalysis & Reforecasting, Decision Support
regional enhanced decision support services in the week 2 time frame, the scope of this low cost project is to develop a test pilot product to enhance the forecasting of drought development or improvement in the week 1 week 2 time period for the states of tx ok la ar ms al and tn using the us drought monitor usdm as a benchmark the pilot would then be used to evaluate potential usage nationwide, current nws drought forecast products are subjective in nature i e done manually and are only done twice per month a seasonal drought outlook is prepared for a 3 month period on the 3rd thursday of each month and a monthly drought outlook is prepared on the last day of each month both verify versus the us drought monitor usdm at the end of the specified time period decision makers are in need of a shorter term i e week 1 to week 2 product that is issued at least weekly time constraints prevent this from being done manually however the existing esrl gefsv2 reforecast tool produces a deterministic gridded qpf for the day 1 7 and day 8 14 time period these grids could be overlain on the usdm each week and compared to climatological normal for the time period the anomalies would then be used to prepare a weekly week 1 and week 2 forecast for drought improvement deterioration or status quo using the weekly usdm output as a point of reference
Not Applicable (N/A)
NOAA/NWS/Southern Region
The scope of this low cost project is to develop a test/pilot product to enhance the forecasting of drought development or improvement in the Week 1/Week 2 time period for the states of TX, OK,
0
Snow Workshop to Improve NOAA's Capabilities in Snow Forecasting
Horsfall
S2S
check_circle
2020
Level 3
The workshop will focus on four snow parameters (see
https://www.weather.gov/media/coop/Snow_Measurement_Guidelines-2014.pdf):
Snowfall: Maximum amount of new snow that has fallen since the previous observation
Snow Depth: The total depth of snow (including ice) on the ground at the normal
observation time; the snow depth includes new snow that has fallen combined with
snow already on the ground
Snowfall Water Content (also known as Water Equivalent): The water content of new
snowfall since the previous day's observation
Snow Depth Water Content (also known as Snow Water Equivalent (SWE)): The water
content of new and old snow on the ground measured by taking a core sample
Fiona Horsfall, Maggie Hurwitz
Complete
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat May 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry
Climate SLA
NOAA's NWS Climate Services Branch, NWS Winter Weather Program, the NWS Climate
Prediction Center (CPC), the OAR Office of Weather and Air Quality (OWAQ), the NWS Office of
Water Prediction (OWP) and the NWS Office of Observations (OBS) jointly propose a snow
workshop, to be held in College Park, MD in March 2020.
Bring together the snow observation and research communities
Review existing observational capabilities and resources, to include NOAA's inventory of
requirements and products (e.g., NOSIA, NOSC)
Identify high-priority NOAA products with unmet observational requirements
Identify existing capabilities, new technologies, and mature research that could meet
these requirements and readily transition to operations
Target snow-related funding solicitations (e.g., FY2021 OAR/OWAQ funding competition
for snow observations, NWS-OAR Service Level Agreement, NWS CSTAR) toward applied
research proposals that leverage new technologies and meet observational
requirements
N/A
N/A
NOAA/NWS/Climate Services Branch
WPO-NWS-SLA 01.FY20.1
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2020 - May 2021
Atmospheric Composition and Chemistry
snow workshop to improve noaa's capabilities in snow forecasting, the workshop will focus on four snow parameters see https www weather gov media coop snow_measurement_guidelines 2014 pdf snowfall maximum amount of new snow that has fallen since the previous observation snow depth the total depth of snow including ice on the ground at the normal observation time the snow depth includes new snow that has fallen combined with snow already on the ground snowfall water content also known as water equivalent the water content of new snowfall since the previous day's observation snow depth water content also known as snow water equivalent swe the water content of new and old snow on the ground measured by taking a core sample, noaa's nws climate services branch nws winter weather program the nws climate prediction center cpc the oar office of weather and air quality owaq the nws office of water prediction owp and the nws office of observations obs jointly propose a snow workshop to be held in college park md in march 2020
Not Applicable (N/A)
NOAA/NWS/Climate Services Branch
The workshop will focus on four snow parameters (see https://www.weather.gov/media/coop/Snow_Measurement_Guidelines-2014.pdf): Snowfall: Maximum amount of new snow that has fallen since the previous observation Snow Depth: The total depth of snow (including ice) on
1
Snow Drought Tracker
Bair
S2S
check_circle
2020
Level 3
We propose to expand upon a Desert Research Institute (DRI)/Western Regional Climate Center
(WRCC) web-tool tool that is currently in the early stages of development. This tool will provide
near real-time snow drought tracking for the western United States and cover all or portions of
Western, Alaska, Central, and Southern NWS regions. This tool will likely be useful in the
mountainous and snow prone areas in the NWS Eastern Region also, though further research
will help clarify this application. Initial funding was provided by a one-year grant from the DRI
Foundation Innovation Research Program, ending mid-November 2019.
Andrea Bair
Complete
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat May 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Climate SLA
Snow drought, defined as below average snowpack for a given time of year, is a phenomenon
that has been of increasing concern in a warming and more variable precipitation climate in the
watersheds of the western US. A significant proportion of the western United States'
population, ecosystems, and agricultural activities relies on snowpack to provide water
resources. Further, there are large areas at risk for snowmelt flooding. There are two types of
snow drought. The first type, dry snow drought, occurs when both precipitation and snowpack
are below normal; precipitation is suppressed due to atmospheric conditions. The other type is
warm snow drought. Warm snow droughts occur when accumulated cold season precipitation
is near normal or above normal, but snow water equivalent (SWE) is well below normal. Such
conditions result from above-normal temperatures and mountain precipitation falling as rain
rather than snow or from mid-winter melt events driven by anomalous warm spells. Snow
drought conditions of either type have implications for water resource and flood management,
as well as recreation and ecosystems. Tracking snow drought conditions in real-time,
specifically warm snow drought, would be beneficial to the NWS and its stakeholders at the
federal, state, and local levels.
Provide extended lead-time information and Impact-based Decision Support Services for
weather and climate extremes for planning and preparedness, including hurricanes,
tornadoes, droughts, floods, extreme heat, winter storms.
Improved NWS capabilities for climate timescale decision support services for water
resources.
N/A
N/A
NOAA/NWS/Western Region
WPO-NWS-SLA 02.FY20.1
Relevant to All Hazards
Utah
Subseasonal to Seasonal Program (S2S)
May 2020 - May 2021
snow drought tracker, we propose to expand upon a desert research institute dri western regional climate center wrcc web tool tool that is currently in the early stages of development this tool will provide near real time snow drought tracking for the western united states and cover all or portions of western alaska central and southern nws regions this tool will likely be useful in the mountainous and snow prone areas in the nws eastern region also though further research will help clarify this application initial funding was provided by a one year grant from the dri foundation innovation research program ending mid november 2019, snow drought defined as below average snowpack for a given time of year is a phenomenon that has been of increasing concern in a warming and more variable precipitation climate in the watersheds of the western us a significant proportion of the western united states' population ecosystems and agricultural activities relies on snowpack to provide water resources further there are large areas at risk for snowmelt flooding there are two types of snow drought the first type dry snow drought occurs when both precipitation and snowpack are below normal precipitation is suppressed due to atmospheric conditions the other type is warm snow drought warm snow droughts occur when accumulated cold season precipitation is near normal or above normal but snow water equivalent swe is well below normal such conditions result from above normal temperatures and mountain precipitation falling as rain rather than snow or from mid winter melt events driven by anomalous warm spells snow drought conditions of either type have implications for water resource and flood management as well as recreation and ecosystems tracking snow drought conditions in real time specifically warm snow drought would be beneficial to the nws and its stakeholders at the federal state and local levels
Not Applicable (N/A)
NOAA/NWS/Western Region
We propose to expand upon a Desert Research Institute (DRI)/Western Regional Climate Center (WRCC) web-tool tool that is currently in the early stages of development. This tool will provide near real-time snow drought tracking for
2
Ocean Hazards Outlook Product for Week 2 and Beyond.
Updated Title: Extending Maritime Hazard Information to Week Two and Beyond
Figurskey
S2S
check_circle
2020
Level 3
To develop wave reforecasts from GEFSv12 and an evaluation of predictive skill from available ensemble forecasts.
Darin Figurskey
Complete
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat May 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Reanalysis & Reforecasting
Climate SLA
Global Ensemble Forecast System (GEFS)
There is no current capability for maritime hazard information for the two-week period and beyond. The science challenge is that we do not know what the predictive skill is for lead times beyond a few days for relevant wind and wave fields available from operational ensemble modeling systems. The proposed future capability is for an oceanic hazards outlook containing delineations of where winds and waves are expected to have the potential of posing a hazard to either life or property for vessels at sea.
A skill assessment, comprised of reforecasts of the GEFS with the WAVEWATCH III model, will be required for a period over the last 15 to 20 years. The model reanalysis will require high temporal frequency. Through data mining, if there is sufficient skill, tools would be developed for forecasters to provide mariners with information on the potential for significant winds and wave heights that would be detrimental to the safety of life at sea. Development of the tools would employ advanced neural-networks and artificial intelligence methods. Vessels traversing the north Atlantic and north Pacific Oceans can take 21 days to navigate between distant ports. Critical economic and life-saving decisions can be made with great situational awareness of upcoming hazards. This would enhance total marine weather services to the private sector, the public and the global community ultimately advancing America's blue economy.
This will include building an inventory of existing applications of probabilistic forecasts in guidance and ocean hazards forecast support. The Ocean Prediction Center and Climate Prediction Center will provide a critical evaluation in consultation with stakeholders to determine if the available skill level would be useful.
N/A
N/A
NOAA/NWS/OPC
WPO-NWS-SLA 03.FY20.3
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2020 - May 2021
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Reanalysis & Reforecasting
ocean hazards outlook product for week 2 and beyond updated title extending maritime hazard information to week two and beyond, to develop wave reforecasts from gefsv12 and an evaluation of predictive skill from available ensemble forecasts, there is no current capability for maritime hazard information for the two week period and beyond the science challenge is that we do not know what the predictive skill is for lead times beyond a few days for relevant wind and wave fields available from operational ensemble modeling systems the proposed future capability is for an oceanic hazards outlook containing delineations of where winds and waves are expected to have the potential of posing a hazard to either life or property for vessels at sea a skill assessment comprised of reforecasts of the gefs with the wavewatch iii model will be required for a period over the last 15 to 20 years the model reanalysis will require high temporal frequency through data mining if there is sufficient skill tools would be developed for forecasters to provide mariners with information on the potential for significant winds and wave heights that would be detrimental to the safety of life at sea development of the tools would employ advanced neural networks and artificial intelligence methods vessels traversing the north atlantic and north pacific oceans can take 21 days to navigate between distant ports critical economic and life saving decisions can be made with great situational awareness of upcoming hazards this would enhance total marine weather services to the private sector the public and the global community ultimately advancing america's blue economy
Not Applicable (N/A)
NOAA/NWS/OPC
To develop wave reforecasts from GEFSv12 and an evaluation of predictive skill from available ensemble forecasts.
1
Probabilistic Sea Ice Guidance in the S2S Time Frame
Petrescu
S2S
check_circle
2020
Level 3
The intent of this project is to provide probabilistic sea ice concentration guidance at specified points. The primary focus will be in the 3-6 week time period, with monthly information through 9 months. This guidance will be derived from available seasonal sea ice and atmospheric dynamic models. The approach to derive the guidance information will build upon existing techniques.
Gene Petrescu, Brian Brettschneider
Complete
Fri May 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat May 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting
Climate SLA
With rapidly changing sea ice conditions in the Bering Sea and Arctic Ocean, the demand on the NWS Alaska is increasing for more specific sea ice guidance to support transportation logistics, subsistence activities, fisheries, and commercial interests. While dynamic coupled seasonal models have shown significant increases in skill forecasting sea ice information in recent years, the guidance still shows significant bias and lacks the detail necessary, especially near the coast to capture near shore impacts.
(FY20Q4). Determine and gather viable data sets to be used to develop the statistical relationships over a training period, and test period for evaluation.
(FY20Q4). In collaboration with NWS Alaska, determine the specific forecast points of interest to NWS stakeholders where guidance is needed.
(FY21Q1). Develop the statistical relationships over a specified training period to develop the probabilistic sea ice concentration guidance.
(FY21Q2). Evaluate the guidance over a test period to determine guidance skill.
(FY21Q2). In collaboration with the NWS, develop a prototype product to provide the probabilistic guidance information to NWS Alaska.
(FY21Q3). In collaboration with the Arctic Testbed and Proving Ground (ATPG), evaluate usefulness of the prototype product and modify products as required.
(FY21Q3). Coordinate with NWS Alaska on the most efficient delivery process of the forecast guidance and transfer any software and data required to support the system to NWS Alaska.
N/A
N/A
NOAA/NWS/Alaska Region
WPO-NWS-SLA 04.FY20.1
Relevant to All Hazards
Alaska
Subseasonal to Seasonal Program (S2S)
May 2020 - May 2021
Coupled Models & Techniques, Dynamics and Nesting, Model & Forecast Guidance, Probabilistic Weather Forecasting
probabilistic sea ice guidance in the s2s time frame, the intent of this project is to provide probabilistic sea ice concentration guidance at specified points the primary focus will be in the 3 6 week time period with monthly information through 9 months this guidance will be derived from available seasonal sea ice and atmospheric dynamic models the approach to derive the guidance information will build upon existing techniques, with rapidly changing sea ice conditions in the bering sea and arctic ocean the demand on the nws alaska is increasing for more specific sea ice guidance to support transportation logistics subsistence activities fisheries and commercial interests while dynamic coupled seasonal models have shown significant increases in skill forecasting sea ice information in recent years the guidance still shows significant bias and lacks the detail necessary especially near the coast to capture near shore impacts
Not Applicable (N/A)
NOAA/NWS/Alaska Region
The intent of this project is to provide probabilistic sea ice concentration guidance at specified points. The primary focus will be in the 3-6 week time period, with monthly information through 9 months. This guidance
1
Routine Monitoring of Climate in the State of Hawai'i: Establishment of State Climate Divisions
Frazier
S2S
check_circle
2021
Level 3
We propose to develop the analytical approach to produce climate divisions for the State of Hawai'i with regional groupings analogous to the CONUS climate division records.
Abby Frazier, Raymond Tanabe
Complete
Sat May 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Climate SLA
Basic climate summaries and historical climate analyses produced by the National Centers for Environmental Information (NCEI) do not include the State of Hawai'i, largely because Hawai'i is the only state that does not have assigned climate divisions. The contiguous United States (CONUS) is separated into 344 climate divisions, while Alaska has an additional 13 which were added to the national data set in 2015.
1.1Project team meeting - refine scope of work
1.2Literature Review and Code Development
1.3Analysis to Develop Climate Division Boundaries
QC & Revise with Team
1.4Final Report and Manuscript Writing
N/A
N/A
NOAA/NWS/Pacific Region
WPO-NWS-SLA 01.FY21.1
Relevant to All Hazards
Hawaii
Subseasonal to Seasonal Program (S2S)
May 2021 - May 2022
routine monitoring of climate in the state of hawai'i establishment of state climate divisions, we propose to develop the analytical approach to produce climate divisions for the state of hawai'i with regional groupings analogous to the conus climate division records, basic climate summaries and historical climate analyses produced by the national centers for environmental information ncei do not include the state of hawai'i largely because hawai'i is the only state that does not have assigned climate divisions the contiguous united states conus is separated into 344 climate divisions while alaska has an additional 13 which were added to the national data set in 2015
Not Applicable (N/A)
NOAA/NWS/Pacific Region
We propose to develop the analytical approach to produce climate divisions for the State of Hawai'i with regional groupings analogous to the CONUS climate division records.
1
Next-generation learning path for developing skill in NWS field climate services staff for delivering Impact-Based Decision Support Services at Sub-seasonal to Seasonal time scales
Timofeyeva
S2S
check_circle
2021
Level 3
In the end of the project, a training course on S2S DSS will be developed and tested in the field. Twenty percent of the staff will be trained and able to successfully engage with IDSS core partners and stakeholders.
Marina Timofeyeva, Jenna Meyers, Jim Keeney
Complete
Sat May 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Forecast Skill Metrics, Decision Support
Climate SLA
NWS staff has an initial understanding of climate variability and change, has general awareness of the CPC suite of climate products, and engages with and delivers interpretive services of meteorological products to Impact-based Decision Support Services (IDSS) core partners. As demonstrated by several unsuccessful initiatives in the local NWS offices, NWS staff lack skill in developing and delivering climate information at sub-seasonal to seasonal ( S2S) time scales to the core partners, although the demand for such information is high.
FY21 Q4
Development of a distance learning project plan
Deliverables: Project plan with detailed learning objectives and training approaches that will be used in distance learning modules
FY22 Q1
Refinement of distance learning project plan based on input from NOAA and NWS subject matter experts
Deliverables: Revised project plan with final learning objectives
Detailed outline of content, interactions, and scenarios for distance learning, including early prototype modules
FY22 Q2
Demonstrate progress in development of distance learning on S2S IDSS
Deliverables: Prototypes of distance learning scenarios and modules for review by NOAA and NWS SMEs
FY22 Q3
Demonstrate continued progress in development of distance learning on S2S IDSS
Deliverables: Revised distance learning training modules for final review by NOAA and NWS SMEs
FY22 Q4
Complete the first training modules and develop a plan for future work.
Deliverables: First S2S IDSS distance learning module published at CLC and NWS Training Portal
N/A
N/A
NWS/Climate Services Branch,
NOAA/NWS/OCLO
WPO-NWS-SLA 02.FY21.2
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2021 - May 2022
Forecast Skill Metrics, Decision Support
next generation learning path for developing skill in nws field climate services staff for delivering impact based decision support services at sub seasonal to seasonal time scales, in the end of the project a training course on s2s dss will be developed and tested in the field twenty percent of the staff will be trained and able to successfully engage with idss core partners and stakeholders, nws staff has an initial understanding of climate variability and change has general awareness of the cpc suite of climate products and engages with and delivers interpretive services of meteorological products to impact based decision support services idss core partners as demonstrated by several unsuccessful initiatives in the local nws offices nws staff lack skill in developing and delivering climate information at sub seasonal to seasonal s2s time scales to the core partners although the demand for such information is high
Not Applicable (N/A)
NWS/Climate Services Branch, NOAA/NWS/OCLO
In the end of the project, a training course on S2S DSS will be developed and tested in the field. Twenty percent of the staff will be trained and able to successfully engage with IDSS
1
Incorporate USGS Gauge Data into the Local Climate Analysis Tool
Churma
S2S
check_circle
2021
Level 3
The LCAT team proposes to incorporate US Geological Survey stream gages data as an analysis option. A set of gage parameters, specified by a science advisory team, would be available in the LCAT interface to be selected by users.
Michael Churma, Stephan B. Smith
Complete
Sat May 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Decision Support, Risk Perception & Response
Climate SLA
Local Climate Analysis Tool (LCAT)
NOAA and NWS are developing analytical capabilities to provide better decision support services to users, especially for those decisions requiring consideration of longer-term information for planning, preparedness, and reducing risks from extreme weather (e.g., extreme temperatures, precipitation events, sea ice changes, etc.). The Local Climate Analysis Tool (LCAT, http://nws.weather.gov/lcat/), one of the NOAA climate services delivery tools, provides recommended NOAA data and statistical capabilities to assess impacts of climate variability and climate change at local levels. The LCAT Integrated Working team is currently focusing on addressing coastal issues such as flooding and inundation, by incorporating National Ocean Service tide gage data and National Ocean Service tide gage data into its options for data analysis.
FY21Q4
Develop requirements by utilizing feedback from a Science Advisory Team.
Deliverables: A list of requirements will be provided, including chosen USGS gage parameters; accompanying interpretive statements; map display specifications; and suggested relationships to existing LCAT analysis features.
FY22Q1
Initial USGS gage capabilities are prototyped on a development LCAT platform.
Science Advisory Team provides feedback
Deliverables: Initial USGS gage data will be displayable in LCAT and viewable on a developmental platform.
FY22Q2
USGS gage capabilities are prototyped on a development LCAT platform, incorporating Science Advisory Team feedback.
LCAT gage data products are tested for data quality and scientific validity
Deliverables: Complete capabilities, based on requirements, are installed and viewable on an LCAT developmental or test platform.
Test procedures are written and completed.
FY22Q3
Update training materials.
Update LCAT documentation.
Install tested USGS gage capabilities on the LCAT production platform.
Deliverables: New LCAT USGS capabilities are complete and available on the production platform.
Training materials are available that reflect the new capabilities.
LCAT documentation is available that describes the new capabilities.
N/A
N/A
NWS/MDL
WPO-NWS-SLA 03.FY21.1
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2021 - May 2022
Decision Support, Risk Perception & Response
incorporate usgs gauge data into the local climate analysis tool, the lcat team proposes to incorporate us geological survey stream gages data as an analysis option a set of gage parameters specified by a science advisory team would be available in the lcat interface to be selected by users, noaa and nws are developing analytical capabilities to provide better decision support services to users especially for those decisions requiring consideration of longer term information for planning preparedness and reducing risks from extreme weather e g extreme temperatures precipitation events sea ice changes etc the local climate analysis tool lcat http nws weather gov lcat one of the noaa climate services delivery tools provides recommended noaa data and statistical capabilities to assess impacts of climate variability and climate change at local levels the lcat integrated working team is currently focusing on addressing coastal issues such as flooding and inundation by incorporating national ocean service tide gage data and national ocean service tide gage data into its options for data analysis
Not Applicable (N/A)
NWS/MDL
The LCAT team proposes to incorporate US Geological Survey stream gages data as an analysis option. A set of gage parameters, specified by a science advisory team, would be available in the LCAT interface to
1
Spatially downscaled S2S outlooks, to scales more applicable for hydrologic and other applications
Rosencrans
S2S
check_circle
2021
Level 3
Currently, operational outlooks at the S2S timescales are delivered at spatial scales of 100km (or more). Hydrologic and other applications require either much higher spatial resolution (1-4km) or fitting to irregular shapes (HUC or drainage basins).
Matthew Rosencrans
Complete
Sat May 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting
Climate SLA
Currently, operational outlooks at the S2S timescales are delivered at spatial scales of 100km (or more). Hydrologic and other applications require either much higher spatial resolution (1-4km) or fitting to irregular shapes (HUC or drainage basins).
FY22Q1
Download, QC RFC analysis of record data (AORC)
Stitching AORC data across multiple RFCs - Maybe for computational efficiency
Deliverable - Full set of AORC data at NCEP that's suitable for further projects
FY22Q2
Constructing proper statistics (accumulations/medians/means, or other moments, as necessary) from the AORC
Deliverable - Set of statistics from the AORC data, suitable for use in training ML-based algorithms for CPC style outlooks.
Assessing, from the literature and consultation with SME at CPC and across NOAA, the best ML-based methods to apply.
Support Vector Machine vs Random Forest, Random Forests , Intercomparison of methods, Deep Learning based CNN, Convolutional Neural Networks, ELM, LSTM
Deliverable - Short report on which method(s) should be explored.
FY22Q3
Train the selected ML-based methods with GEFSv12 and the AORC and other coarser grids suitable for downscaling CPC tools for example for excessive heat.
Deliverable - Trained set of NN coefficients and parameters
FY22Q4
Evaluate benefit of ML-based method versus simpler methods and potentially as input to RFC models and for other applications such as excessive heat.
Deliverable - Report on the evaluation
N/A
N/A
NOAA/NWS/NCEP/CPC
WPO-NWS-SLA 04.FY21.2
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2021 - May 2022
Dynamics and Nesting
spatially downscaled s2s outlooks to scales more applicable for hydrologic and other applications, currently operational outlooks at the s2s timescales are delivered at spatial scales of 100km or more hydrologic and other applications require either much higher spatial resolution 1 4km or fitting to irregular shapes huc or drainage basins, currently operational outlooks at the s2s timescales are delivered at spatial scales of 100km or more hydrologic and other applications require either much higher spatial resolution 1 4km or fitting to irregular shapes huc or drainage basins
Not Applicable (N/A)
NOAA/NWS/NCEP/CPC
Currently, operational outlooks at the S2S timescales are delivered at spatial scales of 100km (or more). Hydrologic and other applications require either much higher spatial resolution (1-4km) or fitting to irregular shapes (HUC or drainage
1
Daily Surface 2-Meter Temperature Analysis for Subseasonal to Seasonal (S2S) Monitoring and Prediction
Wang
S2S
check_circle
2021
Level 3
The overarching goal of this project is to develop an accurate surface air temperature analysis with high spatial and temporal resolutions with improved algorithm and enhanced data sources based on station observations as well as output from atmospheric reanalyses. During the first-year project period, we focus on enhancing and consolidating observational data, and testing and finalizing an improved analysis algorithm. Our objectives include:
Improve the quality of surface air temperature analyses through refining the interpolation algorithm and adding station reports from the NCEI/GHCN-D, NCEI/GSOD and other additional sources; and
Complete the retrospective analyses to at least 1971.
Wanqiu Wang
Complete
Sat May 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting
Climate SLA
Surface air temperature at 2 meters (T2m) is an essential climate variable (ECV) indicating the mean and variations of the climate state. At NOAA Climate Prediction Center (CPC), T2m has been one of the most important variables for climate monitoring and climate predictions. While traditional climate operations at CPC target the monthly mean temperature and its departure from the long-term mean, evolving user needs require that climate variations at sub-seasonal time scales, especially those associated with extreme weather and climate, also be monitored and predicted. A real-time updated long-term data set of global daily T2m analysis is therefore required for the sub-seasonal to seasonal monitoring and prediction.
A consolidated real-time updated surface air temperature data set of daily Tmax and Tmin; and
A refined interpolation algorithm capable of constructing analyses of daily Tmax and Tmin at a 0.05olat/lon grid over the global land.
N/A
N/A
NOAA/NWS/NCEP/CPC
WPO-NWS-SLA 05.FY21.1
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2021 - May 2022
Dynamics and Nesting
daily surface 2 meter temperature analysis for subseasonal to seasonal s2s monitoring and prediction, the overarching goal of this project is to develop an accurate surface air temperature analysis with high spatial and temporal resolutions with improved algorithm and enhanced data sources based on station observations as well as output from atmospheric reanalyses during the first year project period we focus on enhancing and consolidating observational data and testing and finalizing an improved analysis algorithm our objectives include improve the quality of surface air temperature analyses through refining the interpolation algorithm and adding station reports from the ncei ghcn d ncei gsod and other additional sources and complete the retrospective analyses to at least 1971, surface air temperature at 2 meters t2m is an essential climate variable ecv indicating the mean and variations of the climate state at noaa climate prediction center cpc t2m has been one of the most important variables for climate monitoring and climate predictions while traditional climate operations at cpc target the monthly mean temperature and its departure from the long term mean evolving user needs require that climate variations at sub seasonal time scales especially those associated with extreme weather and climate also be monitored and predicted a real time updated long term data set of global daily t2m analysis is therefore required for the sub seasonal to seasonal monitoring and prediction
Not Applicable (N/A)
NOAA/NWS/NCEP/CPC
The overarching goal of this project is to develop an accurate surface air temperature analysis with high spatial and temporal resolutions with improved algorithm and enhanced data sources based on station observations as well as
1
Integrated Deployment of New User Interface and Recent Capability Upgrades into the Local Climate Analysis Tool
Updated Title: LCAT Operational Deployment of New User Interface with Added Analytical Capabilities
Churma
S2S
check_circle
2022
Level 3
The FY22 proposal focuses on operational implementation of these new capabilities including the new user interface. Prior to operational deployment, the project will (1) resolve data flow connectivity issues that have been identified, (2) adapt the interface for mobile device compatibility, (3) test accuracy of the added data and functionality through beta testing, (4) create shortcuts/templates for specific user applications of different variables and analysis capabilities, (5) ensure clarity of interpretation statements of new analysis results, (6) create and publish training modules for the new content and capabilities.
Michael Churma, Judy Ghirardelli
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
NOAA and NWS are developing analytical capabilities to provide better decision support services to users, especially for those decisions requiring consideration of longer-term information for planning, preparedness, and reducing risks from climate change and variability. The Local Climate Analysis Tool (LCAT, https://lcat.nws.noaa.gov/ ), one of the NOAA climate services delivery tools, provides recommended NOAA data and statistical capabilities to assess impacts of climate variability and climate change at local levels. The LCAT Integrated Working Team (IWT) in the past several years has completed developmental work to upgrade the analysis capabilities for Arctic, coastal studies, water resources, drought impacts, and extreme weather events. In addition, a new interface has been developed that allows easier utility of these analyses and interpretation of the results.
FY22Q4
Develop requirements by utilizing feedback from a Science Advisory Team (SAT)
Assess authentication method for proper level of access and security.
Assess ingest strategies for on-demand data set requests.
Deliverables:
A list of requirements will be provided, generated by the SAT and agreed to by MDL, including: updated interpretive statements; smartphone interface requirements; map display specifications; universal pre-set/template settings; new techniques or data sets as specified; suggested relationships to existing LCAT analysis features. This list of requirements will represent what MDL will be able to accomplish during this Period of Performance.
As necessary, a plan for improving data consistency of data ingest will be in place
An authentication strategy for the application will be finalized.
FY23Q1
Initial new capabilities are prototyped on a development LCAT platform.
Science Advisory Team provides feedback
Data ingest consistency issues are addressed.
Deliverables:
Initial smartphone interface, running on a test platform, will be viewable.
Updated interpretation statements will be available for review on a test platform.
FY23Q2
New capabilities are finalized on a development LCAT platform based on SAT feedback.
Test cases are written to test functionality and data validity/consistency.
Final authentication strategy will be in place.
Deliverables:
Complete capabilities, as specified in the requirements list deliverable, are installed and viewable on an LCAT test platform.
Test procedures are specified.
FY23Q3
Update training materials.
Update LCAT documentation.
Complete test procedures.
Install new interface and capabilities on the LCAT production platform.
Deliverables:
New LCAT capabilities, as specified in the requirements list deliverable (including new interface and mobile viewing), are complete and available on the production platform.
Training materials are available that reflect the new capabilities.
LCAT documentation is available that describes the new capabilities.
integrated deployment of new user interface and recent capability upgrades into the local climate analysis tool updated title lcat operational deployment of new user interface with added analytical capabilities, the fy22 proposal focuses on operational implementation of these new capabilities including the new user interface prior to operational deployment the project will 1 resolve data flow connectivity issues that have been identified 2 adapt the interface for mobile device compatibility 3 test accuracy of the added data and functionality through beta testing 4 create shortcuts templates for specific user applications of different variables and analysis capabilities 5 ensure clarity of interpretation statements of new analysis results 6 create and publish training modules for the new content and capabilities, noaa and nws are developing analytical capabilities to provide better decision support services to users especially for those decisions requiring consideration of longer term information for planning preparedness and reducing risks from climate change and variability the local climate analysis tool lcat https lcat nws noaa gov one of the noaa climate services delivery tools provides recommended noaa data and statistical capabilities to assess impacts of climate variability and climate change at local levels the lcat integrated working team iwt in the past several years has completed developmental work to upgrade the analysis capabilities for arctic coastal studies water resources drought impacts and extreme weather events in addition a new interface has been developed that allows easier utility of these analyses and interpretation of the results
Not Applicable (N/A)
NOAA/NWS/MDL
The FY22 proposal focuses on operational implementation of these new capabilities including the new user interface. Prior to operational deployment, the project will (1) resolve data flow connectivity issues that have been identified, (2) adapt
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Alaska Spring River Ice Breakup Guidance
Petrescu
S2S
check_circle
2022
Level 3
This project will improve break-up guidance by applying data science principles (i.e., standard climate variability methods, potentially machine learning techniques depending on applicability) to observational data sets to anticipate Alaska river ice breakup at the subseasonal to seasonal time scale. The Alaska data analysis that provides state-wide guidance information will be complemented with localized guidance for select underserved communities that are off the road system and in observational-data sparse areas. The probabilistic methodology will be tested on various locations that have historically experienced ice jam flood, and this methodology will be cast into a probabilistic framework suitable for operational application.
Eugene Petrescu, Crane Johnson
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Probabilistic Weather Forecasting, Social Vulnerability
Climate SLA
Ice jam flooding is one of the most frequently declared disasters in Alaska. The occurrence of
ice jams results in severe flooding and significant damage caused by a rapid rise in water levels
upstream of ice jams. Understanding the potential for conditions favorable for severe ice jam
flooding, including antecedent conditions and corresponding S2S guidance forecasts, will help the APRFC to assess the risks and provide objective information for communities to prepare for the spring breakup season.
The University of Alaska Fairbanks will complete the following:
(FY23Q2) - Develop a statistical model(s) to estimate the occurrence of river ice breakup date and the severity (which would provide a likelihood of flooding or ice jams) for rivers/communities in Alaska
Evaluate historical reanalysis, station, breakup date, flooding, and ice jam databases.
Potential predictors of breakup date include thawing degree days and the snow water equivalent of the late-season snowpack.
Predictors of severity would be similar to breakup date and include rapid rises in temperature and snowpack depth anomaly.
Statistical predictor models may need to be developed for specific regions (climate divisions) or watersheds depending on the representativeness of the statistics across locations
(FY23Q3) - Initial assessment/demonstration of forecast performance using the breakup/severity approach with forecast data during the Spring 2023 Breakup Season
Apply meteorological inputs from GEFS and/or CFS
Test forecast for one season (or hindcasts, subject to time availability)
(FY23Q3) - The Arctic Testbed and Proving Ground (ATPG) will assist the Alaska Pacific Forecast Center (APRFC) to integrate the developed statistical models into the operational toolkit at the APRFC
N/A
N/A
NOAA/NWS/Alaska Region
WPO-NWS-SLA 02.FY22.1
Relevant to All Hazards
Alaska
Subseasonal to Seasonal Program (S2S)
May 2022 - May 2023
Model & Forecast Guidance, Probabilistic Weather Forecasting, Social Vulnerability
alaska spring river ice breakup guidance, this project will improve break up guidance by applying data science principles i e standard climate variability methods potentially machine learning techniques depending on applicability to observational data sets to anticipate alaska river ice breakup at the subseasonal to seasonal time scale the alaska data analysis that provides state wide guidance information will be complemented with localized guidance for select underserved communities that are off the road system and in observational data sparse areas the probabilistic methodology will be tested on various locations that have historically experienced ice jam flood and this methodology will be cast into a probabilistic framework suitable for operational application, ice jam flooding is one of the most frequently declared disasters in alaska the occurrence of ice jams results in severe flooding and significant damage caused by a rapid rise in water levels upstream of ice jams understanding the potential for conditions favorable for severe ice jam flooding including antecedent conditions and corresponding s2s guidance forecasts will help the aprfc to assess the risks and provide objective information for communities to prepare for the spring breakup season
Not Applicable (N/A)
NOAA/NWS/Alaska Region
This project will improve break-up guidance by applying data science principles (i.e., standard climate variability methods, potentially machine learning techniques depending on applicability) to observational data sets to anticipate Alaska river ice breakup at the
1
Probabilistic Freezing Spray Guidance for Passage Planning for the Mariner
Sienkiewicz
S2S
check_circle
2022
Level 3
The initial effort will be to determine the predictability of the larger scale pattern to produce the conditions associated with freezing spray such as the advection offshore of subfreezing cold air, higher winds speeds, and sea surface temperatures cool enough for moderate to heavy freezing spray rates.
Joe Sienkiewicz
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Probabilistic Weather Forecasting
Climate SLA
Freezing spray or ice accretion results in a combination of cold air temperature, cool to cold sea surface temperature, higher wind speeds, waves, relative humidity, and vessel direction and speed relative to waves and wind. Freezing spray and resulting ice accretion can accumulate on a vessel's superstructure, antennae, railings, ladders, rigging, and crab or lobster pots severely reducing the stability and causing the vessel to capsize. Per the literature, the bulk of the accreted spray is generated by the vessels themselves colliding with waves while underway. The Dec 31, 2019 capsizing and sinking of the fishing vessel Scandies Rose in the western Gulf of Alaska with five crew lost highlights the threat to safe passage.
FY22Q4 - The first task/deliverable will be to develop a detailed plan to address the predictability of the larger scale hemispheric flow patterns that support the development of freezing spray conditions. Key predictive parameters will be determined as candidates for further examination. Very cold air outbreaks and their predictability over the NWS waters of responsibility across North American waters including the North Pacific, North Atlantic, Arctic Oceans, and Great Lakes will be a starting focus.
FY23Q2 - Develop reforecasts of appropriate predictive variables associated with the development of favorable freezing spray conditions.
FY23Q3 - Evaluate predictive skill of key parameters contributing to freezing spray for week 2.
FY23Q4 - Assess predictive capability for key parameters for freezing spray for week 3.
FY24Q2 - Deliver prototype probabilistic guidance graphics for potential freezing spray conditions for week 2 and 3.
(FY24-Q3) Final report and presentation. Delivered final codes and training materials.
N/A
N/A
NOAA/NWS/OPC,
NOAA/NWS/Alaska Region
WPO-NWS-SLA 03.FY22.2
Relevant to All Hazards
Alaska, Maryland
Subseasonal to Seasonal Program (S2S)
May 2022 - May 2023
Model & Forecast Guidance, Probabilistic Weather Forecasting
probabilistic freezing spray guidance for passage planning for the mariner, the initial effort will be to determine the predictability of the larger scale pattern to produce the conditions associated with freezing spray such as the advection offshore of subfreezing cold air higher winds speeds and sea surface temperatures cool enough for moderate to heavy freezing spray rates, freezing spray or ice accretion results in a combination of cold air temperature cool to cold sea surface temperature higher wind speeds waves relative humidity and vessel direction and speed relative to waves and wind freezing spray and resulting ice accretion can accumulate on a vessel's superstructure antennae railings ladders rigging and crab or lobster pots severely reducing the stability and causing the vessel to capsize per the literature the bulk of the accreted spray is generated by the vessels themselves colliding with waves while underway the dec 31 2019 capsizing and sinking of the fishing vessel scandies rose in the western gulf of alaska with five crew lost highlights the threat to safe passage
Not Applicable (N/A)
NOAA/NWS/OPC, NOAA/NWS/Alaska Region
The initial effort will be to determine the predictability of the larger scale pattern to produce the conditions associated with freezing spray such as the advection offshore of subfreezing cold air, higher winds speeds, and
1
Development of a high-resolution downslope wind climatology in the Western US to provide operational support of wildfire danger forecasting and communication
Bair
S2S
check_circle
2022
Level 3
We propose to develop a high-resolution climatology of downslope wind events for relevant regions in the Western US. We also propose to develop a prototype Downslope Wildfire Threat Index which NWS will be able to use operationally (Year 2). In year 1 we will develop a 40-year downslope wind climatology using the Desert Research Institute (DRI) Climate Ecosystem and Fire Applications (CEFA) California-Nevada Smoke and Air Committee (CANSAC)-WRF reanalysis, covering the period 1981-2020. The CANSAC-WRF is currently run operationally at 2 km spatial resolution over California-Nevada with a middle and outer domain run at 6 km and 12 km, respectively, and extending the entire expanse of the West: https://cansac.dri.edu/coffframe.php?page=WRFsetup.php. The full reanalysis will be completed (using other funding sources) by the end of 2021 which will then be used in year 1 of this project for our wind climatology data source.
Andrea Bair
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Reanalysis & Reforecasting
Climate SLA
Weather Research and Forecasting Model (WRF)
Downslope wind events occur in mountainous areas throughout the West and are major contributors to increased wildfire danger as they are often accompanied by temperature spikes, critically low relative humidity, and no precipitation. These high, and in some cases extreme, wind events drive rapid wildfire spread on new or existing fires and can generate extreme wildfire behavior. While the wind events themselves are typically forecasted quite well by the NWS, there is no climatological context to provide information on just how severe a forecasted event might be relative to historical events. A downslope wind climatology would be beneficial for communicating the severity of an upcoming event to forecasters, media, wildfire agencies, and the public.
In addition to the wildfire community, a high-resolution wind climatology product will also benefit other sectors such as transportation, energy, and recreation. For example, winter storm winds can have significant impacts on the lee side of mountain ranges. This product would help with messaging for major wind events in general, by putting wind speeds into a proper climatological context for overall general safety and protection of lives and property.
(FY22Q4) - Have an initial meeting with NWS partners to learn about specific metrics of interest that should be calculated for downslope winds and communication of the associated hazards.
(FY23Q1) - Apply downslope wind algorithm from Abatzoglou et al. 2020 (https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/joc.6607) to the CANSAC-WRF 40-year hourly reanalysis and for all three domains (2 km, 6 km, and 12 km). Vertical pressure level data (surface-500 hPa) of wind velocity, temperature, geopotential height, and pressure vertical velocity will be used from the CANSAC-WRF archive.
(FY23Q2) - Compute weekly (or other timescale based on feedback from NWS) downslope wind climatologies of the three key inputs: cross barrier wind near mountain top level, static stability above mountain top level, and vertical velocity below mountain top level. This will include the mean but also percentiles of the full distribution will be computed to provide context on extreme events.
(FY23Q2) - Compute weekly (or other timescale based on feedback from NWS) climatologies of the average number of downslope wind events each year.
(FY23Q2) - Compute other metrics of interest identified by NWS partners.
(FY23Q3) - Provide maps (for visualization purposes) and data files of climatologies and percentiles to NWS partners.
N/A
N/A
NOAA/NWS/Western Region
WPO-NWS-SLA 04.FY22.2
Relevant to All Hazards
Utah
Subseasonal to Seasonal Program (S2S)
May 2022 - May 2023
Dynamics and Nesting, Reanalysis & Reforecasting
development of a high resolution downslope wind climatology in the western us to provide operational support of wildfire danger forecasting and communication, we propose to develop a high resolution climatology of downslope wind events for relevant regions in the western us we also propose to develop a prototype downslope wildfire threat index which nws will be able to use operationally year 2 in year 1 we will develop a 40 year downslope wind climatology using the desert research institute dri climate ecosystem and fire applications cefa california nevada smoke and air committee cansac wrf reanalysis covering the period 1981 2020 the cansac wrf is currently run operationally at 2 km spatial resolution over california nevada with a middle and outer domain run at 6 km and 12 km respectively and extending the entire expanse of the west https cansac dri edu coffframe php?page=wrfsetup php the full reanalysis will be completed using other funding sources by the end of 2021 which will then be used in year 1 of this project for our wind climatology data source, downslope wind events occur in mountainous areas throughout the west and are major contributors to increased wildfire danger as they are often accompanied by temperature spikes critically low relative humidity and no precipitation these high and in some cases extreme wind events drive rapid wildfire spread on new or existing fires and can generate extreme wildfire behavior while the wind events themselves are typically forecasted quite well by the nws there is no climatological context to provide information on just how severe a forecasted event might be relative to historical events a downslope wind climatology would be beneficial for communicating the severity of an upcoming event to forecasters media wildfire agencies and the public in addition to the wildfire community a high resolution wind climatology product will also benefit other sectors such as transportation energy and recreation for example winter storm winds can have significant impacts on the lee side of mountain ranges this product would help with messaging for major wind events in general by putting wind speeds into a proper climatological context for overall general safety and protection of lives and property
Not Applicable (N/A)
NOAA/NWS/Western Region
We propose to develop a high-resolution climatology of downslope wind events for relevant regions in the Western US. We also propose to develop a prototype Downslope Wildfire Threat Index which NWS will be able to
1
Experimental Week-1 and Week-2 Global Heat Hazards Outlooks
Updated Title: Global Heat Health Predictions
Thiaw
S2S
check_circle
2022
Level 3
The NWS has executed a heat - health project for Africa enabling heat hazards outlooks and the translation of the outlooks into heat health outlooks, working with the Ministry of Health of Senegal. This project is an extension of the previous project to prepare heat wave outlooks globally. This project will be a NOAA contribution to the WMO Global Framework for Climate Services and the Global Heat Health Information Network.
Wassila Thiaw, David Dewitt
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Climate SLA
The NWS has executed a heat - health project for Africa enabling heat hazards outlooks and the translation of the outlooks into heat health outlooks, working with the Ministry of Health of Senegal. This project is an extension of the previous project to prepare heat wave outlooks globally. This project will be a NOAA contribution to the WMO Global Framework for Climate Services and the Global Heat Health Information Network.
Determine heat waves threshold values globally by calculating the 80th to the 90th climatological Tmax and HI, and document heat waves events for periods when the threshold values persist for three consecutive days. (Q1FY2023)
Refine the heat waves indices for specific regions initially starting from the Caribbean and Central America, Northern Africa, the Middle East, South and Central Asia. (Q1FY2023)
Document the historical regional heat wave events, assess model performance in predicting heat waves, develop heat wave forecasting tools, start issuing experimental heat hazards outlooks at week-1 and week-2 for regions where forecasts skill are reasonable, verify heat wave forecasts at week-1 and week-2, and develop a website for easy access to the heat wave forecasts and verifications. (Q2FY2023)
Work with meteorological institutions in the target regions to develop capacity in heat wave forecasting through training and knowledge sharing. (Q3FY2023)
Perform diagnostics studies to improve our understanding of the mechanisms associated with heat waves and use this additional knowledge to improve forecasts and communicate improvements to stakeholders. (Q3FY2023)
Organize a regional scoping workshop to identify a country where there is potential for success to initiate a pilot project on heat-health early warning, and provide training for both meteorologists and health professionals on the end-to-end process of heat-health early warning. (Q4FY2023)
N/A
N/A
NOAA/NWS/NCEP/CPC
WPO-NWS-SLA 05.FY22.2
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
May 2022 - May 2023
experimental week 1 and week 2 global heat hazards outlooks updated title global heat health predictions, the nws has executed a heat - health project for africa enabling heat hazards outlooks and the translation of the outlooks into heat health outlooks working with the ministry of health of senegal this project is an extension of the previous project to prepare heat wave outlooks globally this project will be a noaa contribution to the wmo global framework for climate services and the global heat health information network, the nws has executed a heat - health project for africa enabling heat hazards outlooks and the translation of the outlooks into heat health outlooks working with the ministry of health of senegal this project is an extension of the previous project to prepare heat wave outlooks globally this project will be a noaa contribution to the wmo global framework for climate services and the global heat health information network
Not Applicable (N/A)
NOAA/NWS/NCEP/CPC
The NWS has executed a heat - health project for Africa enabling heat hazards outlooks and the translation of the outlooks into heat health outlooks, working with the Ministry of Health of Senegal. This project
1
Seasonal to Sub-Seasonal Guidance for Underserved Communities in Alaska.
Updated Title: Seasonal to Sub-Seasonal Guidance for Underrepresented Communities in Alaska
Petrescu
S2S
check_circle
2022
Level 3
The focus of this project is to improve the communication of seasonal to sub-seasonal information on the potential evolution and characteristics of sea and freshwater ice to Alaska communities in a format and delivery method that is accessible and understandable. With improved access to S2S information on expected ice and weather conditions, communities can better plan future activities and make adjustments to activities if anomalous conditions are anticipated in the week 2 to a few months time frame. The first year focus of this project is to gain a better understanding of how Alaska communities make decisions on activities based upon ice conditions, their S2S information needs, preferences, and priorities, and the best pathways and means to communicate S2S information. For year 2, information summarized in year 1 will be used to develop or improve new tools and methods of communicating information with these communities.
Eugene Petrescu, Donald Moore
Complete
Sun May 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Information Seeking & Processing, Social Vulnerability
Climate SLA
Most Alaska communities off of the road network are highly dependent on over-water and
over ice travel for everyday activities, including general transportation and subsistence
activities. The seasonal characteristics of sea ice and freshwater ice, (freeze, thaw,
inter-seasonal quality of the ice), strongly influence day to day activities and the success or
failure of subsistence activities and thus food security. Rapid climate change at higher
latitudes is outpacing traditional knowledge and creating a need for additional information on
climate and weather hazards. Poor ice conditions in the winter of 2018-2019 led to a high
number of fatalities with people falling through the ice. Another significant challenge is the
ability to communicate potential conditions with many Alaskan communities given limited
internet infrastructure and available communication channels.
(FY22Q4) - Review previous community engagement activities on S2S weather/climate guidance products as they relate to sea ice or riverine predictions in Alaska
(FY22Q4) - Identify opportunities to engage with severalAlaska riverine or coastal communities to determine their access, understanding, and usage of available S2S information.
(FY23Q2) - Engaging with communities and relevant organizations:
Identify important ice freezing, thaw, and intra-seasonal evolution that impact their activities.
Collaborate with communities to understand locally-relevant environmental factors and time frames related to ice evolution
Identify appropriate means of communicating S2S information in an accessible way that is most easily understood.
(FY23Q3) - The ATPG / AESSIC will use this information and identify specific development activities in year two of this project to improve messaging and communication of S2S information.
N/A
N/A
NOAA/NWS/Alaska Region
WPO-NWS-SLA 06.FY22.1
Relevant to All Hazards
Alaska
Subseasonal to Seasonal Program (S2S)
May 2022 - May 2023
Model & Forecast Guidance, Information Seeking & Processing, Social Vulnerability
seasonal to sub seasonal guidance for underserved communities in alaska updated title seasonal to sub seasonal guidance for underrepresented communities in alaska, the focus of this project is to improve the communication of seasonal to sub seasonal information on the potential evolution and characteristics of sea and freshwater ice to alaska communities in a format and delivery method that is accessible and understandable with improved access to s2s information on expected ice and weather conditions communities can better plan future activities and make adjustments to activities if anomalous conditions are anticipated in the week 2 to a few months time frame the first year focus of this project is to gain a better understanding of how alaska communities make decisions on activities based upon ice conditions their s2s information needs preferences and priorities and the best pathways and means to communicate s2s information for year 2 information summarized in year 1 will be used to develop or improve new tools and methods of communicating information with these communities, most alaska communities off of the road network are highly dependent on over water and over ice travel for everyday activities including general transportation and subsistence activities the seasonal characteristics of sea ice and freshwater ice freeze thaw inter seasonal quality of the ice strongly influence day to day activities and the success or failure of subsistence activities and thus food security rapid climate change at higher latitudes is outpacing traditional knowledge and creating a need for additional information on climate and weather hazards poor ice conditions in the winter of 2018 2019 led to a high number of fatalities with people falling through the ice another significant challenge is the ability to communicate potential conditions with many alaskan communities given limited internet infrastructure and available communication channels
Not Applicable (N/A)
NOAA/NWS/Alaska Region
The focus of this project is to improve the communication of seasonal to sub-seasonal information on the potential evolution and characteristics of sea and freshwater ice to Alaska communities in a format and delivery method
1
Extending Marine Hazard Information to Week
S2S
search_activity
2024
Level 3
The goal of this project is to provide maritime hazard information for the week two period and beyond, specifically for wind and wave field outlooks containing delineations of most probable locations of hazards conditions to mariners and economic activity.
Active
Wed May 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu May 01 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
"Risk Categories, Indices, or Levels", Risk Perception & Response
Climate SLA
New operational tools for Ocean Prediction Center marine forecasters and potential ocean enterprise/blue economy applications will provide maritime hazard information for the week two period and beyond. Specifically, the proposed capability is for oceanic and atmospheric (wind and wave field) outlooks containing delineations of most probable locations of hazardous conditions to mariners and economic activity. Vessels traversing the north Atlantic and north Pacific Oceans can take 10-21 days at sea to navigate between distant ports. Critical economic and life-saving decisions can be made with reliable situational awareness of likely winds and wave hazards to include: informing long-range routing for optimal fuel savings; supply chain reliability with on time for the delivery of goods; and avoiding dangerous seas that can lead to loss of life and property and potentially even costly environmental disasters (note; there are ~100 hurricane-force wind events on average each year between the north Atlantic and Pacific oceans, some building seas to greater than 50ft). This project is a continuation and final operational transition and implementation of work formerly supported by the
NWS Climate SLA Services Innovation and Improvement Needs Requiring Research to Operations (R2O). It impacts NWS Climate Mission Requirement/Strategic Area items 1), 2), and 5).
Critical economic and life-saving decisions can be made with reliable situational awareness of likely winds and wave hazards to include: informing long-range routing for optimal fuel savings; supply chain reliability with on time for the delivery of goods; and avoiding dangerous seas that can lead to loss of life and property and potentially even costly environmental disasters (note; there are ~100 hurricane-force wind events on average each year between the north Atlantic and Pacific oceans, some building seas to greater than 50ft).
N/A
N/A
NOAA/NWS/OPC
Tropical Cyclones
Maryland
Subseasonal to Seasonal Program (S2S)
May 2024 - May 2025
"Risk Categories, Indices, or Levels", Risk Perception & Response
extending marine hazard information to week, the goal of this project is to provide maritime hazard information for the week two period and beyond specifically for wind and wave field outlooks containing delineations of most probable locations of hazards conditions to mariners and economic activity, new operational tools for ocean prediction center marine forecasters and potential ocean enterprise blue economy applications will provide maritime hazard information for the week two period and beyond specifically the proposed capability is for oceanic and atmospheric wind and wave field outlooks containing delineations of most probable locations of hazardous conditions to mariners and economic activity vessels traversing the north atlantic and north pacific oceans can take 10 21 days at sea to navigate between distant ports critical economic and life saving decisions can be made with reliable situational awareness of likely winds and wave hazards to include informing long range routing for optimal fuel savings supply chain reliability with on time for the delivery of goods and avoiding dangerous seas that can lead to loss of life and property and potentially even costly environmental disasters note there are ~100 hurricane force wind events on average each year between the north atlantic and pacific oceans some building seas to greater than 50ft this project is a continuation and final operational transition and implementation of work formerly supported by the nws climate sla services innovation and improvement needs requiring research to operations r2o it impacts nws climate mission requirement strategic area items 1 2 and 5
Not Applicable (N/A)
NOAA/NWS/OPC
The goal of this project is to provide maritime hazard information for the week two period and beyond, specifically for wind and wave field outlooks containing delineations of most probable locations of hazards conditions to
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Marine, Land and Atmospheric Composition Observations (Task 1)
Levit
S2S
check_circle
2019
Level 3
This project aims to continue acquiring observations for the earth system components and develop a unified observation data processing for the coupled system to augment the atmospheric data collected during the development of the Climate Forecast System Reanalysis (CFSR); will specifically focus on collecting the following observations extending at least 30 years in the past and will continue in real-time:
Significant wave heights (altimeters and buoys)
Sea Surface Temperature from satellites
Sea Surface Height (altimeters)
Sea Surface Salinity from satellites
Chlorophyll (ocean color)
Ice thickness and concentration
Ocean currents (ADCP, moorings, drifters)
Temperature and Salinity profiles (ARGO, XBT's, CTD's, gliders)
ocean surface winds from scatterometer
Aerosol optical depths from geostationary, polar orbiting satellite and surface based monitors
Aerosol amounts, size and composition and vertical profiles from lidar
Total column ozone and vertical concentrations
Snow cover, snow depth, snow water equivalent
Soil Moisture from satellites
Precipitation Analysis;
conduct extensive QA - QC and the observation processing team will work with the DA team for the respective components of the coupled system to assess the impact of the data on various analyses; and develop decoders for the marine, aerosol and land DA development that is being carried out as a separate development activity.
Climate Forecast System (CFS), Unified Forecast System (UFS)
This project aims to continue acquiring observations for the earth system components and develop a unified observation data processing for the coupled system to augment the atmospheric data collected during the development of the Climate Forecast System Reanalysis (CFSR); will specifically focus on collecting the following observations extending at least 30 years in the past and will continue in real-time:
Significant wave heights (altimeters and buoys)
Sea Surface Temperature from satellites
Sea Surface Height (altimeters)
Sea Surface Salinity from satellites
Chlorophyll (ocean color)
Ice thickness and concentration
Ocean currents (ADCP, moorings, drifters)
Temperature and Salinity profiles (ARGO, XBT's, CTD's, gliders)
ocean surface winds from scatterometer
Aerosol optical depths from geostationary, polar orbiting satellite and surface based monitors
Aerosol amounts, size and composition and vertical profiles from lidar
Total column ozone and vertical concentrations
Snow cover, snow depth, snow water equivalent
Soil Moisture from satellites
Precipitation Analysis;
conduct extensive QA - QC and the observation processing team will work with the DA team for the respective components of the coupled system to assess the impact of the data on various analyses; and develop decoders for the marine, aerosol and land DA development that is being carried out as a separate development activity.
This project aims to continue acquiring observations for the earth system components and develop a unified observation data processing for the coupled system to augment the atmospheric data collected during the development of the Climate Forecast System Reanalysis (CFSR); will specifically focus on collecting the following observations extending at least 30 years in the past and will continue in real-time:
Significant wave heights (altimeters and buoys)
Sea Surface Temperature from satellites
Sea Surface Height (altimeters)
Sea Surface Salinity from satellites
Chlorophyll (ocean color)
Ice thickness and concentration
Ocean currents (ADCP, moorings, drifters)
Temperature and Salinity profiles (ARGO, XBT's, CTD's, gliders)
ocean surface winds from scatterometer
Aerosol optical depths from geostationary, polar orbiting satellite and surface based monitors
Aerosol amounts, size and composition and vertical profiles from lidar
Total column ozone and vertical concentrations
Snow cover, snow depth, snow water equivalent
Soil Moisture from satellites
Precipitation Analysis;
conduct extensive QA - QC and the observation processing team will work with the DA team for the respective components of the coupled system to assess the impact of the data on various analyses; and develop decoders for the marine, aerosol and land DA development that is being carried out as a separate development activity.
augmenting the development of a coupled model for sub seasonal to seasonal applications marine land and atmospheric composition observations task 1, this project aims to continue acquiring observations for the earth system components and develop a unified observation data processing for the coupled system to augment the atmospheric data collected during the development of the climate forecast system reanalysis cfsr will specifically focus on collecting the following observations extending at least 30 years in the past and will continue in real time significant wave heights altimeters and buoys sea surface temperature from satellites sea surface height altimeters sea surface salinity from satellites chlorophyll ocean color ice thickness and concentration ocean currents adcp moorings drifters temperature and salinity profiles argo xbt's ctd's gliders ocean surface winds from scatterometer aerosol optical depths from geostationary polar orbiting satellite and surface based monitors aerosol amounts size and composition and vertical profiles from lidar total column ozone and vertical concentrations snow cover snow depth snow water equivalent soil moisture from satellites precipitation analysis conduct extensive qa qc and the observation processing team will work with the da team for the respective components of the coupled system to assess the impact of the data on various analyses and develop decoders for the marine aerosol and land da development that is being carried out as a separate development activity, this project aims to continue acquiring observations for the earth system components and develop a unified observation data processing for the coupled system to augment the atmospheric data collected during the development of the climate forecast system reanalysis cfsr will specifically focus on collecting the following observations extending at least 30 years in the past and will continue in real time significant wave heights altimeters and buoys sea surface temperature from satellites sea surface height altimeters sea surface salinity from satellites chlorophyll ocean color ice thickness and concentration ocean currents adcp moorings drifters temperature and salinity profiles argo xbt's ctd's gliders ocean surface winds from scatterometer aerosol optical depths from geostationary polar orbiting satellite and surface based monitors aerosol amounts size and composition and vertical profiles from lidar total column ozone and vertical concentrations snow cover snow depth snow water equivalent soil moisture from satellites precipitation analysis conduct extensive qa qc and the observation processing team will work with the da team for the respective components of the coupled system to assess the impact of the data on various analyses and develop decoders for the marine aerosol and land da development that is being carried out as a separate development activity
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This project aims to continue acquiring observations for the earth system components and develop a unified observation data processing for the coupled system to augment the atmospheric data collected during the development of the Climate
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Aerosol (and constituent) Modeling (Task 2)
McQueen
S2S
check_circle
2019
Level 3
This project aims to include reduced tropospheric ozone chemistry into FV3GFS-Chem coupled system; obtain and test emissions for biomass burning (GBBEPx and VIIRS and MODIS satellite FRP for plume rise), RETRO-2, GFED-3.1, and QFED-2.4.r6 for use in the FV3GFS-Chem coupled system; update sea salt processes and anthropogenic emissions along with dust, sea salt and anthropogenic sulfates; conduct detailed testing and evaluation of FV3GFS-Chem coupled system for medium to sub-seasonal time scales; work on incorporating aerosol coupling with atmospheric radiation and conduct testing and evaluation using columnar aerosol measurements and aerosol analyses; and begin to evaluate the impact of aerosols on radiation and resulting sensible weather predictions (temperatures, winds, precipitation, cloud cover) using the FV3GFS global physics package.
Tue Mar 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting
UFS SLA
Gridpoint Statistical Interpolation (GSI), Unified Forecast System (UFS)
This project aims to include reduced tropospheric ozone chemistry into FV3GFS-Chem coupled system; obtain and test emissions for biomass burning (GBBEPx and VIIRS and MODIS satellite FRP for plume rise), RETRO-2, GFED-3.1, and QFED-2.4.r6 for use in the FV3GFS-Chem coupled system; update sea salt processes and anthropogenic emissions along with dust, sea salt and anthropogenic sulfates; conduct detailed testing and evaluation of FV3GFS-Chem coupled system for medium to sub-seasonal time scales; work on incorporating aerosol coupling with atmospheric radiation and conduct testing and evaluation using columnar aerosol measurements and aerosol analyses; and begin to evaluate the impact of aerosols on radiation and resulting sensible weather predictions (temperatures, winds, precipitation, cloud cover) using the FV3GFS global physics package.
This project aims to include reduced tropospheric ozone chemistry into FV3GFS-Chem coupled system; obtain and test emissions for biomass burning (GBBEPx and VIIRS and MODIS satellite FRP for plume rise), RETRO-2, GFED-3.1, and QFED-2.4.r6 for use in the FV3GFS-Chem coupled system; update sea salt processes and anthropogenic emissions along with dust, sea salt and anthropogenic sulfates; conduct detailed testing and evaluation of FV3GFS-Chem coupled system for medium to sub-seasonal time scales; work on incorporating aerosol coupling with atmospheric radiation and conduct testing and evaluation using columnar aerosol measurements and aerosol analyses; and begin to evaluate the impact of aerosols on radiation and resulting sensible weather predictions (temperatures, winds, precipitation, cloud cover) using the FV3GFS global physics package.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY19-P-26
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
April 2019 - March 2020
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting
augmenting the development of a coupled model for sub seasonal to seasonal applications aerosol and constituent modeling task 2, this project aims to include reduced tropospheric ozone chemistry into fv3gfs chem coupled system obtain and test emissions for biomass burning gbbepx and viirs and modis satellite frp for plume rise retro 2 gfed 3 1 and qfed 2 4 r6 for use in the fv3gfs chem coupled system update sea salt processes and anthropogenic emissions along with dust sea salt and anthropogenic sulfates conduct detailed testing and evaluation of fv3gfs chem coupled system for medium to sub seasonal time scales work on incorporating aerosol coupling with atmospheric radiation and conduct testing and evaluation using columnar aerosol measurements and aerosol analyses and begin to evaluate the impact of aerosols on radiation and resulting sensible weather predictions temperatures winds precipitation cloud cover using the fv3gfs global physics package, this project aims to include reduced tropospheric ozone chemistry into fv3gfs chem coupled system obtain and test emissions for biomass burning gbbepx and viirs and modis satellite frp for plume rise retro 2 gfed 3 1 and qfed 2 4 r6 for use in the fv3gfs chem coupled system update sea salt processes and anthropogenic emissions along with dust sea salt and anthropogenic sulfates conduct detailed testing and evaluation of fv3gfs chem coupled system for medium to sub seasonal time scales work on incorporating aerosol coupling with atmospheric radiation and conduct testing and evaluation using columnar aerosol measurements and aerosol analyses and begin to evaluate the impact of aerosols on radiation and resulting sensible weather predictions temperatures winds precipitation cloud cover using the fv3gfs global physics package
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This project aims to include reduced tropospheric ozone chemistry into FV3GFS-Chem coupled system; obtain and test emissions for biomass burning (GBBEPx and VIIRS and MODIS satellite FRP for plume rise), RETRO-2, GFED-3.1, and QFED-2.4.r6 for
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Aerosol and constituent Data Assimilation (Task 3)
Kleist
S2S
check_circle
2019
Level 3
This project aims to develop data assimilation (DA()techniques into Gridpoint Statistical Interpolation (GSI) for assimilation of aerosol optical depth (AOD) retrievals; generate background error estimates using lagged forecast pairs of aerosols for use in GSI 3DVar and Hybrid En-Var systems; ensemble information from atmospheric component will be utilized with EnKF and EnVar solvers within the GSI framework; start using components from Joint Effort for Data Assimilation Integration (JEDI), especially the Unified Forward Operator to include aerosol DA capabilities; and conduct extensive evaluation of AOD and aerosol analysis comparing with Modern-Era Retrospective analysis for Research and Applications (MERRA) analysis, independent column AOD measurements and Lidar derived aerosol profiles (where available).
Tue Mar 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting
UFS SLA
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Unified Forecast System (UFS)
This project aims to develop data assimilation (DA()techniques into Gridpoint Statistical Interpolation (GSI) for assimilation of aerosol optical depth (AOD) retrievals; generate background error estimates using lagged forecast pairs of aerosols for use in GSI 3DVar and Hybrid En-Var systems; ensemble information from atmospheric component will be utilized with EnKF and EnVar solvers within the GSI framework; start using components from Joint Effort for Data Assimilation Integration (JEDI), especially the Unified Forward Operator to include aerosol DA capabilities; and conduct extensive evaluation of AOD and aerosol analysis comparing with Modern-Era Retrospective analysis for Research and Applications (MERRA) analysis, independent column AOD measurements and Lidar derived aerosol profiles (where available).
This project aims to develop data assimilation (DA()techniques into Gridpoint Statistical Interpolation (GSI) for assimilation of aerosol optical depth (AOD) retrievals; generate background error estimates using lagged forecast pairs of aerosols for use in GSI 3DVar and Hybrid En-Var systems; ensemble information from atmospheric component will be utilized with EnKF and EnVar solvers within the GSI framework; start using components from Joint Effort for Data Assimilation Integration (JEDI), especially the Unified Forward Operator to include aerosol DA capabilities; and conduct extensive evaluation of AOD and aerosol analysis comparing with Modern-Era Retrospective analysis for Research and Applications (MERRA) analysis, independent column AOD measurements and Lidar derived aerosol profiles (where available).
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY19-P-27
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
April 2019 - March 2020
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting
augmenting the development of a coupled model for sub seasonal to seasonal applications aerosol and constituent data assimilation task 3, this project aims to develop data assimilation da techniques into gridpoint statistical interpolation gsi for assimilation of aerosol optical depth aod retrievals generate background error estimates using lagged forecast pairs of aerosols for use in gsi 3dvar and hybrid en var systems ensemble information from atmospheric component will be utilized with enkf and envar solvers within the gsi framework start using components from joint effort for data assimilation integration jedi especially the unified forward operator to include aerosol da capabilities and conduct extensive evaluation of aod and aerosol analysis comparing with modern era retrospective analysis for research and applications merra analysis independent column aod measurements and lidar derived aerosol profiles where available, this project aims to develop data assimilation da techniques into gridpoint statistical interpolation gsi for assimilation of aerosol optical depth aod retrievals generate background error estimates using lagged forecast pairs of aerosols for use in gsi 3dvar and hybrid en var systems ensemble information from atmospheric component will be utilized with enkf and envar solvers within the gsi framework start using components from joint effort for data assimilation integration jedi especially the unified forward operator to include aerosol da capabilities and conduct extensive evaluation of aod and aerosol analysis comparing with modern era retrospective analysis for research and applications merra analysis independent column aod measurements and lidar derived aerosol profiles where available
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This project aims to develop data assimilation (DA()techniques into Gridpoint Statistical Interpolation (GSI) for assimilation of aerosol optical depth (AOD) retrievals; generate background error estimates using lagged forecast pairs of aerosols for use in GSI
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: EMC liaisons at GFDL for FV3 and MOM6 development (Task 4)
Yang
S2S
check_circle
2019
Level 3
This project aims to place two scientists at GFDL as their liaisons for collaborative development of FV3 and MOM6; support sustainable code management practices and repository synchronization between GFDL (internal Git) and UFS (github.com) for FV3 dynamic core; support transitioning advancements in atmospheric physics and data assimilation related aspects of FV3; assist in development of MOM6 based data assimilation, higher resolution (weather scale) MOM6 configuration, and nested ocean configuration for hurricane applications; and closely work with GFDL scientists in enhancing R2O efforts.
Tue Mar 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting
UFS SLA
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Modular Ocean Model (MOM), Unified Forecast System (UFS)
This project aims to place two scientists at GFDL as their liaisons for collaborative development of FV3 and MOM6; support sustainable code management practices and repository synchronization between GFDL (internal Git) and UFS (github.com) for FV3 dynamic core; support transitioning advancements in atmospheric physics and data assimilation related aspects of FV3; assist in development of MOM6 based data assimilation, higher resolution (weather scale) MOM6 configuration, and nested ocean configuration for hurricane applications; and closely work with GFDL scientists in enhancing R2O efforts.
This project aims to place two scientists at GFDL as their liaisons for collaborative development of FV3 and MOM6; support sustainable code management practices and repository synchronization between GFDL (internal Git) and UFS (github.com) for FV3 dynamic core; support transitioning advancements in atmospheric physics and data assimilation related aspects of FV3; assist in development of MOM6 based data assimilation, higher resolution (weather scale) MOM6 configuration, and nested ocean configuration for hurricane applications; and closely work with GFDL scientists in enhancing R2O efforts.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY19-P-28
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
April 2019 - March 2020
Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting
augmenting the development of a coupled model for sub seasonal to seasonal applications emc liaisons at gfdl for fv3 and mom6 development task 4, this project aims to place two scientists at gfdl as their liaisons for collaborative development of fv3 and mom6 support sustainable code management practices and repository synchronization between gfdl internal git and ufs github com for fv3 dynamic core support transitioning advancements in atmospheric physics and data assimilation related aspects of fv3 assist in development of mom6 based data assimilation higher resolution weather scale mom6 configuration and nested ocean configuration for hurricane applications and closely work with gfdl scientists in enhancing r2o efforts, this project aims to place two scientists at gfdl as their liaisons for collaborative development of fv3 and mom6 support sustainable code management practices and repository synchronization between gfdl internal git and ufs github com for fv3 dynamic core support transitioning advancements in atmospheric physics and data assimilation related aspects of fv3 assist in development of mom6 based data assimilation higher resolution weather scale mom6 configuration and nested ocean configuration for hurricane applications and closely work with gfdl scientists in enhancing r2o efforts
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This project aims to place two scientists at GFDL as their liaisons for collaborative development of FV3 and MOM6; support sustainable code management practices and repository synchronization between GFDL (internal Git) and UFS (github.com) for
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Ocean Data Assimilation (Task 5)
Kleist
S2S
check_circle
2019
Level 3
This project aims to setup the MOM6+CICE5 coupled system and develop a workflow to generate Hybrid GODAS development infrastructure; conduct extensive QA/QC of ocean and ice observations for generating 30-year observational data base; DA development will be conducted within the Joint Effort for Data assimilation Integration (JEDI) framework to develop a stand-alone hybrid GODAS infrastructure using 1/4th degree MOM6+CICE5 and cycling workflow; use FV3-GEFS generated reanalysis data as forcing for 30-year Hybrid GODAS based reanalysis; integrate the LETKF and 3DVAR algorithms from the current MOM6_SIS2 Hybrid GODAS configuration into marine JEDI; start making 5-day nearly centered data assimilation cycles and evaluate the analysis products; and closely work with CPC scientists in enhancing R2O efforts.
Tue Mar 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting
UFS SLA
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
This project aims to setup the MOM6+CICE5 coupled system and develop a workflow to generate Hybrid GODAS development infrastructure; conduct extensive QA/QC of ocean and ice observations for generating 30-year observational data base; DA development will be conducted within the Joint Effort for Data assimilation Integration (JEDI) framework to develop a stand-alone hybrid GODAS infrastructure using 1/4th degree MOM6+CICE5 and cycling workflow; use FV3-GEFS generated reanalysis data as forcing for 30-year Hybrid GODAS based reanalysis; integrate the LETKF and 3DVAR algorithms from the current MOM6_SIS2 Hybrid GODAS configuration into marine JEDI; start making 5-day nearly centered data assimilation cycles and evaluate the analysis products; and closely work with CPC scientists in enhancing R2O efforts.
This project aims to setup the MOM6+CICE5 coupled system and develop a workflow to generate Hybrid GODAS development infrastructure; conduct extensive QA/QC of ocean and ice observations for generating 30-year observational data base; DA development will be conducted within the Joint Effort for Data assimilation Integration (JEDI) framework to develop a stand-alone hybrid GODAS infrastructure using 1/4th degree MOM6+CICE5 and cycling workflow; use FV3-GEFS generated reanalysis data as forcing for 30-year Hybrid GODAS based reanalysis; integrate the LETKF and 3DVAR algorithms from the current MOM6_SIS2 Hybrid GODAS configuration into marine JEDI; start making 5-day nearly centered data assimilation cycles and evaluate the analysis products; and closely work with CPC scientists in enhancing R2O efforts.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY19-P-29
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
April 2019 - March 2020
Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting
augmenting the development of a coupled model for sub seasonal to seasonal applications ocean data assimilation task 5, this project aims to setup the mom6+cice5 coupled system and develop a workflow to generate hybrid godas development infrastructure conduct extensive qa qc of ocean and ice observations for generating 30 year observational data base da development will be conducted within the joint effort for data assimilation integration jedi framework to develop a stand alone hybrid godas infrastructure using 1 4th degree mom6+cice5 and cycling workflow use fv3 gefs generated reanalysis data as forcing for 30 year hybrid godas based reanalysis integrate the letkf and 3dvar algorithms from the current mom6_sis2 hybrid godas configuration into marine jedi start making 5 day nearly centered data assimilation cycles and evaluate the analysis products and closely work with cpc scientists in enhancing r2o efforts, this project aims to setup the mom6+cice5 coupled system and develop a workflow to generate hybrid godas development infrastructure conduct extensive qa qc of ocean and ice observations for generating 30 year observational data base da development will be conducted within the joint effort for data assimilation integration jedi framework to develop a stand alone hybrid godas infrastructure using 1 4th degree mom6+cice5 and cycling workflow use fv3 gefs generated reanalysis data as forcing for 30 year hybrid godas based reanalysis integrate the letkf and 3dvar algorithms from the current mom6_sis2 hybrid godas configuration into marine jedi start making 5 day nearly centered data assimilation cycles and evaluate the analysis products and closely work with cpc scientists in enhancing r2o efforts
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This project aims to setup the MOM6+CICE5 coupled system and develop a workflow to generate Hybrid GODAS development infrastructure; conduct extensive QA/QC of ocean and ice observations for generating 30-year observational data base; DA development
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Ensemble Reforecasts (Task 6)
Zhu
S2S
check_circle
2019
Level 3
This project aims to continue production of 30-year FV3GEFS reforecasts extending the forecast length to 35 days using Climate Forecast System Reanalysis (CFSR) initial conditions for 1989-1999 and using FV3GFS based reanalysis for 2000-2018; conduct evaluation of 30-year reforecast datasets using standard verification metrics; and provide reforecast data to customers and stakeholders.
Tue Mar 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting, Verification & Validation
UFS SLA
Climate Forecast System (CFS), Unified Forecast System (UFS)
This project aims to continue production of 30-year FV3GEFS reforecasts extending the forecast length to 35 days using Climate Forecast System Reanalysis (CFSR) initial conditions for 1989-1999 and using FV3GFS based reanalysis for 2000-2018; conduct evaluation of 30-year reforecast datasets using standard verification metrics; and provide reforecast data to customers and stakeholders.
This project aims to continue production of 30-year FV3GEFS reforecasts extending the forecast length to 35 days using Climate Forecast System Reanalysis (CFSR) initial conditions for 1989-1999 and using FV3GFS based reanalysis for 2000-2018; conduct evaluation of 30-year reforecast datasets using standard verification metrics; and provide reforecast data to customers and stakeholders.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY19-P-30
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
April 2019 - March 2020
Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting, Verification & Validation
augmenting the development of a coupled model for sub seasonal to seasonal applications ensemble reforecasts task 6, this project aims to continue production of 30 year fv3gefs reforecasts extending the forecast length to 35 days using climate forecast system reanalysis cfsr initial conditions for 1989 1999 and using fv3gfs based reanalysis for 2000 2018 conduct evaluation of 30 year reforecast datasets using standard verification metrics and provide reforecast data to customers and stakeholders, this project aims to continue production of 30 year fv3gefs reforecasts extending the forecast length to 35 days using climate forecast system reanalysis cfsr initial conditions for 1989 1999 and using fv3gfs based reanalysis for 2000 2018 conduct evaluation of 30 year reforecast datasets using standard verification metrics and provide reforecast data to customers and stakeholders
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This project aims to continue production of 30-year FV3GEFS reforecasts extending the forecast length to 35 days using Climate Forecast System Reanalysis (CFSR) initial conditions for 1989-1999 and using FV3GFS based reanalysis for 2000-2018; conduct
0
Toward the Operational Production of Next-Generation Global Reanalyses
Hamill
S2S
check_circle
2019
Level 3
This project has the goal to develop a multi-decadal FV3 GEFS reanalysis and reforecast in order to support key customer needs, such as the sub-seasonal postprocessing needs of the Climate Prediction Center and the hydrologic processing needs of the Office of Water Prediction.
Tue Mar 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Reanalysis & Reforecasting, Post-processing
UFS SLA
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Ensemble Forecast System (GEFS), Unified Forecast System (UFS)
This project requests previously agreed-upon funding be sent to ESRL/PSD for year 3 of an originally three-year (now extended to four years with only three years of delivered funding expected at PSD) project for the development of reanalyses and reforecasts for the next-generation operational FV3 Global Ensemble Forecast System GEFS (version 12).
This project has the goal to develop a multi-decadal FV3 GEFS reanalysis and reforecast in order to support key customer needs, such as the sub-seasonal postprocessing needs of the Climate Prediction Center and the hydrologic processing needs of the Office of Water Prediction.
N/A
N/A
NOAA/OAR/PSL
OWAQ-NWS-SLA FY19--X-YY
Relevant to All Hazards
Colorado
Subseasonal to Seasonal Program (S2S)
April 2019 - March 2020
Data Assimilation and Ensembles, Reanalysis & Reforecasting, Post-processing
toward the operational production of next generation global reanalyses, this project has the goal to develop a multi decadal fv3 gefs reanalysis and reforecast in order to support key customer needs such as the sub seasonal postprocessing needs of the climate prediction center and the hydrologic processing needs of the office of water prediction, this project requests previously agreed upon funding be sent to esrl psd for year 3 of an originally three year now extended to four years with only three years of delivered funding expected at psd project for the development of reanalyses and reforecasts for the next generation operational fv3 global ensemble forecast system gefs version 12
Not Applicable (N/A)
NOAA/OAR/PSL
This project has the goal to develop a multi-decadal FV3 GEFS reanalysis and reforecast in order to support key customer needs, such as the sub-seasonal postprocessing needs of the Climate Prediction Center and the hydrologic
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Task 1: Marine, Land and Atmospheric Composition Observations
Kleist
S2S
check_circle
2020
Level 3
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13
implementations and the development in the final stages for SFS v1. The focus of the requests here is for
FY20. The focus for the out-years will evolve as we achieve critical capabilities. Initial emphasis is on
coupling atmosphere - marine - ice - aerosol - land components followed by improving data assimilation for
individual components and start building the capabilities for strongly coupled data assimilation system.
Daryl Kleist
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting, Satellite & Remote Sensing Technologies
UFS SLA
Climate Forecast System (CFS), Global Ensemble Forecast System (GEFS), Global Forecast System (GFS), Seasonal Forecast System (SFS), Unified Forecast System (UFS)
This project aims to continue acquiring observations for the earth system components and develop a unified observation data processing for the coupled system to augment the atmospheric data collected during the development of the Climate Forecast System Reanalysis (CFSR); specifically focus on collecting the following observations extending at least 30 years in the past and will continue in real-time:
Significant wave heights (altimeters and buoys)
Sea Surface Temperature from satellites
Sea Surface Height (altimeters)
Sea Surface Salinity from satellites
Chlorophyll (ocean color)
Ice thickness and concentration
Ocean currents (ADCP, moorings, drifters)
Temperature and Salinity profiles (ARGO, XBT's, CTD's, gliders)
ocean surface winds from scatterometer
Aerosol optical depths from geostationary, polar orbiting satellite and surface based monitors
Aerosol amounts, size and composition and vertical profiles from lidar
Total column ozone and vertical concentrations
Snow cover, snow depth, snow water equivalent
Soil Moisture from satellites
Precipitation Analysis;
conduct extensive QA - QC and the observation processing team will work with the DA team for the respective components of the coupled system to assess the impact of the data on various analyses; develop decoders for the marine, aerosol and land DA development that is being carried out as a separate development activity; and coordinate this activity with the Joint Center for Satellite Data Assimilation for integration into the future observation data store (under the Interface to Observations DAtabase/IODA project).
This proposal augments the additional resources needed at EMC to advance the coupled model development for transitioning MER/S2S applications into operations.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY20-P-XX
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
June 2020 - May 2021
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting, Satellite & Remote Sensing Technologies
augmenting the development of a coupled model for sub seasonal to seasonal applications task 1 marine land and atmospheric composition observations, this is a multi year effort that ends with the coupled model in operations for gfsv17 gefs v13 implementations and the development in the final stages for sfs v1 the focus of the requests here is for fy20 the focus for the out years will evolve as we achieve critical capabilities initial emphasis is on coupling atmosphere marine ice aerosol land components followed by improving data assimilation for individual components and start building the capabilities for strongly coupled data assimilation system, this project aims to continue acquiring observations for the earth system components and develop a unified observation data processing for the coupled system to augment the atmospheric data collected during the development of the climate forecast system reanalysis cfsr specifically focus on collecting the following observations extending at least 30 years in the past and will continue in real time significant wave heights altimeters and buoys sea surface temperature from satellites sea surface height altimeters sea surface salinity from satellites chlorophyll ocean color ice thickness and concentration ocean currents adcp moorings drifters temperature and salinity profiles argo xbt's ctd's gliders ocean surface winds from scatterometer aerosol optical depths from geostationary polar orbiting satellite and surface based monitors aerosol amounts size and composition and vertical profiles from lidar total column ozone and vertical concentrations snow cover snow depth snow water equivalent soil moisture from satellites precipitation analysis conduct extensive qa qc and the observation processing team will work with the da team for the respective components of the coupled system to assess the impact of the data on various analyses develop decoders for the marine aerosol and land da development that is being carried out as a separate development activity and coordinate this activity with the joint center for satellite data assimilation for integration into the future observation data store under the interface to observations database ioda project
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13 implementations and the development in the final stages for SFS v1. The focus of the requests here is for
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Task 2: Aerosol (and constituent) Modeling
McQueen
S2S
check_circle
2020
Level 3
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13
implementations and the development in the final stages for SFS v1. The focus of the requests here is for
FY20. The focus for the out-years will evolve as we achieve critical capabilities. Initial emphasis is on
coupling atmosphere - marine - ice - aerosol - land components followed by improving data assimilation for
individual components and start building the capabilities for strongly coupled data assimilation system.
Jeff McQueen
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles
UFS SLA
Global Ensemble Forecast System (GEFS), Global Forecast System (GFS), Seasonal Forecast System (SFS), Unified Forecast System (UFS)
This project aims to include reduced tropospheric ozone chemistry into FV3GFS-Chem coupled system; obtain and test emissions for biomass burning (GBBEPx and VIIRS and MODIS satellite FRP for plume rise), RETRO-2, GFED-3.1, and QFED-2.4.r6 for use in the FV3GFS-Chem coupled system; update sea salt processes and anthropogenic emissions along with dust, sea salt and anthropogenic sulfates; conduct detailed testing and evaluation of FV3GFS-Chem coupled system for medium to sub-seasonal time scales; work on incorporating aerosol coupling with atmospheric radiation and conduct testing and evaluation using columnar aerosol measurements and aerosol analyses; and begin to evaluate the impact of aerosols on radiation and resulting sensible weather predictions (temperatures, winds, precipitation, cloud cover) using the FV3GFS global physics package.
This proposal augments the additional resources needed at EMC to advance the coupled model development for transitioning MER/S2S applications into operations.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY20-P-XX
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
June 2020 - May 2021
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles
augmenting the development of a coupled model for sub seasonal to seasonal applications task 2 aerosol and constituent modeling, this is a multi year effort that ends with the coupled model in operations for gfsv17 gefs v13 implementations and the development in the final stages for sfs v1 the focus of the requests here is for fy20 the focus for the out years will evolve as we achieve critical capabilities initial emphasis is on coupling atmosphere marine ice aerosol land components followed by improving data assimilation for individual components and start building the capabilities for strongly coupled data assimilation system, this project aims to include reduced tropospheric ozone chemistry into fv3gfs chem coupled system obtain and test emissions for biomass burning gbbepx and viirs and modis satellite frp for plume rise retro 2 gfed 3 1 and qfed 2 4 r6 for use in the fv3gfs chem coupled system update sea salt processes and anthropogenic emissions along with dust sea salt and anthropogenic sulfates conduct detailed testing and evaluation of fv3gfs chem coupled system for medium to sub seasonal time scales work on incorporating aerosol coupling with atmospheric radiation and conduct testing and evaluation using columnar aerosol measurements and aerosol analyses and begin to evaluate the impact of aerosols on radiation and resulting sensible weather predictions temperatures winds precipitation cloud cover using the fv3gfs global physics package
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13 implementations and the development in the final stages for SFS v1. The focus of the requests here is for
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Task 3: Aerosol and Constituent Data Assimilation
Kleist
S2S
check_circle
2020
Level 3
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13
implementations and the development in the final stages for SFS v1. The focus of the requests here is for
FY20. The focus for the out-years will evolve as we achieve critical capabilities. Initial emphasis is on
coupling atmosphere - marine - ice - aerosol - land components followed by improving data assimilation for
individual components and start building the capabilities for strongly coupled data assimilation system.
Daryl Kleist
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Verification & Validation
UFS SLA
Global Ensemble Forecast System (GEFS), Global Forecast System (GFS), Seasonal Forecast System (SFS), Unified Forecast System (UFS)
This project aims to generate background error estimates using lagged forecast pairs of aerosols for use in 3DVar and Hybrid En-Var systems; start using components from JEDI, especially the Unified Forward Operator to include aerosol DA capabilities; contribute to maturing JEDI-based development with partners to enable JEDI-based assimilation using JEDI-LETKF and Hybrid EnVar solvers; contribute significant development into the quality control and bias correction of MODIS and VIIRS based AOD retrieval products; and conduct extensive evaluation of AOD and aerosol analysis comparing with MERRA analysis, independent column AOD measurements and Lidar derived aerosol profiles (where available).
This proposal augments the additional resources needed at EMC to advance the coupled model development for transitioning MER/S2S applications into operations.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY20-P-XX
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
June 2020 - May 2021
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Verification & Validation
augmenting the development of a coupled model for sub seasonal to seasonal applications task 3 aerosol and constituent data assimilation, this is a multi year effort that ends with the coupled model in operations for gfsv17 gefs v13 implementations and the development in the final stages for sfs v1 the focus of the requests here is for fy20 the focus for the out years will evolve as we achieve critical capabilities initial emphasis is on coupling atmosphere marine ice aerosol land components followed by improving data assimilation for individual components and start building the capabilities for strongly coupled data assimilation system, this project aims to generate background error estimates using lagged forecast pairs of aerosols for use in 3dvar and hybrid en var systems start using components from jedi especially the unified forward operator to include aerosol da capabilities contribute to maturing jedi based development with partners to enable jedi based assimilation using jedi letkf and hybrid envar solvers contribute significant development into the quality control and bias correction of modis and viirs based aod retrieval products and conduct extensive evaluation of aod and aerosol analysis comparing with merra analysis independent column aod measurements and lidar derived aerosol profiles where available
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13 implementations and the development in the final stages for SFS v1. The focus of the requests here is for
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Task 4: EMC liaisons at GFDL for FV3 and MOM6 development
Mehra
S2S
check_circle
2020
Level 3
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13
implementations and the development in the final stages for SFS v1. The focus of the requests here is for
FY20. The focus for the out-years will evolve as we achieve critical capabilities. Initial emphasis is on
coupling atmosphere - marine - ice - aerosol - land components followed by improving data assimilation for
individual components and start building the capabilities for strongly coupled data assimilation system.
Avichal Mehra
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting
UFS SLA
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Ensemble Forecast System (GEFS), Global Forecast System (GFS), Modular Ocean Model (MOM), Seasonal Forecast System (SFS), Unified Forecast System (UFS)
This project aims to continue working with two scientists at GFDL as their liaisons for collaborative development of FV3 and MOM6; support sustainable code management practices and repository synchronization between GFDL (internal Git) and UFS (github.com) for FV3 dynamic core; support transitioning advancements in dynamics, atmospheric physics and data assimilation related aspects of FV3; assist in development of MOM6 based data assimilation, higher resolution (weather scale) MOM6 configuration, and nested ocean configuration for hurricane applications; and closely work with GFDL scientists in enhancing R2O efforts.
This proposal augments the additional resources needed at EMC to advance the coupled model development for transitioning MER/S2S applications into operations.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY20-P-XX
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
June 2020 - May 2021
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Dynamics and Nesting
augmenting the development of a coupled model for sub seasonal to seasonal applications task 4 emc liaisons at gfdl for fv3 and mom6 development, this is a multi year effort that ends with the coupled model in operations for gfsv17 gefs v13 implementations and the development in the final stages for sfs v1 the focus of the requests here is for fy20 the focus for the out years will evolve as we achieve critical capabilities initial emphasis is on coupling atmosphere marine ice aerosol land components followed by improving data assimilation for individual components and start building the capabilities for strongly coupled data assimilation system, this project aims to continue working with two scientists at gfdl as their liaisons for collaborative development of fv3 and mom6 support sustainable code management practices and repository synchronization between gfdl internal git and ufs github com for fv3 dynamic core support transitioning advancements in dynamics atmospheric physics and data assimilation related aspects of fv3 assist in development of mom6 based data assimilation higher resolution weather scale mom6 configuration and nested ocean configuration for hurricane applications and closely work with gfdl scientists in enhancing r2o efforts
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13 implementations and the development in the final stages for SFS v1. The focus of the requests here is for
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Task 5: Ocean Data Assimilation
Kleist
S2S
check_circle
2020
Level 3
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13
implementations and the development in the final stages for SFS v1. The focus of the requests here is for
FY20. The focus for the out-years will evolve as we achieve critical capabilities. Initial emphasis is on
coupling atmosphere - marine - ice - aerosol - land components followed by improving data assimilation for
individual components and start building the capabilities for strongly coupled data assimilation system.
Daryl Kleist
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting
UFS SLA
Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Ensemble Forecast System (GEFS), Global Forecast System (GFS), Seasonal Forecast System (SFS), Unified Forecast System (UFS)
This project aims to setup the MOM6+CICE5 coupled system and will develop a workflow to generate Hybrid GODAS development infrastructure; will conduct extensive QA/QC of ocean and ice observations for generating 30-year observational data base
DA development will be conducted within the JEDI framework to develop a stand-alone hybrid GODAS infrastructure using 1/4th degree MOM6+CICE5 and cycling workflow; use FV3-GEFS generated reanalysis data as forcing for 30-year Hybrid GODAS based reanalysis; integrate the LETKF and 3DVAR algorithms from the current MOM6_SIS2 Hybrid GODAS configuration into marine JEDI; transition from unified LETKF to JEDI-LETKF; start making 5-day nearly centered data assimilation cycles and evaluate the analysis products; and work with CPC scientists in enhancing R2O efforts.
This proposal augments the additional resources needed at EMC to advance the coupled model development for transitioning MER/S2S applications into operations.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY20-P-XX
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
June 2020 - May 2021
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting
augmenting the development of a coupled model for sub seasonal to seasonal applications task 5 ocean data assimilation, this is a multi year effort that ends with the coupled model in operations for gfsv17 gefs v13 implementations and the development in the final stages for sfs v1 the focus of the requests here is for fy20 the focus for the out years will evolve as we achieve critical capabilities initial emphasis is on coupling atmosphere marine ice aerosol land components followed by improving data assimilation for individual components and start building the capabilities for strongly coupled data assimilation system, this project aims to setup the mom6+cice5 coupled system and will develop a workflow to generate hybrid godas development infrastructure will conduct extensive qa qc of ocean and ice observations for generating 30 year observational data base da development will be conducted within the jedi framework to develop a stand alone hybrid godas infrastructure using 1 4th degree mom6+cice5 and cycling workflow use fv3 gefs generated reanalysis data as forcing for 30 year hybrid godas based reanalysis integrate the letkf and 3dvar algorithms from the current mom6_sis2 hybrid godas configuration into marine jedi transition from unified letkf to jedi letkf start making 5 day nearly centered data assimilation cycles and evaluate the analysis products and work with cpc scientists in enhancing r2o efforts
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13 implementations and the development in the final stages for SFS v1. The focus of the requests here is for
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Task 6: Land Data Assimilation
Kleist
S2S
check_circle
2020
Level 3
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13
implementations and the development in the final stages for SFS v1. The focus of the requests here is for
FY20. The focus for the out-years will evolve as we achieve critical capabilities. Initial emphasis is on
coupling atmosphere - marine - ice - aerosol - land components followed by improving data assimilation for
individual components and start building the capabilities for strongly coupled data assimilation system.
Daryl Kleist
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting
UFS SLA
Global Ensemble Forecast System (GEFS), Seasonal Forecast System (SFS), Unified Forecast System (UFS)
This project aims to set up necessary infrastructure to replace GLDAS with JEDI based land data assimilation system, this includes contributing to and validating the land model interface to JEDI (noah-mp to start); build standalone, offline land data assimilation capabilities, for use in testing in preparation for coupled land-atmospheric data assimilation; univariate systems for snow and soil moisture will be developed and tested; build and validate necessary QA/QC for snow retrievals, soil moisture retrievals, and screen-level temperature and humidity observations; will being to build infrastructure for performing land-only reanalysis (not for initialization) to test capabilities; being to explore strongly coupled land-atmospheric assimilation using JEDI-EnKF capabilities; and integrate land assimilation componentes into the workflow for the coupled UFS applications for use in full-scale coupled reanalysis and coupled model initialization.
This proposal augments the additional resources needed at EMC to advance the coupled model development for transitioning MER/S2S applications into operations.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY20-P-XX
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
June 2020 - May 2021
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting
augmenting the development of a coupled model for sub seasonal to seasonal applications task 6 land data assimilation, this is a multi year effort that ends with the coupled model in operations for gfsv17 gefs v13 implementations and the development in the final stages for sfs v1 the focus of the requests here is for fy20 the focus for the out years will evolve as we achieve critical capabilities initial emphasis is on coupling atmosphere marine ice aerosol land components followed by improving data assimilation for individual components and start building the capabilities for strongly coupled data assimilation system, this project aims to set up necessary infrastructure to replace gldas with jedi based land data assimilation system this includes contributing to and validating the land model interface to jedi noah mp to start build standalone offline land data assimilation capabilities for use in testing in preparation for coupled land atmospheric data assimilation univariate systems for snow and soil moisture will be developed and tested build and validate necessary qa qc for snow retrievals soil moisture retrievals and screen level temperature and humidity observations will being to build infrastructure for performing land only reanalysis not for initialization to test capabilities being to explore strongly coupled land atmospheric assimilation using jedi enkf capabilities and integrate land assimilation componentes into the workflow for the coupled ufs applications for use in full scale coupled reanalysis and coupled model initialization
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13 implementations and the development in the final stages for SFS v1. The focus of the requests here is for
0
Augmenting the Development of a Coupled Model for Sub-Seasonal to Seasonal Applications: Task 7: Coupled Reanalysis and Reforecast Planning
Kleist
S2S
check_circle
2020
Level 3
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13
implementations and the development in the final stages for SFS v1. The focus of the requests here is for
FY20. The focus for the out-years will evolve as we achieve critical capabilities. Initial emphasis is on
coupling atmosphere - marine - ice - aerosol - land components followed by improving data assimilation for
individual components and start building the capabilities for strongly coupled data assimilation system.
Daryl Kleist
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting
UFS SLA
Global Ensemble Forecast System (GEFS), Global Forecast System (GFS), Seasonal Forecast System (SFS), Unified Forecast System (UFS)
This project aims to design and develop a comprehensive plan for conducting coupled reanalysis and reforecasts in preparation for implementation of GFSv17/GEFSv13 and SFS applications; coordinate with partners on planning and execution of potential joint reanalysis effort, Core partners include (but are not limited to) Physical Sciences Laboratory (ESRL), NASA Goddard Modeling and Assimilation Office, and the Joint Center for Satellite Data Assimilation; work with stakeholders to define requirements and best practices for reanalysis; exploratory efforts at low resolution and various sub-components will be executed to generate evidence for establishing potential configures and to explore trade space between complexity, ensemble size, resolution; foster the execution of short reanalysis scout runs with a prototype system to iteratively work on expanding the observational database; explore the sensitivity to the number of streams, overlap periods, and update cadence for the various components; facilitate the development of end-to-end workflow for both development and implementation; and test the applicability of newly explored stochastic physics within the context of system error representation for use in weakly coupled assimilation for the reanalysis.
This proposal augments the additional resources needed at EMC to advance the coupled model development for transitioning MER/S2S applications into operations.
N/A
N/A
NOAA/NWS/NCEP/EMC
OWAQ-NWS-SLA FY20-P-XX
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
June 2020 - May 2021
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Data Assimilation and Ensembles, Reanalysis & Reforecasting
augmenting the development of a coupled model for sub seasonal to seasonal applications task 7 coupled reanalysis and reforecast planning, this is a multi year effort that ends with the coupled model in operations for gfsv17 gefs v13 implementations and the development in the final stages for sfs v1 the focus of the requests here is for fy20 the focus for the out years will evolve as we achieve critical capabilities initial emphasis is on coupling atmosphere marine ice aerosol land components followed by improving data assimilation for individual components and start building the capabilities for strongly coupled data assimilation system, this project aims to design and develop a comprehensive plan for conducting coupled reanalysis and reforecasts in preparation for implementation of gfsv17 gefsv13 and sfs applications coordinate with partners on planning and execution of potential joint reanalysis effort core partners include but are not limited to physical sciences laboratory esrl nasa goddard modeling and assimilation office and the joint center for satellite data assimilation work with stakeholders to define requirements and best practices for reanalysis exploratory efforts at low resolution and various sub components will be executed to generate evidence for establishing potential configures and to explore trade space between complexity ensemble size resolution foster the execution of short reanalysis scout runs with a prototype system to iteratively work on expanding the observational database explore the sensitivity to the number of streams overlap periods and update cadence for the various components facilitate the development of end to end workflow for both development and implementation and test the applicability of newly explored stochastic physics within the context of system error representation for use in weakly coupled assimilation for the reanalysis
Not Applicable (N/A)
NOAA/NWS/NCEP/EMC
This is a multi-year effort that ends with the coupled model in operations for GFSv17/GEFS v13 implementations and the development in the final stages for SFS v1. The focus of the requests here is for
0
High-Resolution Large-Ensemble Probabilistic Forecasts of Precipitation for the Northwestern US
Zhang
S2S
check_circle
2020
Level 3
Provide experience for NOAA to build future operational probabilistic precipitation prediction system using the convection-permitting large-ensemble approach under the auspices of UFS.
Chidong Zhang, Yolande Serra, Nicholas Bond, Cliff Mass
Complete
Sat Aug 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Precipitation Grand Challenge, Probabilistic Weather Forecasting
PPGC-S2S
Unified Forecast System (UFS)
The proposed project will demonstrate a prototype of a future U.S. high-resolution large-ensemble system for predicting precipitation.
The NWS will one day use high-resolution large-ensembles with calibration. This project will explore technology, science, and evaluation of such a system for one area of the country. NWS forecasters will be able to use these tools operationally, providing guidance for development of the national system and aiding the decision making of NWS stakeholders during the project period.
N/A
N/A
NOAA/OAR/PMEL,
JISAO/University of Washington,
University of Washington
WPO20-S2S-I-1
Water Extremes
Washington
Subseasonal to Seasonal Program (S2S)
August 2020 - July 2021
Data Assimilation and Ensembles, Dynamics and Nesting, Precipitation Grand Challenge, Probabilistic Weather Forecasting
high resolution large ensemble probabilistic forecasts of precipitation for the northwestern us, provide experience for noaa to build future operational probabilistic precipitation prediction system using the convection permitting large ensemble approach under the auspices of ufs, the proposed project will demonstrate a prototype of a future u s high resolution large ensemble system for predicting precipitation
Not Applicable (N/A)
NOAA/OAR/PMEL, JISAO/University of Washington, University of Washington
Provide experience for NOAA to build future operational probabilistic precipitation prediction system using the convection-permitting large-ensemble approach under the auspices of UFS.
0
An optimized hybrid seasonal forecast system for U.S. regional precipitation in late-summer to mid-fall based on inter-basin SST and convection parameters
Lee
S2S
check_circle
2020
Level 3
The main goal of this proposal is to develop an optimized hybrid seasonal forecast system
for U.S. precipitation in ASO using the inter-basin SSTA contrast and CS convection.
Sang-Ki Lee, Hosmay Lopez, Greg Foltz, Renellys Perez, Michael Alexander, Dongmin Kim, Marlos Goes, Sang-Ik Shin, Yan Wang, Arun Kumar
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Forecast System (CFSv2), North American Multi-Model Ensemble (NMME)
In the U.S., late-fall to mid-spring (November-April) large-scale precipitation variability is predominantly driven by Pacific sea surface temperature anomalies (SSTAs) associated with El Nio-Southern Oscillation through extratropical Rossby wave trains to North America, while early- to mid-summer (June-July) precipitation variability, especially east of the Rockies, is largely linked to Atlantic SSTAs and associated variation in the North Atlantic subtropical high. However, these well-established relationships almost completely break down in late-summer to mid-fall (August-October; ASO). Thus, operational seasonal forecast models have generally low skill in ASO for U.S. regional precipitation, leaving a large portion of U.S. populations vulnerable to unpredictable extreme events in that season. Our recent study (Kim et al., 2020) identified key physical processes in the Atlantic and Pacific that directly modulate late-summer to mid-fall U.S. precipitation variability, and demonstrated these connections using observations, a fully coupled model and a series of simple atmospheric model experiments. The study showed that in ASO, the inter-basin contrast between Pacific and Atlantic SSTAs significantly influences convective activity over the Caribbean Sea; this regional change in convection directly modulates the strength of the North American Low-Level Jet, and thus drives U.S. precipitation variability east of the Rockies. Through this mechanism, inter-basin SSTA contrast and Caribbean Sea convective activity could serve as potential predictors for U.S. late-summer to mid-fall precipitation. We propose to develop a seasonal forecast system for U.S. regional precipitation, targeting the ASO season, based on this newly identified mechanism. We also propose a new optimized hybrid forecast system that combines dynamic and empirical models to maximize the forecast skill. Our proposal addresses the U.S. rainfall prediction gap in late-summer to mid-fall through the innovative applications of inter-basin SST and convection parameters in a novel optimized hybrid seasonal forecast system. This proposal is therefore highly relevant to the OAR/WPO call for "Innovative Research for the Precipitation Prediction Grand Challenge". This project explores the use of machine learning.
U.S. precipitation variability and associated hazardous weather events such as floods, droughts, severe thunderstorms and deadly heat waves exert a range of significant socio-economic impacts. Previous studies have extensively studied large-scale climate processes that are linked to U.S. precipitation variability. For instance, El Nio-Southern Oscillation (ENSO) significantly modulates late-fall to mid-spring (November-April) precipitation over the U.S. through the associated extratropical Rossby wave propagations to North America. However, in late-spring to mid-fall (May-October), ENSO-induced extratropical atmospheric teleconnection is relatively weak, thus playing a minor role in U.S. precipitation variability. For this reason, the seasonal prediction skill for U.S. precipitation is relatively poor in the warm season compared to the cold season. Our proposal addresses the U.S. rainfall prediction gap in late-summer to mid-fall through the innovative applications of inter-basin SST and convection parameters in a novel optimized hybrid seasonal forecast system, which make our proposal both innovative and significant.
N/A
N/A
NOAA/OAR/AOML,
NOAA/OAR/PSL,
CIMAS/University of Miami,
CIRES/University of Colorado,
NOAA/NWS/NCEP/CPC
an optimized hybrid seasonal forecast system for u s regional precipitation in late summer to mid fall based on inter basin sst and convection parameters, the main goal of this proposal is to develop an optimized hybrid seasonal forecast system for u s precipitation in aso using the inter basin ssta contrast and cs convection, in the u s late fall to mid spring november april large scale precipitation variability is predominantly driven by pacific sea surface temperature anomalies sstas associated with el nio southern oscillation through extratropical rossby wave trains to north america while early to mid summer june july precipitation variability especially east of the rockies is largely linked to atlantic sstas and associated variation in the north atlantic subtropical high however these well established relationships almost completely break down in late summer to mid fall august october aso thus operational seasonal forecast models have generally low skill in aso for u s regional precipitation leaving a large portion of u s populations vulnerable to unpredictable extreme events in that season our recent study kim et al 2020 identified key physical processes in the atlantic and pacific that directly modulate late summer to mid fall u s precipitation variability and demonstrated these connections using observations a fully coupled model and a series of simple atmospheric model experiments the study showed that in aso the inter basin contrast between pacific and atlantic sstas significantly influences convective activity over the caribbean sea this regional change in convection directly modulates the strength of the north american low level jet and thus drives u s precipitation variability east of the rockies through this mechanism inter basin ssta contrast and caribbean sea convective activity could serve as potential predictors for u s late summer to mid fall precipitation we propose to develop a seasonal forecast system for u s regional precipitation targeting the aso season based on this newly identified mechanism we also propose a new optimized hybrid forecast system that combines dynamic and empirical models to maximize the forecast skill our proposal addresses the u s rainfall prediction gap in late summer to mid fall through the innovative applications of inter basin sst and convection parameters in a novel optimized hybrid seasonal forecast system this proposal is therefore highly relevant to the oar wpo call for "innovative research for the precipitation prediction grand challenge" this project explores the use of machine learning
Not Applicable (N/A)
NOAA/OAR/AOML, NOAA/OAR/PSL, CIMAS/University of Miami, CIRES/University of Colorado, NOAA/NWS/NCEP/CPC
The main goal of this proposal is to develop an optimized hybrid seasonal forecast system
for U.S. precipitation in ASO using the inter-basin SSTA contrast and CS convection.
0
Impact of the western boundary currents on extreme precipitation: A case study in the Atlantic Ocean from along-transect measurements and high-resolution model simulations.
Dong
S2S
check_circle
2020
Level 3
This proposal aims at using synchronous upper-ocean, surface atmospheric measurements, and high-resolution coupled climate models to improve understanding of the
physics and the magnitude of the coupling between WBC systems (Gulf Stream and Brazil Current) and the atmosphere, with the main goal of assessing the impact of the ocean mesoscale on rainfall over nearby continental regions.
Sat Aug 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Coupled Models & Techniques, Dynamics and Nesting, Reanalysis & Reforecasting, Precipitation Grand Challenge
PPGC-ESPC
This proposal aims at using synchronous upper-ocean, surface atmospheric measurements, and high-resolution coupled climate models to improve understanding of the physics and the magnitude of the coupling between WBC systems (Gulf Stream and Brazil Current) and the atmosphere, with the main goal of assessing the impact of the ocean mesoscale on rainfall over nearby continental regions. For this, we will leverage observations from existing global ocean arrays, ships of opportunity XBT (expendable bathythermograph) transects augmented with meteorological stations, and existing coupled climate model simulations with different resolutions. An eddy-resolving, coupled simulation is key for exploring these processes in a region where reanalysis products and climate models underestimate temperature gradients and fluxes.
Most of the U.S. and South American populations live near the shore and are therefore affected directly by weather patterns modulated by the variability of WBCs. This work will improve forecasts of these weather patterns.
N/A
N/A
NOAA/OAR/AOML,
CIMAS/University of Miami,
University of Miami,
Universidade Federal do Rio de Janeiro
WPO20-S2S-I-1
Water Extremes
Florida
Subseasonal to Seasonal Program (S2S)
August 2020 - July 2021
Atmospheric Physics, Coupled Models & Techniques, Dynamics and Nesting, Reanalysis & Reforecasting, Precipitation Grand Challenge
impact of the western boundary currents on extreme precipitation a case study in the atlantic ocean from along transect measurements and high resolution model simulations, this proposal aims at using synchronous upper ocean surface atmospheric measurements and high resolution coupled climate models to improve understanding of the physics and the magnitude of the coupling between wbc systems gulf stream and brazil current and the atmosphere with the main goal of assessing the impact of the ocean mesoscale on rainfall over nearby continental regions, this proposal aims at using synchronous upper ocean surface atmospheric measurements and high resolution coupled climate models to improve understanding of the physics and the magnitude of the coupling between wbc systems gulf stream and brazil current and the atmosphere with the main goal of assessing the impact of the ocean mesoscale on rainfall over nearby continental regions for this we will leverage observations from existing global ocean arrays ships of opportunity xbt expendable bathythermograph transects augmented with meteorological stations and existing coupled climate model simulations with different resolutions an eddy resolving coupled simulation is key for exploring these processes in a region where reanalysis products and climate models underestimate temperature gradients and fluxes
Not Applicable (N/A)
NOAA/OAR/AOML, CIMAS/University of Miami, University of Miami, Universidade Federal do Rio de Janeiro
This proposal aims at using synchronous upper-ocean, surface atmospheric measurements, and high-resolution coupled climate models to improve understanding of the physics and the magnitude of the coupling between WBC systems (Gulf Stream and Brazil Current)
0
Using high-resolution simulations (LES and CAM) from field experiments to improve physics parameterizations for medium-range and sub-seasonal forecasting
Olson
S2S
check_circle
2020
Level 3
The goal of this proposal is to demonstrate improved overall precipitation in both baroclinic and convective regimes globally.
Wed Jun 30 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting, Earth Prediction Innovation Center (EPIC), In-situ Observation Technologies and Sensors, Parameterization, Precipitation Grand Challenge
PPGC-S2S
Community Atmosphere Model (CAM)
Improving medium range (MER) and subseasonal to seasonal (S2S) forecasting will require a strong focus on improving forecasting in the tropics. Critical research is needed to support development of the unified forecast system including an emphasis on the prediction of tropical boundary layers and their important trade-wind cumuli. These aspects represent the environment that fosters the deep convection that ultimately drives the global circulations. This proposal helps to fill this scientific gap by leveraging multi-scale observational data from the EUREC4 A and ATOMIC field campaigns (Bony et al. 2017). The EUREC4 A and ATOMIC field campaigns took place in parallel between 20 January and 20 February 2020 to the east of Barbados over the western tropical Atlantic. The dataset consists of dropsondes, profiling instruments, and in situ data, which characterizes the shallow cumulus clouds and the large-scale environment they evolve within. This dataset will be leveraged along with high-resolution simulations (i.e. LES or CAM) which are currently under way for an EPIC/OWAQ proposal to help direct the developments of model physical parameterizations to represent the moist-turbulent processes in the tropical boundary layer at coarser resolutions. Increased collaboration with other international modeling groups that are also participating in this experiment may lead to further insights that increase the impact of this research.
Improved medium range and S2S forecasts will benefit many sectors, especially water resources and agriculture.
N/A
N/A
NOAA/OAR/GSL
WPO20-S2S-I-3
Water Extremes
Colorado
Subseasonal to Seasonal Program (S2S)
July 2020 - June 2021
Atmospheric Physics, Dynamics and Nesting, Earth Prediction Innovation Center (EPIC), In-situ Observation Technologies and Sensors, Parameterization, Precipitation Grand Challenge
using high resolution simulations les and cam from field experiments to improve physics parameterizations for medium range and sub seasonal forecasting, the goal of this proposal is to demonstrate improved overall precipitation in both baroclinic and convective regimes globally, improving medium range mer and subseasonal to seasonal s2s forecasting will require a strong focus on improving forecasting in the tropics critical research is needed to support development of the unified forecast system including an emphasis on the prediction of tropical boundary layers and their important trade wind cumuli these aspects represent the environment that fosters the deep convection that ultimately drives the global circulations this proposal helps to fill this scientific gap by leveraging multi scale observational data from the eurec4 a and atomic field campaigns bony et al 2017 the eurec4 a and atomic field campaigns took place in parallel between 20 january and 20 february 2020 to the east of barbados over the western tropical atlantic the dataset consists of dropsondes profiling instruments and in situ data which characterizes the shallow cumulus clouds and the large scale environment they evolve within this dataset will be leveraged along with high resolution simulations i e les or cam which are currently under way for an epic owaq proposal to help direct the developments of model physical parameterizations to represent the moist turbulent processes in the tropical boundary layer at coarser resolutions increased collaboration with other international modeling groups that are also participating in this experiment may lead to further insights that increase the impact of this research
Not Applicable (N/A)
NOAA/OAR/GSL
The goal of this proposal is to demonstrate improved overall precipitation in both baroclinic and convective regimes globally.
0
Leveraging GEFSv12 Reforecasts to Improve National Blend of Models Precipitation Forecasts
Hamill
S2S
check_circle
2020
Level 3
This project will replace the current NBM procedure of training precipitation adjustments for GEFSv12 with a short training data set with the use of a multi-decadal training period afforded by GEFSv12 reforecasts.
Thomas Hamill
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Jul 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Reanalysis & Reforecasting, Model & Forecast Guidance, Post-processing, Precipitation Grand Challenge
PPGC
Global Ensemble Forecast System (GEFS), National Blend of Models (NBM)
The NBM is the flagship program for the statistical postprocessing of weather elements. It is also the passion and strategic priority of the NWS Director, Louis Uccellini. The NBM statistically modifies and synthesizes numerical guidance from a variety of prediction systems, correcting the guidance for discrepancies between the forecasts and past observations/analyses. It is used to provide the initial gridded fields of critical weather elements (precipitation, temperature, winds, clouds, and so forth) to NWS forecast offices. These, with additional forecaster modification if deemed necessary, underlie the NWS operational forecasts.
The result will be that gridded precipitation forecasts from the NBM will be higher in quality and will require less modification by human forecasters at NWS forecast offices, especially precipitation forecasts of high-impact events. The ultimate official NWS precipitation forecasts will also be more seamless across forecast office boundaries, thereby improving the credibility of NWS precipitation guidance. The NWS forecasters will be able to turn their attention more from grid editing to the Weather-Ready Nation vision of providing decision support to key customers.
MDL
N/A
NOAA/OAR/PSL
PGC01.FY20.1
Water Extremes
Colorado
Subseasonal to Seasonal Program (S2S)
June 2020 - July 2021
Dynamics and Nesting, Reanalysis & Reforecasting, Model & Forecast Guidance, Post-processing, Precipitation Grand Challenge
leveraging gefsv12 reforecasts to improve national blend of models precipitation forecasts, this project will replace the current nbm procedure of training precipitation adjustments for gefsv12 with a short training data set with the use of a multi decadal training period afforded by gefsv12 reforecasts, the nbm is the flagship program for the statistical postprocessing of weather elements it is also the passion and strategic priority of the nws director louis uccellini the nbm statistically modifies and synthesizes numerical guidance from a variety of prediction systems correcting the guidance for discrepancies between the forecasts and past observations analyses it is used to provide the initial gridded fields of critical weather elements precipitation temperature winds clouds and so forth to nws forecast offices these with additional forecaster modification if deemed necessary underlie the nws operational forecasts
NWS Meteorological Development Laboratory (MDL)
NOAA/OAR/PSL
This project will replace the current NBM procedure of training precipitation adjustments for GEFSv12 with a short training data set with the use of a multi-decadal training period afforded by GEFSv12 reforecasts.
0
Identifying physical processes responsible for tropical UFS errors and their relation to UFS week 2-4 precipitation predictability in the western US
Dias
S2S
check_circle
2020
Level 3
This project would promote necessary steps for informing UFS improvements, such as the continued direct collaboration between observational, modeling, and prediction teams at NOAA ESRL. The results will also inform PSL's ongoing contributions to renewing Tropical Pacific and Indian Ocean Observing Systems because the work quantifies which tropical observations improve precipitation predictions.
Sun Jun 20 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Precipitation Grand Challenge, Teleconnections
PPGC
Unified Forecast System (UFS)
The western United States is particularly vulnerable to precipitation forecast uncertainty because precipitation is infrequent and water availability is low. This uncertainty is amplified by teleconnections to the tropical IndoPacific. The IndoPacific is imperfectly observed and the origin of complex weather and climate patterns known to impact the western US, such as ENSO, atmospheric rivers, and the MJO. Small changes in these extreme events responsible for most western US precipitation can send large urban areas into either drought or flood conditions.
This will help improve predictions of amounts and locations of rain and snow, as well as other precipitation related quantities. This project is well-aligned with the Precipitation Challenge goals by testing and guiding future improvements of the UFS.
CPC
N/A
NOAA/OAR/PSL,
NOAA/OAR/GSL,
CIRES/University of Colorado Boulder
PGC02.FY20.1
Water Extremes
Colorado
Subseasonal to Seasonal Program (S2S)
July 2020 - June 2021
Atmospheric Physics, Precipitation Grand Challenge, Teleconnections
identifying physical processes responsible for tropical ufs errors and their relation to ufs week 2 4 precipitation predictability in the western us, this project would promote necessary steps for informing ufs improvements such as the continued direct collaboration between observational modeling and prediction teams at noaa esrl the results will also inform psl's ongoing contributions to renewing tropical pacific and indian ocean observing systems because the work quantifies which tropical observations improve precipitation predictions, the western united states is particularly vulnerable to precipitation forecast uncertainty because precipitation is infrequent and water availability is low this uncertainty is amplified by teleconnections to the tropical indopacific the indopacific is imperfectly observed and the origin of complex weather and climate patterns known to impact the western us such as enso atmospheric rivers and the mjo small changes in these extreme events responsible for most western us precipitation can send large urban areas into either drought or flood conditions
Climate Prediction Center (CPC)
NOAA/OAR/PSL, NOAA/OAR/GSL, CIRES/University of Colorado Boulder
This project would promote necessary steps for informing UFS improvements, such as the continued direct collaboration between observational, modeling, and prediction teams at NOAA ESRL. The results will also inform PSL's ongoing contributions to renewing
0
High-Resolution Precipitation Reanalysis (HRPR)
Zhang
S2S
check_circle
2020
Level 3
This proposal aims at a pilot project towards a decadal High-Resolution Precipitation Reanalysis
(HRPR) using the Multi-Radar Multi-Sensor (MRMS, Zhang et al. 2016) system.
Jian Zhang, Micheal Simpson, Kenneth Howard, Thomas Hamill, Robert Cifelli
Complete
Mon Jun 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon May 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Reanalysis & Reforecasting, Numerical Weather Prediction (NWP), Precipitation Grand Challenge, Radar
PPGC
Multi-Radar/Multi-Sensor System (MRMS)
Improved understanding and predictions of the water cycle at global to local scales is one of NOAA's grand challenges. Flooding and flash flooding are leading causes of weather related fatalities and result in billions of dollars lost to the US economy each year. Accurate estimation and prediction of precipitation at high spatial and temporal resolution is critical across several sectors of the US economy and for the protection and well-being of US communities. This proposal aims at a pilot project towards a decadal High-Resolution Precipitation Reanalysis (HRPR) using the Multi-Radar Multi-Sensor (MRMS) system. The MRMS integrates radar and gauge data and combines with numerical weather prediction (NWP) model output and generates a suite of quantitative precipitation estimation (QPE) products at 1-km and 2-min resolution.
The long-term goal of creating a High-Resolution Precipitation Reanalysis will ultimately be used to improve National Weather Service hydrological predictions and forecast and nowcast skill.
EMC
N/A
NOAA/OAR/NSSL,
NOAA/OAR/GSL,
CIMMS/University of Oklahoma,
NOAA/OAR/PSL
PGC03.FY20.1
Water Extremes
Oklahoma
Subseasonal to Seasonal Program (S2S)
June 2020 - May 2021
Dynamics and Nesting, Reanalysis & Reforecasting, Numerical Weather Prediction (NWP), Precipitation Grand Challenge, Radar
high resolution precipitation reanalysis hrpr, this proposal aims at a pilot project towards a decadal high resolution precipitation reanalysis hrpr using the multi radar multi sensor mrms zhang et al 2016 system, improved understanding and predictions of the water cycle at global to local scales is one of noaa's grand challenges flooding and flash flooding are leading causes of weather related fatalities and result in billions of dollars lost to the us economy each year accurate estimation and prediction of precipitation at high spatial and temporal resolution is critical across several sectors of the us economy and for the protection and well being of us communities this proposal aims at a pilot project towards a decadal high resolution precipitation reanalysis hrpr using the multi radar multi sensor mrms system the mrms integrates radar and gauge data and combines with numerical weather prediction nwp model output and generates a suite of quantitative precipitation estimation qpe products at 1 km and 2 min resolution
Environmental Modeling Center (EMC)
NOAA/OAR/NSSL, NOAA/OAR/GSL, CIMMS/University of Oklahoma, NOAA/OAR/PSL
This proposal aims at a pilot project towards a decadal High-Resolution Precipitation Reanalysis
(HRPR) using the Multi-Radar Multi-Sensor (MRMS, Zhang et al. 2016) system.
0
Critical Steps Toward a National Ocean Modeling Capability in Support of the National Earth System Prediction Capability - Phase II
Hallberg
S2S
check_circle
2018
Level 3
This proposal aims to the HYCOM features we identified in FY17 to MOM6 and extending our testing on both a 1/12 degree global tripole grid and a "low resolution" setup on a 0.72 degree global tripole grid.
Robert Hallberg, Eric Chassignet
Complete
Wed Aug 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
HYbrid Coordinates Ocean Model (HYCOM), Modular Ocean Model (MOM), Unified Forecast System (UFS)
The National Earth System Prediction Capability (ESPC) effort aims to provide improved environmental prediction capabilities for the entire earth climate system on timescales of hours out to 30 years for both civilian and military applications. An advanced global ocean modeling capability is one of the central components of this system.
Improved MOM6 grid management, data assimilation progress, simulation of tides, and boundary conditions supporting nests.
N/A
N/A
NOAA/OAR/GFDL,
COAPS/Florida State University
OWAQ18-ESPC-I-1
Relevant to All Hazards
Florida, New Jersey
Subseasonal to Seasonal Program (S2S)
August 2018 - September 2019
Dynamics and Nesting
critical steps toward a national ocean modeling capability in support of the national earth system prediction capability - phase ii, this proposal aims to the hycom features we identified in fy17 to mom6 and extending our testing on both a 1 12 degree global tripole grid and a "low resolution" setup on a 0 72 degree global tripole grid, the national earth system prediction capability espc effort aims to provide improved environmental prediction capabilities for the entire earth climate system on timescales of hours out to 30 years for both civilian and military applications an advanced global ocean modeling capability is one of the central components of this system
Not Applicable (N/A)
NOAA/OAR/GFDL, COAPS/Florida State University
This proposal aims to the HYCOM features we identified in FY17 to MOM6 and extending our testing on both a 1/12 degree global tripole grid and a "low resolution" setup on a 0.72 degree global
1
Preliminary Steps Toward a National Ocean Modeling Capability in Support of the National Earth System Prediction Capability - Phase III
Hallberg
S2S
check_circle
2019
Level 3
This proposal aims to add all the HYCOM features we identified
in the earlier phases to MOM6.
Robert Hallberg, Eric Chassignet
Complete
Tue Oct 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Information Seeking & Processing
ESPC
HYbrid Coordinates Ocean Model (HYCOM), Modular Ocean Model (MOM), Unified Forecast System (UFS)
This project aims to take the key steps to evaluating the suitability of MOM6 as a
successor to HYCOM in operational Earth System Predictions by both the Navy and NOAA.
Improved MOM6 grid management, data assimilation progress, simulation of tides, and boundary conditions supporting nests.
N/A
N/A
NOAA/OAR/GFDL,
COAPS/Florida State University
OWAQ19-ESPC-I-1
Relevant to All Hazards
Florida, New Jersey
Subseasonal to Seasonal Program (S2S)
October 2019 - September 2021
Dynamics and Nesting, Information Seeking & Processing
preliminary steps toward a national ocean modeling capability in support of the national earth system prediction capability phase iii, this proposal aims to add all the hycom features we identified in the earlier phases to mom6, this project aims to take the key steps to evaluating the suitability of mom6 as a successor to hycom in operational earth system predictions by both the navy and noaa
Not Applicable (N/A)
NOAA/OAR/GFDL, COAPS/Florida State University
This proposal aims to add all the HYCOM features we identified
in the earlier phases to MOM6.
0
Preliminary Steps Toward a National Ocean Modeling Capability in Support of the National Earth System Prediction Capability - Phase IV
Hallberg
S2S
check_circle
2020
Level 3
This proposal directly addresses the goal of unifying NOAA modeling efforts on the global ocean side, while also enhancing ocean modeling collaborations between NOAA and the U.S. Navy.
Robert Hallberg, Eric Chassignet
Complete
Thu Oct 01 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Information Seeking & Processing
ESPC
Modular Ocean Model (MOM), Unified Forecast System (UFS)
The National Earth System Prediction Capability (ESPC) effort aims to provide improved environmental prediction capabilities for the entire earth climate system on timescales of hours out to 30 years for both civilian and military applications. An advanced global ocean modeling capability is one of the central components of this system.
This proposal directly addresses the goal of unifying NOAA modeling efforts on the global ocean side, while also enhancing ocean modeling collaborations between NOAA and the U.S. Navy.
N/A
N/A
NOAA/OAR/GFDL,
COAPS/Florida State University
OWAQ20-ESPC-I-1
Relevant to All Hazards
Florida, New Jersey
Subseasonal to Seasonal Program (S2S)
October 2020 - September 2021
Dynamics and Nesting, Information Seeking & Processing
preliminary steps toward a national ocean modeling capability in support of the national earth system prediction capability phase iv, this proposal directly addresses the goal of unifying noaa modeling efforts on the global ocean side while also enhancing ocean modeling collaborations between noaa and the u s navy, the national earth system prediction capability espc effort aims to provide improved environmental prediction capabilities for the entire earth climate system on timescales of hours out to 30 years for both civilian and military applications an advanced global ocean modeling capability is one of the central components of this system
Not Applicable (N/A)
NOAA/OAR/GFDL, COAPS/Florida State University
This proposal directly addresses the goal of unifying NOAA modeling efforts on the global ocean side, while also enhancing ocean modeling collaborations between NOAA and the U.S. Navy.
0
Fire Weather Prediction and Associated Hazards to Support Decision-making Across Time Scales
Mahoney
S2S
check_circle
2021
Level 3
This work will allow improved smoke transport estimates over complex terrain, and improved
weather forecasts (near-term to S2S timescales) including wind forecasts crucial for fighting
wildfires.
High-Resolution Rapid Refresh-Smoke (HRRR-S), NOAA's National Air Quality Forecasting Capability (NAQFC), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
The work proposed herein is just the beginning of a longer-term collaborative effort to advance the understanding of the fire weather environment at the subseasonal to seasonal to decadal scales to improve near-term fire weather forecasts, products and tools. Specific tasks to be conducted during the period of performance include: improving the understanding of fire behavior and the estimation of uncertainties in wildfire emissions, plume transport and dispersion and improving understanding of the interaction and feedback between fire and weather and improve predications of the conditions conductive to fire weather at S2S time scales. Additionally, machine learning algorithms will be developed to improve the computational needs limiting the advancement of fire weather models. These activities, working together, will advance fire weather forecasting from the near-term fire weather environment to climate (S2S) timescales.
New physics parameterizations to simulate the effects of fire heat and moisture flux on meteorology in weather and air quality models, a new model grid and configuration to simulate the fire-weather interactions including
pyroCbs, completion of at least one high-resolution meteorological, fire weather and smoke simulation over the western US using the August-September 2020 fire dataset, improved understanding of the impacts from fire heat flux and model physics configurations on operational weather forecasting that could be applied to the Unified Forecast System (UFS) regional convective-allowing model.
fire weather prediction and associated hazards to support decision making across time scales, this work will allow improved smoke transport estimates over complex terrain and improved weather forecasts near term to s2s timescales including wind forecasts crucial for fighting wildfires, the work proposed herein is just the beginning of a longer term collaborative effort to advance the understanding of the fire weather environment at the subseasonal to seasonal to decadal scales to improve near term fire weather forecasts products and tools specific tasks to be conducted during the period of performance include improving the understanding of fire behavior and the estimation of uncertainties in wildfire emissions plume transport and dispersion and improving understanding of the interaction and feedback between fire and weather and improve predications of the conditions conductive to fire weather at s2s time scales additionally machine learning algorithms will be developed to improve the computational needs limiting the advancement of fire weather models these activities working together will advance fire weather forecasting from the near term fire weather environment to climate s2s timescales
Climate Prediction Center (CPC)
NOAA/OAR/GSL, NOAA/OAR/ARL, NOAA/OAR/PSL
This work will allow improved smoke transport estimates over complex terrain, and improved
weather forecasts (near-term to S2S timescales) including wind forecasts crucial for fighting
wildfires.
5
Support real-time climate monitoring at Climate Prediction Center (CPC)
Kumar
S2S
2023
Level 3
Implement an operational climate monitoring reanalysis system called Conventional Observations Reanalysis (CORe) at the Climate Prediction Center (CPC).
Arun Kumar
Sun Oct 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Reanalysis & Reforecasting, Model & Forecast Guidance
S2S
Aims to complete historical CORe reanalysis (1950-present) and achieve its operational implementation within FY24 (October 1, 2023 - September 30, 2024), with tasks including offline updates, testing operational scripts, and handover to NCEP Central Operations (NCO).
Improvements to NOAA's long-term forecasting ability through improved monitoring and diagnostics. Ability to analyze real-time data and apply it to long-term forecasts.
CPC
N/A
NOAA/NWS/NCEP/CPC
Relevant to All Hazards
Maryland
Subseasonal to Seasonal Program (S2S)
October 2023 - September 2024
Reanalysis & Reforecasting, Model & Forecast Guidance
support real time climate monitoring at climate prediction center cpc, implement an operational climate monitoring reanalysis system called conventional observations reanalysis core at the climate prediction center cpc, aims to complete historical core reanalysis 1950 present and achieve its operational implementation within fy24 october 1 2023 september 30 2024 with tasks including offline updates testing operational scripts and handover to ncep central operations nco
Climate Prediction Center (CPC)
NOAA/NWS/NCEP/CPC
Implement an operational climate monitoring reanalysis system called Conventional Observations Reanalysis (CORe) at the Climate Prediction Center (CPC).
0
Utilizing AI/ML to improve monthly and seasonal forecast consolidation tools
Goss
S2S
2024
Level 3
Improve monthly and seasonal forecast consolidation tools at NWS's Climate Prediction Center using AI/ML techniques
Aims to develop a third edition of CPC's monthly and seasonal forecast consolidation tools. This version will investigate using an extreme learning machine for temperature and precipitation forecasts
Improvements to monthly and seasonal temp/precip forecast quality and accuracy, implementation of AI/ML in these long-term forecasts
utilizing ai ml to improve monthly and seasonal forecast consolidation tools, improve monthly and seasonal forecast consolidation tools at nws's climate prediction center using ai ml techniques, aims to develop a third edition of cpc's monthly and seasonal forecast consolidation tools this version will investigate using an extreme learning machine for temperature and precipitation forecasts
Climate Prediction Center (CPC)
NOAA/NWS/NCEP/CPC
Improve monthly and seasonal forecast consolidation tools at NWS's Climate Prediction Center using AI/ML techniques
0
Novel Sea Ice Data Assimilation Techniques for Coupled Subseasonal-to-Seasonal Predictions
Bushuk
S2S
2024
Level 3
Anticipated outcomes include producing sea ice ensemble data assimilation experiments incorporating various observational data, creating a real-time quasi-operational seasonal prediction system based on SPEAR, and conducting an intercomparison of the GFDL and UFS sea ice data assimilation and prediction systems to inform future design choices.
Mitch Bushuk, Yongfei Zhang
Sun Sep 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Aug 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques
S2S
It will explore the role of different atmospheric forcing products (e.g., ERA5, CFSR, MERRA2), perform coupled ocean-sea ice assimilation experiments, and develop a real-time prediction system for submitting to the Sea Ice Outlook. A longer-term vision includes transitioning data assimilation efforts from DART to JEDI software to enhance collaboration between UMD/EMC and GFDL.
Improvements to sea-ice forecasting, which will improve long-term weather forecasting and shipping route forecasting
GFDL
N/A
NOAA/OAR/GFDL
Winter Weather
New Jersey
Subseasonal to Seasonal Program (S2S)
September 2024 - August 2025
Coupled Models & Techniques
novel sea ice data assimilation techniques for coupled subseasonal to seasonal predictions, anticipated outcomes include producing sea ice ensemble data assimilation experiments incorporating various observational data creating a real time quasi operational seasonal prediction system based on spear and conducting an intercomparison of the gfdl and ufs sea ice data assimilation and prediction systems to inform future design choices, it will explore the role of different atmospheric forcing products e g era5 cfsr merra2 perform coupled ocean sea ice assimilation experiments and develop a real time prediction system for submitting to the sea ice outlook a longer term vision includes transitioning data assimilation efforts from dart to jedi software to enhance collaboration between umd emc and gfdl
NOAA/OAR/GFDL
Anticipated outcomes include producing sea ice ensemble data assimilation experiments incorporating various observational data, creating a real-time quasi-operational seasonal prediction system based on SPEAR, and conducting an intercomparison of the GFDL and UFS sea ice
0
GSL contributions to the Seasonal Forecast System
Sun
S2S
2024
Level 3
Improve ENSO prediction accuracy through investigating model biases
Shan Sun
Tue Oct 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
GSL will investigate model biases related to cloud representation, radiation in the tropics and sub-tropics, and sea surface temperature (SST) errors. This will involve evaluating simulations with different physics parameterizations and exploring changes in vertical and horizontal ocean model resolutions.
Improved ENSO forecasts will strengthen temperature and precipitation forecasts on subseasonal to seasonal scales (weeks to months) across the Continental US
GSL
N/A
NOAA/OAR/GSL
Extreme Temperatures, Water Extremes, Winter Weather
gsl contributions to the seasonal forecast system, improve enso prediction accuracy through investigating model biases, gsl will investigate model biases related to cloud representation radiation in the tropics and sub tropics and sea surface temperature sst errors this will involve evaluating simulations with different physics parameterizations and exploring changes in vertical and horizontal ocean model resolutions
NOAA/OAR/GSL
Improve ENSO prediction accuracy through investigating model biases
0
National Climate Model Archive (NCMA)
Baldwin
S2S
2024
Level 3
Improve climate model archiving and enhance data access and availability for NOAA and the climate model community.
Rich Baldwin
Thu Feb 01 2024 03:00:00 GMT-0500 (Eastern Standard Time)
Fri Jan 31 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Flow of Weather Risk Information, Forecast Product Usability & Design, Information Seeking & Processing, "Organizational Dimensions (e.g., culture, management, communication)"
S2S
This project will maintain archive data and provide services for improved access and user support, by making existing files more accessible to the cloud and assessing data storage and retention needs
Improved data accessibility will enhance collaborations on modeling projects, helping to improve long-term weather forecast accuracy
NCEI
N/A
NCEI
Relevant to All Hazards
North Carolina
Subseasonal to Seasonal Program (S2S)
February 2024 - January 2025
Flow of Weather Risk Information, Forecast Product Usability & Design, Information Seeking & Processing, "Organizational Dimensions (e.g., culture, management, communication)"
national climate model archive ncma, improve climate model archiving and enhance data access and availability for noaa and the climate model community, this project will maintain archive data and provide services for improved access and user support by making existing files more accessible to the cloud and assessing data storage and retention needs
NCEI
Improve climate model archiving and enhance data access and availability for NOAA and the climate model community.
0
Accelerating research and development of an AI-ready coupled reanalysis
Frolov
S2S
2024
Level 3
Create a high-resolution coupled reanalysis (up to ~13 km, possibly higher resolution over CONUS) as a training dataset for AI applications.
Sergey Frolov
Tue Oct 01 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
This will involve a sequence of increasingly complex scout runs over the last 10 years (2014-present), starting with low resolution and simplified data assimilation.
The products of this projects will be used in AI training, which will help improve forecasting, sensitivity studies, and the initial conditions which are used in models to predict future atmospheric conditions
accelerating research and development of an ai ready coupled reanalysis, create a high resolution coupled reanalysis up to ~13 km possibly higher resolution over conus as a training dataset for ai applications, this will involve a sequence of increasingly complex scout runs over the last 10 years 2014 present starting with low resolution and simplified data assimilation
NOAA/OAR/PSL
Create a high-resolution coupled reanalysis (up to ~13 km, possibly higher resolution over CONUS) as a training dataset for AI applications.
0
S2S precipitation forecasts for water management in the western U.S.
Webb
S2S
2024
Level 3
S2S ML/AI Modeling: Develop and assess the skill of a hybrid explainable deep learning and model analog-based forecast system for subseasonal-to-seasonal AR precipitation prediction in the western U.S and compare skill to skill of operational and experimental subseasonal-to-seasonal forecasts. Subseasonal Predictive Understanding: Determine the sources of subseasonal-to-seasonal AR precipitation predictability in the western U.S. based on the developed forecast hybrid explainable deep learning and model analog-based forecast system, focusing on ENSO, MJO and antecedent flow regimes.
Project Manager: Robert Webb, Project Team: Benjamin Moore, Sergey Frolov, Laura Slivinski, Lisa Bengtsson, Juliana Dias, Gary Wick, Mimi Hughes, Rob Cifelli, Andrew Hoell
Mon Dec 01 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Artificial Intelligence (AI) / Machine Learning (ML), Coupled Models & Techniques, High-Performance Computing (HPC), Model & Forecast Guidance, Water in the West
S2S
PSL
N/A
NOAA/OAR/PSL
Water Extremes
Colorado
Subseasonal to Seasonal Program (S2S)
July 2024 - December 2025
Artificial Intelligence (AI) / Machine Learning (ML), Coupled Models & Techniques, High-Performance Computing (HPC), Model & Forecast Guidance, Water in the West
s2s precipitation forecasts for water management in the western u s, s2s ml ai modeling develop and assess the skill of a hybrid explainable deep learning and model analog based forecast system for subseasonal to seasonal ar precipitation prediction in the western u s and compare skill to skill of operational and experimental subseasonal to seasonal forecasts subseasonal predictive understanding determine the sources of subseasonal to seasonal ar precipitation predictability in the western u s based on the developed forecast hybrid explainable deep learning and model analog based forecast system focusing on enso mjo and antecedent flow regimes
NOAA/OAR/PSL
S2S ML/AI Modeling: Develop and assess the skill of a hybrid explainable deep learning and model analog-based forecast system for subseasonal-to-seasonal AR precipitation prediction in the western U.S and compare skill to skill of operational
0
VORTEX-SE: Improving Understanding and Predictability
of Tornadic Storms in the Southeastern U.S. Using
Intensive Observations and High-Resolution Modeling
Weiss
VORTEX
check_circle
2015
Level 3
The goal of this project was to improve our understanding of vertical-vorticity rivers: why they develop and their stability, or conditions leading to collapse into a vortex.
Christopher Weiss, Eric Bruning, Johannes Dahl, David Dowell, Curtis Alexander
Thu Aug 31 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, In-situ Observation Technologies and Sensors, Post-processing
VORTEX FY15
The VORTEX-SE project represents the first concentrated effort to improve our
understanding of tornadic storms, specifically in the southeastern United States.
Proposed in this project is a multi-faceted, relatively short-fuse effort, driven by
intensive in situ observation (surface and upper-air) and high-resolution numerical
modeling.
The VORTEX-SE project provides a unique opportunity to understand aspects of the
southeastern United States environment that influence the genesis and maintenance
of tornadoes.
N/A
N/A
Texas Tech University
NA15OAR4590226
Severe Weather
Colorado, Texas
VORTEX
September 2015 - August 2017
Data Assimilation and Ensembles, Dynamics and Nesting, In-situ Observation Technologies and Sensors, Post-processing
vortex se improving understanding and predictability of tornadic storms in the southeastern u s using intensive observations and high resolution modeling, the goal of this project was to improve our understanding of vertical vorticity rivers why they develop and their stability or conditions leading to collapse into a vortex, the vortex se project represents the first concentrated effort to improve our understanding of tornadic storms specifically in the southeastern united states proposed in this project is a multi faceted relatively short fuse effort driven by intensive in situ observation surface and upper air and high resolution numerical modeling
Not Applicable (N/A)
Texas Tech University
The goal of this project was to improve our understanding of vertical-vorticity rivers: why they develop and their stability, or conditions leading to collapse into a vortex.
1
Addressing Interconnections between the Built and Natural
Environments through Post-Event Damage Surveys
Godfrey
VORTEX
check_circle
2015
Level 3
The goal of this project was to use post-tornado damage surveys to model near-surface wind and damage fields to understand interconnections between built and natural environments and estimate damage severity from tornadoes.
Christopher Godfrey
Complete
Thu Oct 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
The proposed research thus seeks to identify and explain the interconnectedness of damage in a given community, and how risk from tornadoes can be mitigated by improving our knowledge of these interconnections. We propose research organized around two primary objectives, each subdivided into several tasks. The primary objectives are to 1) undertake a post-storm damage survey and 2) perform computer-based modeling and simulation techniques using the post-storm survey data.
The project takes a step toward
improving the resilience of communities.
N/A
N/A
University of North Carolina at Asheville
NA15OAR4590227
Severe Weather
Georgia, New York, North Carolina
VORTEX
October 2015 - September 2016
Verification & Validation
addressing interconnections between the built and natural environments through post event damage surveys, the goal of this project was to use post tornado damage surveys to model near surface wind and damage fields to understand interconnections between built and natural environments and estimate damage severity from tornadoes, the proposed research thus seeks to identify and explain the interconnectedness of damage in a given community and how risk from tornadoes can be mitigated by improving our knowledge of these interconnections we propose research organized around two primary objectives each subdivided into several tasks the primary objectives are to 1 undertake a post storm damage survey and 2 perform computer based modeling and simulation techniques using the post storm survey data
Not Applicable (N/A)
University of North Carolina at Asheville
The goal of this project was to use post-tornado damage surveys to model near-surface wind and damage fields to understand interconnections between built and natural environments and estimate damage severity from tornadoes.
1
VORTEX-SE: Polarimetric Radar-based Field Campaign Activities and Storm Scale Studies
Carey
VORTEX
check_circle
2015
Level 3
The goal of this project is to study (1) updraft evolution by determining if there is a relationship between lightning flash rates and convective intensity; and (2) downdraft forcing via inferred microphysical properties.
Lawrence D. Carey
Complete
Thu Oct 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Post-processing, Radar, Verification & Validation
VORTEX FY15
To obtain new knowledge of atmospheric processes that are conducive to tornadoes and ultimately to help improve the prediction of tornadoes, this project proposes to provide multi-Doppler and polarimetric radar, lightning mapping, and disdrometer observations of microphysical, kinematic and lightning properties in three dimensions to the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) - Southeast.
The radar observations proposed herein will also serve several other mesoscale and storm scale hypotheses and therefore provide broad benefit to VORTEX-SE beyond these two objectives. This project will examine the utility of "Lightning Drive", potential "Goldilocks Zone" for tornadogenesis, and larger differential reflectivity (Zdr) inferred drops in non-tornadic hook echoes. Additionally, this project will provide educational engagement opportunities for K-12 students as well as graduate students and the curriculum at the University of Alabama at Huntsville.
As part of Weather-Ready Nation, VORTEX-SE will allow us to engage the Alabama and
southeastern community to heighten awareness of tornado research, watch and warning operations,
preparedness and actions in the event of an emergency.
N/A
N/A
University of Alabama at Huntsville
NA15OAR4590230
Severe Weather
Alabama
VORTEX
October 2015 - September 2017
Post-processing, Radar, Verification & Validation
vortex se polarimetric radar based field campaign activities and storm scale studies, the goal of this project is to study 1 updraft evolution by determining if there is a relationship between lightning flash rates and convective intensity and 2 downdraft forcing via inferred microphysical properties, to obtain new knowledge of atmospheric processes that are conducive to tornadoes and ultimately to help improve the prediction of tornadoes this project proposes to provide multi doppler and polarimetric radar lightning mapping and disdrometer observations of microphysical kinematic and lightning properties in three dimensions to the verification of the origins of rotation in tornadoes experiment vortex - southeast
Not Applicable (N/A)
University of Alabama at Huntsville
The goal of this project is to study (1) updraft evolution by determining if there is a relationship between lightning flash rates and convective intensity; and (2) downdraft forcing via inferred microphysical properties.
1
Improved Understanding of Tornado Development and Risk using Models and Observations from VORTEX-SE
Baldwin
VORTEX
check_circle
2015
Level 3
The goal of this project was to use radar observations to determine parameters (e.g., boundary layer evolution) to better model cool-season tornadogenesis risk in the Southeast.
Michael E. Baldwin, Robin L. Tanamachi, Daniel T. Dawson, Daniel R. Chavas, Ernest M. Agee, Stephen J. Frasier
Complete
Thu Oct 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Radar
VORTEX FY15
This study proposes to contribute to VORTEX-SE by collecting special observations during intensive observing periods, applying state-of-art numerical weather prediction and data assimilation systems, and developing a statistical
tornadogenesis risk model using historical data.
This effort will lay the groundwork for developing novel parameters better
suited to model cool-season tornadogenesis risk in the Southeast.
N/A
N/A
Purdue University
NA15OAR4590231
Severe Weather
Indiana, Massachusetts
VORTEX
October 2015 - September 2017
Atmospheric Physics, Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Radar
improved understanding of tornado development and risk using models and observations from vortex se, the goal of this project was to use radar observations to determine parameters e g boundary layer evolution to better model cool season tornadogenesis risk in the southeast, this study proposes to contribute to vortex se by collecting special observations during intensive observing periods applying state of art numerical weather prediction and data assimilation systems and developing a statistical tornadogenesis risk model using historical data
Not Applicable (N/A)
Purdue University
The goal of this project was to use radar observations to determine parameters (e.g., boundary layer evolution) to better model cool-season tornadogenesis risk in the Southeast.
0
Tornado warning response in the Southeast: Advancing knowledge for action in Tennessee
Ellis
VORTEX
check_circle
2015
Level 3
The goal of this project is to further understand and improve public response to tornado warnings in the Southeast. The project pairs physical and social data to gain rapid results on the timely, vital issue of potential misinterpretation and complacency of Southeast residents during periods of tornadic activity, and how this impacts their response to warning information from the National Weather Service.
Kelsey Ellis, Lisa Reyes Mason
Complete
Thu Oct 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY15
Interviews, Surveys
Mixed Methods
Public
This study aims to further understand and improve public response to tornado warnings in the Southeast. The PIs will use the state of Tennessee as a case study, pairing physical and social data to gain rapid results on the timely, vital issue of potential misinterpretation and complacency of Southeast residents during periods of tornadic activity, and how this impacts their response to warning information from the National Weather Service.
The results of this project will provide information on
which communities are not responding appropriately to life-threatening weather, allowing for strategic and targeted educational outreach by NOAA and partners. Additionally, one goal of the proposed workshops is to share NOAA resources that are available to help individuals stay aware
of immediate severe weather threats.
tornado warning response in the southeast advancing knowledge for action in tennessee, the goal of this project is to further understand and improve public response to tornado warnings in the southeast the project pairs physical and social data to gain rapid results on the timely vital issue of potential misinterpretation and complacency of southeast residents during periods of tornadic activity and how this impacts their response to warning information from the national weather service, this study aims to further understand and improve public response to tornado warnings in the southeast the pis will use the state of tennessee as a case study pairing physical and social data to gain rapid results on the timely vital issue of potential misinterpretation and complacency of southeast residents during periods of tornadic activity and how this impacts their response to warning information from the national weather service
Not Applicable (N/A)
University of Tennessee
The goal of this project is to further understand and improve public response to tornado warnings in the Southeast. The project pairs physical and social data to gain rapid results on the timely, vital issue
6
Multi-disciplinary investigation of concurrent tornadoes and flash floods in the Southeastern US
Schumacher
VORTEX
check_circle
2015
Level 3
The goal of this project is to investigate both meteorological factors that support concurrent, co-located tornado and flash flood events, as well as the challenges experienced by National Weather Service (NWS) forecasters in their detection, warning issuance, and communication processes, using research approaches from multiple disciplines.
Russ Schumacher
Complete
Thu Oct 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
This study proposes to investigate both meteorological factors that support concurrent, co-located tornado and flash flood events, as well as the challenges experienced by National Weather Service (NWS) forecasters in their detection, warning issuance, and communication processes, using research approaches from multiple disciplines.
The goal of this research is to address a gap in the literature about detection and
communication challenges facing NWS forecasters who must grapple with the
complexities of issuing warnings for multiple, simultaneous hazards, like TORFF. Further, this multidisciplinary project will also help with improve the warning process...
multi disciplinary investigation of concurrent tornadoes and flash floods in the southeastern us, the goal of this project is to investigate both meteorological factors that support concurrent co located tornado and flash flood events as well as the challenges experienced by national weather service nws forecasters in their detection warning issuance and communication processes using research approaches from multiple disciplines, this study proposes to investigate both meteorological factors that support concurrent co located tornado and flash flood events as well as the challenges experienced by national weather service nws forecasters in their detection warning issuance and communication processes using research approaches from multiple disciplines
Not Applicable (N/A)
CIRA/Colorado State University
The goal of this project is to investigate both meteorological factors that support concurrent, co-located tornado and flash flood events, as well as the challenges experienced by National Weather Service (NWS) forecasters in their detection,
3
Complacency and False Alarms in Tornado Affected Communities
Egnoto
VORTEX
check_circle
2015
Level 3
The goal of this project is to investigate uncover avenues for advancement in tornado alert / warning communication and the reduction of complacency. It seeks to better understand the needs of the public in an effort to prevent communication barriers and aid in the overall resilience of communities during and after tornado events.
Michael J. Egnoto
Complete
Thu Oct 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Community Preparedness & Resilience, Risk Communication, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY15
Surveys, Focus Groups, Literature Review
Mixed Methods
Public
This project seeks to improve understanding of false alarms and complacency by conducting a series of
focus groups and a large scale, regional survey to the Southeastern US.
This work builds on previous risk and crisis communication models to better comprehend how to reach out to different publics who need tornado alert information, and what channels are most desirable for specific types of information related to tornado awareness.
N/A
N/A
University of Maryland
NA15OAR4590237
Severe Weather
Maryland
VORTEX
October 2015 - September 2017
Community Preparedness & Resilience, Risk Communication, "Social Science (SBES)", Surveys, Focus Groups, Literature Review, Mixed Methods, Public
complacency and false alarms in tornado affected communities, the goal of this project is to investigate uncover avenues for advancement in tornado alert warning communication and the reduction of complacency it seeks to better understand the needs of the public in an effort to prevent communication barriers and aid in the overall resilience of communities during and after tornado events, this project seeks to improve understanding of false alarms and complacency by conducting a series of focus groups and a large scale regional survey to the southeastern us
Not Applicable (N/A)
University of Maryland
The goal of this project is to investigate uncover avenues for advancement in tornado alert / warning communication and the reduction of complacency. It seeks to better understand the needs of the public in an
4
Understanding the Variability and Predictability of Southeastern Severe Storm Environments using Mobile Soundings during VORTEX-SE
Brown
VORTEX
check_circle
2015
Level 3
The goal of the project is to address gaps in understanding of physical processes leading to the evolution of convective storms, by collecting upsonde data; quantifying spatial and temporal variability of severe storms in the Southeastern US; and quantifying impacts of surface heterogeneity upon mesoscale environments in the Southeast US.
Mike Brown, Matthew Parker, Todd Murphy
Complete
Thu Oct 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Verification & Validation
VORTEX FY15
The research team's long-range goal is to improve the understanding and forecasting of convective
weather hazards in the SE. The goal of this application, which is the next step in the pursuit of the
long-range goal, is to sample and analyze the temporal and spatial variability of the mesoscale
environment near tornadic and non-tornadic storms in the SE.
The outcomes of this effort will include the delivery of an important dataset for all VORTEX-SE
PIs; and, experimental results from tests of several hypotheses that have the potential to
improve tornado forecasts.
N/A
N/A
Mississippi State University
NA15OAR4590234
Severe Weather
Louisiana, Mississippi, North Carolina
VORTEX
October 2015 - September 2017
Data Assimilation and Ensembles, Verification & Validation
understanding the variability and predictability of southeastern severe storm environments using mobile soundings during vortex se, the goal of the project is to address gaps in understanding of physical processes leading to the evolution of convective storms by collecting upsonde data quantifying spatial and temporal variability of severe storms in the southeastern us and quantifying impacts of surface heterogeneity upon mesoscale environments in the southeast us, the research team's long range goal is to improve the understanding and forecasting of convective weather hazards in the se the goal of this application which is the next step in the pursuit of the long range goal is to sample and analyze the temporal and spatial variability of the mesoscale environment near tornadic and non tornadic storms in the se
Not Applicable (N/A)
Mississippi State University
The goal of the project is to address gaps in understanding of physical processes leading to the evolution of convective storms, by collecting upsonde data; quantifying spatial and temporal variability of severe storms in the
0
Resolution Dependence of Simulated Convective Storms in the Southeast United States
Romine
VORTEX
check_circle
2015
Level 3
The goal of the project was to to improve Quasi-Linear Convective Systems prediction by investigating grid-
spacing dependence of vortex formation, identifying appropriate model surrogates, and performing process based studies to understand differences in environmental factors impacting strength, spacing, and duration of quasi-linear convective system vortices in numerical models, and to compare against observed vortex characteristics.
Glen Scott Romine
Complete
Thu Oct 01 2015 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Verification & Validation
VORTEX FY15
The proposed work seeks to reveal limits of predictability of QLCS events, identify environmental characteristics that
differentiate non-damaging and damaging QLCSs, and development of more appropriate surrogates to improve forecaster anticipation of damaging QLCS events.
Novel aspects of the proposed work include: Investigation of ensemble predictability and
resolution dependence of QLCS hazards, development of QLCS hazard surrogates, real-time
prediction, and testing of new prediction methods within a quasi-operational environment.
N/A
N/A
National Center for Atmospheric Research
NA15OAR4590238
Severe Weather
Colorado, Oklahoma
VORTEX
October 2015 - September 2018
Data Assimilation and Ensembles, Dynamics and Nesting, Verification & Validation
resolution dependence of simulated convective storms in the southeast united states, the goal of the project was to to improve quasi linear convective systems prediction by investigating grid spacing dependence of vortex formation identifying appropriate model surrogates and performing process based studies to understand differences in environmental factors impacting strength spacing and duration of quasi linear convective system vortices in numerical models and to compare against observed vortex characteristics, the proposed work seeks to reveal limits of predictability of qlcs events identify environmental characteristics that differentiate non damaging and damaging qlcss and development of more appropriate surrogates to improve forecaster anticipation of damaging qlcs events
Not Applicable (N/A)
National Center for Atmospheric Research
The goal of the project was to to improve Quasi-Linear Convective Systems prediction by investigating grid- spacing dependence of vortex formation, identifying appropriate model surrogates, and performing process based studies to understand differences in environmental
1
Improving Risk Communication and Reducing Vulnerabilities for Dynamic Tornado Threats in the Southeastern U.S.
Demuth
VORTEX
check_circle
2016
Level 3
The goal of this project is to help improve tornado forecast and warning communication and response by developing new knowledge about how people's risk perceptions and responses evolve dynamically with a tornado threat; and how these interact with evolving risk information and vulnerabilities for tornado events in the southeastern US.
Julie Demuth
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
With a goal of helping improve tornado forecast and warning communication and response, this project will develop new knowledge about (a) how people's risk perceptions and responses evolve dynamically with a tornado threat; and (b) how these interact with evolving risk information and vulnerabilities for tornado events in the southeastern US.
Our research will be informed throughout by interfacing with National Weather Service (NWS) collaborators and by ongoing VORTEX-SE work that focuses on forecasters and the warning
system. Guided by these collaborations, we will use the research to develop knowledge that can help NWS forecasters and their key partners improve risk communication efforts in ways that can reduce vulnerabilities and enhance resilience to future tornado threats.
N/A
N/A
National Center for Atmospheric Research
NA16OAR4590217
Severe Weather
Colorado
VORTEX
October 2016 - September 2019
Risk Communication, Risk Perception & Response, "Social Science (SBES)", Interviews, Social Media Analysis, Mixed Methods, Forecasters, Public
improving risk communication and reducing vulnerabilities for dynamic tornado threats in the southeastern u s, the goal of this project is to help improve tornado forecast and warning communication and response by developing new knowledge about how people's risk perceptions and responses evolve dynamically with a tornado threat and how these interact with evolving risk information and vulnerabilities for tornado events in the southeastern us, with a goal of helping improve tornado forecast and warning communication and response this project will develop new knowledge about a how people's risk perceptions and responses evolve dynamically with a tornado threat and b how these interact with evolving risk information and vulnerabilities for tornado events in the southeastern us
Not Applicable (N/A)
National Center for Atmospheric Research
The goal of this project is to help improve tornado forecast and warning communication and response by developing new knowledge about how people's risk perceptions and responses evolve dynamically with a tornado threat; and how
1
Lay Judgments of Environmental Cues That Signal a Tornado
Broomell
VORTEX
check_circle
2016
Level 3
The goal of this project was to understand perceptions of tornado risk, to inform strategies for promoting people's safety and wellbeing in areas vulnerable to severe storms.
Stephen Broomell
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Our approach to improve decision-making is to analyze the efficacy of self-validation based on the normative-descriptive-prescriptive framework developed from decision sciences.
This framework will help generate prescriptions to improve the warning process and potentially save lives. We are proposing to build empirically valid prescriptions by performing descriptive
analysis to document the environmental cues that the general public uses to perform self-validation.
This project will inform strategies for promoting people's safety and wellbeing in areas vulnerable to storm damage, sensitive to their natural tendency to verify environmental cues.
lay judgments of environmental cues that signal a tornado, the goal of this project was to understand perceptions of tornado risk to inform strategies for promoting people's safety and wellbeing in areas vulnerable to severe storms, our approach to improve decision making is to analyze the efficacy of self validation based on the normative descriptive prescriptive framework developed from decision sciences this framework will help generate prescriptions to improve the warning process and potentially save lives we are proposing to build empirically valid prescriptions by performing descriptive analysis to document the environmental cues that the general public uses to perform self validation
Not Applicable (N/A)
Carnegie Mellon University
The goal of this project was to understand perceptions of tornado risk, to inform strategies for promoting people's safety and wellbeing in areas vulnerable to severe storms.
1
Collaborative Research: Understanding How Uncertainty in Severe Weather Information Affects Decisions
LaDue
VORTEX
check_circle
2016
Level 3
The goal of this project is to examine how National Weather Service forecasters, broadcast meteorologists, emergency managers (EMs), and publics within the the NWS Huntsville Warning Area interpret, communicate, and make decisions under the uncertainty associated with difficult to forecast tornadoes and associated severe weather events.
Daphne LaDue
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Interviews, Surveys, Focus Groups, Social Media Analysis, Ethnographic Observation
Mixed Methods
Forecasters, Emergency Managers, Other Core Partners, Broadcast Meteorologists, Public
This social science project will examine how National Weather Service forecasters, broadcast meteorologists, emergency managers (EMs), and publics interpret, communicate, and
make decisions under the uncertainty associated with difficult to forecast tornadoes and associated severe weather events.
By examining forecasters, broadcasters, EMs, and publics before, during, and after severe weather
events, this research will focus on integrating these actor/communication networks.
N/A
N/A
University of Oklahoma
NA16OAR4590223
Severe Weather
Alabama, Oklahoma
VORTEX
October 2016 - September 2019
Communicating Probabilities & Uncertainty, Decision-Making, "Social Science (SBES)", Interviews, Surveys, Focus Groups, Social Media Analysis, Ethnographic Observation, Mixed Methods, Forecasters, Emergency Managers, Other Core Partners, Broadcast Meteorologists, Public
collaborative research understanding how uncertainty in severe weather information affects decisions, the goal of this project is to examine how national weather service forecasters broadcast meteorologists emergency managers ems and publics within the the nws huntsville warning area interpret communicate and make decisions under the uncertainty associated with difficult to forecast tornadoes and associated severe weather events, this social science project will examine how national weather service forecasters broadcast meteorologists emergency managers ems and publics interpret communicate and make decisions under the uncertainty associated with difficult to forecast tornadoes and associated severe weather events
Not Applicable (N/A)
University of Oklahoma
The goal of this project is to examine how National Weather Service forecasters, broadcast meteorologists, emergency managers (EMs), and publics within the the NWS Huntsville Warning Area interpret, communicate, and make decisions under the uncertainty
2
Convective mode and Tennessee tornadoes: Climatology, warning procedures, and false alarm rates
Ellis
VORTEX
check_circle
2017
Level 3
The goal of this project is to specifically address the climatological and operational aspects of the convective mode of Southeast tornadic events and examine their relevance to tornado vulnerability.
Kelsey Ellis
Complete
Sun Jan 01 2017 03:00:00 GMT-0500 (Eastern Standard Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Physical-Social Data Linkages, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY16
Interviews
Mixed Methods
Forecasters
The proposed interdisciplinary study will specifically address the climatological and operational aspects of the convective mode
of Southeast tornadic events and examine their relevance to tornado vulnerability.
Through understanding the role of convective mode in the tornado climatology, FARs, and warning generation procedures, and by mutual sharing of ideas with NWS forecasters, this study may improve the
communication network between forecasters and the public in the unique tornado climate of the Southeast.
N/A
N/A
University of Tennessee
NA16OAR4590222
Severe Weather
Tennessee
VORTEX
January 2017 - June 2020
Physical-Social Data Linkages, Social Vulnerability, "Social Science (SBES)", Interviews, Mixed Methods, Forecasters
convective mode and tennessee tornadoes climatology warning procedures and false alarm rates, the goal of this project is to specifically address the climatological and operational aspects of the convective mode of southeast tornadic events and examine their relevance to tornado vulnerability, the proposed interdisciplinary study will specifically address the climatological and operational aspects of the convective mode of southeast tornadic events and examine their relevance to tornado vulnerability
Not Applicable (N/A)
University of Tennessee
The goal of this project is to specifically address the climatological and operational aspects of the convective mode of Southeast tornadic events and examine their relevance to tornado vulnerability.
2
Four-dimensional variability of Southeastern storm environments, with and without terrain
Parker
VORTEX
check_circle
2016
Level 3
The goal of this project was to understand differences between convective storms in the Southeastern US and the Great Plains, by using: (1) idealized simulations of convective storms in observed VORTEX-SE environments to understand how processes differ; and (2) case study simulations of observed VORTEX-SE cases to understand stability and shear within an NWP model, and understand the roles of terrain.
Matthew Parker
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Our long-range goal is to improve the understanding and forecasting of convective weather hazards in the SE. The goal of this application, which is the next step in the pursuit of the long-range goal, is to more fully exploit the data collected during VORTEX-SE cases in order to understand the influences of temporal and spatial variability of the mesoscale environment near tornadic and non-tornadic storms in the SE.
The research proposed in this application is expected to be significant, because it will address operationally important differences in variability and predictability between Plains and SE storm environments. It is expected that these results will provide forecasters with a clearer idea of the magnitudes and temporal-spatial scales of variability in SE severe weather environments, including their similarities to and differences from Plains tornado environments.
four dimensional variability of southeastern storm environments with and without terrain, the goal of this project was to understand differences between convective storms in the southeastern us and the great plains by using 1 idealized simulations of convective storms in observed vortex se environments to understand how processes differ and 2 case study simulations of observed vortex se cases to understand stability and shear within an nwp model and understand the roles of terrain, our long range goal is to improve the understanding and forecasting of convective weather hazards in the se the goal of this application which is the next step in the pursuit of the long range goal is to more fully exploit the data collected during vortex se cases in order to understand the influences of temporal and spatial variability of the mesoscale environment near tornadic and non tornadic storms in the se
Not Applicable (N/A)
North Carolina State University
The goal of this project was to understand differences between convective storms in the Southeastern US and the Great Plains, by using: (1) idealized simulations of convective storms in observed VORTEX-SE environments to understand how
7
Addressing Intercommunications between the Built and Natural Environments through Post-Event Damage Surveys
Lombardo
VORTEX
check_circle
2016
Level 3
The goal of this project was to collect post-tornado damage surveys to model damage fields to understand interconnections between built and natural environments and estimate damage severity from tornadoes. It builds off a similar project funded in FY15 that did not collect any new post-storm data.
Franklin Lombardo , Christopher Godfrey , Chris Peterson
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
The proposed research thus seeks to identify and explain the interconnectedness of damage in a given community, and how risk from tornadoes can be mitigated by improving our knowledge of these interconnections. We propose research
organized around two primary objectives, each subdivided into several tasks. The primary objectives are to 1) undertake a post-storm damage survey and 2) perform computer-based
modeling and simulation techniques using the post-storm survey data.
Findings from the proposed research can be incorporated in National Weather Service (NWS) operations and included in the dissemination of crucial information to emergency
managers and the public regarding tornado events.
addressing intercommunications between the built and natural environments through post event damage surveys, the goal of this project was to collect post tornado damage surveys to model damage fields to understand interconnections between built and natural environments and estimate damage severity from tornadoes it builds off a similar project funded in fy15 that did not collect any new post storm data, the proposed research thus seeks to identify and explain the interconnectedness of damage in a given community and how risk from tornadoes can be mitigated by improving our knowledge of these interconnections we propose research organized around two primary objectives each subdivided into several tasks the primary objectives are to 1 undertake a post storm damage survey and 2 perform computer based modeling and simulation techniques using the post storm survey data
Not Applicable (N/A)
University of Illinois Urbana-Champaign
The goal of this project was to collect post-tornado damage surveys to model damage fields to understand interconnections between built and natural environments and estimate damage severity from tornadoes. It builds off a similar project
2
VORTEX-SE: The Role and Predictability of Baroclinic and Terrain Influences in Southeastern U.S. Tornado Environments
Weiss
VORTEX
check_circle
2016
Level 3
The goal of this project was improved understanding of how tornadoes develop in the southeastern United States using direct observables and high-resolution numerical modeling.
Christopher Weiss
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, Verification & Validation
VORTEX FY16
The overarching goal of the VORTEX-SE project is to better understand the mechanisms controlling the genesis and maintenance of tornadoes in the southeastern United States.
The overarching outcome from this research will be the improved understanding of how tornadoes develop in the southeastern United States, an area where tornadoes occur less frequently than in the Plains, but also where there is a disproportionate number of casualties owing, in part, to gaps in our understanding of the processes germane to tornado development in this section of the country.
N/A
N/A
Texas Tech University
NA16OAR4590227
Severe Weather
Colorado, Texas
VORTEX
October 2016 - September 2019
Data Assimilation and Ensembles, Dynamics and Nesting, Verification & Validation
vortex se the role and predictability of baroclinic and terrain influences in southeastern u s tornado environments, the goal of this project was improved understanding of how tornadoes develop in the southeastern united states using direct observables and high resolution numerical modeling, the overarching goal of the vortex se project is to better understand the mechanisms controlling the genesis and maintenance of tornadoes in the southeastern united states
Not Applicable (N/A)
Texas Tech University
The goal of this project was improved understanding of how tornadoes develop in the southeastern United States using direct observables and high-resolution numerical modeling.
1
Improving Understanding and Prediction of Concurrent Tornadoes and Flash Floods With Numerical Models and VORTEX-SE Observations
Schumacher
VORTEX
check_circle
2016
Level 3
The goal of this project was to improve understanding of the environmental sensitivities, and the observations required to predict of concurrent, co-
located tornado and flash flood events to improve NWS forecasts and warnings of TORFF (and tornado-only) hazards.
Russ Schumacher
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Compound Hazards, Dynamics and Nesting, Verification & Validation
VORTEX FY16
The specific objectives of the proposed project are to collect and analyze field observations of boundary-layer winds, thermodynamics, and precipitation structures in both tornado-only and TORFF situations to reveal important physical processes and environmental sensitivities; use case-study and idealized numerical modeling experiments to quantify the influence of boundary-layer wind shear and thermodynamics on rainfall production in supercell storms and vortices embedded in convective lines; continue analysis of the NWS warning process during multi-hazard events by documenting the unique challenges in these events.
These objectives are all aimed at ultimately improving NWS forecasts and warnings of TORFF (and tornado-only) hazards. Understanding the environmental sensitivities, and the
observations required to predict them, can be directly translated into the forecast process, and improved knowledge of the unique challenges that forecasters face in multi-hazard situations can potentially be disseminated and implemented at forecast offices across the country.
N/A
N/A
Colorado State University
NA16OAR4590215
Severe Weather, Water Extremes
Colorado
VORTEX
October 2016 - September 2018
Compound Hazards, Dynamics and Nesting, Verification & Validation
improving understanding and prediction of concurrent tornadoes and flash floods with numerical models and vortex se observations, the goal of this project was to improve understanding of the environmental sensitivities and the observations required to predict of concurrent co located tornado and flash flood events to improve nws forecasts and warnings of torff and tornado only hazards, the specific objectives of the proposed project are to collect and analyze field observations of boundary layer winds thermodynamics and precipitation structures in both tornado only and torff situations to reveal important physical processes and environmental sensitivities use case study and idealized numerical modeling experiments to quantify the influence of boundary layer wind shear and thermodynamics on rainfall production in supercell storms and vortices embedded in convective lines continue analysis of the nws warning process during multi hazard events by documenting the unique challenges in these events
Not Applicable (N/A)
Colorado State University
The goal of this project was to improve understanding of the environmental sensitivities, and the observations required to predict of concurrent, co- located tornado and flash flood events to improve NWS forecasts and warnings of
7
A Numerical Modeling Approach to Understanding the Influence of Terrain, Land Surface, and Boundary Layer Heterogeneity on Tornadic Storm Development
Buban
VORTEX
check_circle
2016
Level 3
The goal of this study was to understand small-scale features, particularly those caused or modified by differences in land surface characteristics that influence convection initiation and severe thunderstorm evolution. This project collected data in 2017 and used collected data to initialize and evaluate numerical simulations.
Michael Buban
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Verification & Validation
VORTEX FY16
The goal of this study is to understand the small-scale features, particularly those caused or modified by differences in land surface characteristics that influence CI and severe thunderstorm evolution.
Of interest is to understand the effects of heterogeneities of surface characteristics in the distribution of updrafts and cumuli, particularly
in relation to the scales of the underlying heterogeneities. This understanding is necessary to
improve subgrid-scale convective parameterizations in larger scale weather prediction models.
N/A
N/A
Oak Ridge Associated Universities, Inc.
NA16OAR4590228
Severe Weather
Tennessee
VORTEX
October 2016 - September 2017
Verification & Validation
a numerical modeling approach to understanding the influence of terrain land surface and boundary layer heterogeneity on tornadic storm development, the goal of this study was to understand small scale features particularly those caused or modified by differences in land surface characteristics that influence convection initiation and severe thunderstorm evolution this project collected data in 2017 and used collected data to initialize and evaluate numerical simulations, the goal of this study is to understand the small scale features particularly those caused or modified by differences in land surface characteristics that influence ci and severe thunderstorm evolution
Not Applicable (N/A)
Oak Ridge Associated Universities, Inc.
The goal of this study was to understand small-scale features, particularly those caused or modified by differences in land surface characteristics that influence convection initiation and severe thunderstorm evolution. This project collected data in 2017
2
Analysis and modeling of topographic influences on the atmospheric layer: Potential impact on tornado evolution
Knupp
VORTEX
check_circle
2016
Level 3
This project investigated mesoscale variation of atmospheric boundary layer flows modified by topography. Data collected from northern Alabama will evaluate numerical simulations under a broad range of conditions (boundary layer wind speed, direction, vertical shear, stability) for experiments to (1) determine the relative accuracy of numerical simulations and (b) evaluate the relative contribution of assimilation of radar, profiler, sounding, and surface data.
Kevin Knupp
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Radar, Verification & Validation
VORTEX FY16
This project will investigate the mesoscale variation of atmospheric boundary layer (ABL) flows modified by topography, which are hypothesized to influence tornadogenesis and/or tornado evolution over northern Alabama.
A project overview and project results will be summarized on wall posters and electronic slide
shows in the Severe Weather Institute Radar and Lightning Laboratories (SWIRLL) building. A
University of Alabama at Huntsville (UAH)-hosted web page describing the project, initial results,
and a summary of reports will be established, and initial documentation will be developed in the
form of conference presentations and journal manuscripts describing the project and preliminary
results, the contents of which will be dependent on data acquired early in the project.
N/A
N/A
University of Alabama at Huntsville
NA16OAR4590216
Severe Weather
Alabama
VORTEX
October 2016 - September 2018
Data Assimilation and Ensembles, Radar, Verification & Validation
analysis and modeling of topographic influences on the atmospheric layer potential impact on tornado evolution, this project investigated mesoscale variation of atmospheric boundary layer flows modified by topography data collected from northern alabama will evaluate numerical simulations under a broad range of conditions boundary layer wind speed direction vertical shear stability for experiments to 1 determine the relative accuracy of numerical simulations and b evaluate the relative contribution of assimilation of radar profiler sounding and surface data, this project will investigate the mesoscale variation of atmospheric boundary layer abl flows modified by topography which are hypothesized to influence tornadogenesis and or tornado evolution over northern alabama
Not Applicable (N/A)
University of Alabama at Huntsville
This project investigated mesoscale variation of atmospheric boundary layer flows modified by topography. Data collected from northern Alabama will evaluate numerical simulations under a broad range of conditions (boundary layer wind speed, direction, vertical shear,
5
Understanding the Variability of Southeastern Severe Storm Environments using Mobile Soundings during VORTEX-SE
Brown
VORTEX
check_circle
2016
Level 3
The goal of this project was to improve tornado forecasts by: (1) collecting high resolution rainsonde data; (2) understanding the role of varying boundary layer flow & moisture in complex terrain and its effect on the tornadic environment, and (3) understanding surface heterogeneity and mesoscale boundaries on the SE severe storm environment.
Mike Brown, Ryan Wade, Todd Murphy
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Dynamics and Nesting, Verification & Validation
VORTEX FY16
We propose a physical science project where the goal is to address the gaps in the knowledge base through a complementary set of specific objectives. (i) We propose to coordinate and collect high temporal resolution mobile rawinsonde data from six sounding systems during the VORTEXSE field campaign. (ii) We propose to quantify the role varying boundary layer flow and moisture within complex terrain has on the tornadic environment. (iii) We propose to quantify the impacts of surface heterogeneity upon the mesoscale environments of storms in the SE.
The outcomes of this effort will include the delivery of an important dataset for all VORTEXSE PIs.
N/A
N/A
Mississippi State University
NA16OAR4590212
Severe Weather
Alabama, Louisiana, Mississippi
VORTEX
October 2016 - September 2018
Dynamics and Nesting, Verification & Validation
understanding the variability of southeastern severe storm environments using mobile soundings during vortex se, the goal of this project was to improve tornado forecasts by 1 collecting high resolution rainsonde data 2 understanding the role of varying boundary layer flow moisture in complex terrain and its effect on the tornadic environment and 3 understanding surface heterogeneity and mesoscale boundaries on the se severe storm environment, we propose a physical science project where the goal is to address the gaps in the knowledge base through a complementary set of specific objectives i we propose to coordinate and collect high temporal resolution mobile rawinsonde data from six sounding systems during the vortexse field campaign ii we propose to quantify the role varying boundary layer flow and moisture within complex terrain has on the tornadic environment iii we propose to quantify the impacts of surface heterogeneity upon the mesoscale environments of storms in the se
Not Applicable (N/A)
Mississippi State University
The goal of this project was to improve tornado forecasts by: (1) collecting high resolution rainsonde data; (2) understanding the role of varying boundary layer flow & moisture in complex terrain and its effect on
2
Improved Understanding of Tornado Development and Risk using Models and Observations from VORTEX-SE 2017
Dawson
VORTEX
check_circle
2016
Level 3
The goal of this project was to test parameters better suited to model cool-season tornado risk in the Southeast by: collecting special observations during intensive observing periods, applying state-of-art numerical weather prediction and data assimilation systems, and developing a statistical tornadogenesis risk model using historical data.
Daniel Dawson, Stephen Frasier
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Verification & Validation
VORTEX FY16
We propose to develop a statistical tornadogenesis risk model to evaluate the extent to which 1) canonical environmental parameters associated with tornado formation and 2) local terrain gradients can capture the space/time genesis distribution in this region, with special emphasis on non-supercell formation pathways.
This effort will lay the groundwork for testing novel parameters better suited to model cool-season tornado risk in the Southeast, a crucial step towards reducing impacts in this region.
N/A
N/A
Purdue University
NA16OAR4590208
Severe Weather
Indiana, Massachusetts
VORTEX
October 2016 - September 2018
Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Verification & Validation
improved understanding of tornado development and risk using models and observations from vortex se 2017, the goal of this project was to test parameters better suited to model cool season tornado risk in the southeast by collecting special observations during intensive observing periods applying state of art numerical weather prediction and data assimilation systems and developing a statistical tornadogenesis risk model using historical data, we propose to develop a statistical tornadogenesis risk model to evaluate the extent to which 1 canonical environmental parameters associated with tornado formation and 2 local terrain gradients can capture the space time genesis distribution in this region with special emphasis on non supercell formation pathways
Not Applicable (N/A)
Purdue University
The goal of this project was to test parameters better suited to model cool-season tornado risk in the Southeast by: collecting special observations during intensive observing periods, applying state-of-art numerical weather prediction and data assimilation
4
Elucidating Tornado Precursors in Quasi-Linear Convective Storms with Polarmetric Radar
Kumjian
VORTEX
check_circle
2016
Level 3
This project sought to develop a means to identify tornadic quasi-linear convective systems (QLCSs) using dual-polarization radar data from the WSR-88D radar network, by identifying repeatable signatures in tornadic QLCSs, quantifying the ZDR-KDP enhancement separation, and developing a polarimetric conceptual model of tornadic QLCSs
Matthew Kumjian
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
This proposal focuses on better understanding and detection of some of the physical processes leading to tornadogenesis in southeastern U.S. storms. In particular, we propose a one-year research program targeting an improved understanding of and better anticipation of tornadogenesis in non-supercellular
(e.g., quasi-linear) convective storms.
The new knowledge gained by the proposed research efforts ultimately will lead to improved tornado warnings not only in the Southeast, but across the entire United States.
elucidating tornado precursors in quasi linear convective storms with polarmetric radar, this project sought to develop a means to identify tornadic quasi linear convective systems qlcss using dual polarization radar data from the wsr 88d radar network by identifying repeatable signatures in tornadic qlcss quantifying the zdr kdp enhancement separation and developing a polarimetric conceptual model of tornadic qlcss, this proposal focuses on better understanding and detection of some of the physical processes leading to tornadogenesis in southeastern u s storms in particular we propose a one year research program targeting an improved understanding of and better anticipation of tornadogenesis in non supercellular e g quasi linear convective storms
Not Applicable (N/A)
Pennsylvania State University
This project sought to develop a means to identify tornadic quasi-linear convective systems (QLCSs) using dual-polarization radar data from the WSR-88D radar network, by identifying repeatable signatures in tornadic QLCSs, quantifying the ZDR-KDP enhancement separation,
2
VORTEX-SE: Characterization of environmental influences on downdraft processes occurring in potentially tornadic storms in the Southeast United States
Wurman
VORTEX
check_circle
2016
Level 3
This project had two research goals: 1) produce and evaluate dual-Doppler, in situ, and environmental analyses of downdrafts associated with potential tornadic storms via instruments in the VORTEX-SE 2017 field campaign (northern Alabama); and 2) investigate dual-
/multi-Doppler analysis techniques, focusing on lower boundary conditions in areas with complex terrain and land use similar to the southeastern U.S.
Josh Wurman
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Sep 30 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting, In-situ Observation Technologies and Sensors, Verification & Validation
VORTEX FY16
This research will characterize the kinematic and thermodynamic variability of the environment ahead of potentially tornadic storms and investigate how the interactions between the near-storm environment and storm outflow contribute to tornadogenesis and evolution over the complex terrain of the Southeast U.S.
Refine radar analysis techniques employed in such areas of complex
terrain and vegetation.
N/A
N/A
Center for Severe Weather Research
NA16OAR4590225
Severe Weather
Colorado
VORTEX
October 2016 - September 2018
Atmospheric Physics, Dynamics and Nesting, In-situ Observation Technologies and Sensors, Verification & Validation
vortex se characterization of environmental influences on downdraft processes occurring in potentially tornadic storms in the southeast united states, this project had two research goals 1 produce and evaluate dual doppler in situ and environmental analyses of downdrafts associated with potential tornadic storms via instruments in the vortex se 2017 field campaign northern alabama and 2 investigate dual multi doppler analysis techniques focusing on lower boundary conditions in areas with complex terrain and land use similar to the southeastern u s, this research will characterize the kinematic and thermodynamic variability of the environment ahead of potentially tornadic storms and investigate how the interactions between the near storm environment and storm outflow contribute to tornadogenesis and evolution over the complex terrain of the southeast u s
Not Applicable (N/A)
Center for Severe Weather Research
This project had two research goals: 1) produce and evaluate dual-Doppler, in situ, and environmental analyses of downdrafts associated with potential tornadic storms via instruments in the VORTEX-SE 2017 field campaign (northern Alabama); and 2)
0
Augmentation of VORTEX-SE Intensive Observations Period Measurements with Infrasound Observations to Detect and Track Tornadoes
Talmadge
VORTEX
check_circle
2016
Level 3
This project sought to deploy infrasound/low-frequency sensor arrays as part of the 2017 VORTEX-SE data collection to better understand the source and propagation of the infrasonic signal, its relevance to tracking and detection, and its possible relevance to tornado genesis.
Carrick Talmadge
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Dec 31 2017 03:00:00 GMT-0500 (Eastern Standard Time)
Verification & Validation
VORTEX FY16
This study proposes to deploy infrasound/low-frequency sensor arrays in support of the Intensive Observing Periods of
the 2017 VORTEX-SE data collection campaign to collect infrasound data from tornados. This data will be analyzed and correlated with data from other sensors to better understand the source and propagation of the infrasonic signal, its relevance to tracking and detection, and its possible relevance to tornadogenesis.
Improved propagation modeling
combined with higher resolution multiple array measurements is expected to allow us to more
accurately determine the source strength and location, including the path of observed individual tornados.
N/A
N/A
University of Mississippi
NA16OAR4590204
Severe Weather
Mississippi
VORTEX
October 2016 - December 2017
Verification & Validation
augmentation of vortex se intensive observations period measurements with infrasound observations to detect and track tornadoes, this project sought to deploy infrasound low frequency sensor arrays as part of the 2017 vortex se data collection to better understand the source and propagation of the infrasonic signal its relevance to tracking and detection and its possible relevance to tornado genesis, this study proposes to deploy infrasound low frequency sensor arrays in support of the intensive observing periods of the 2017 vortex se data collection campaign to collect infrasound data from tornados this data will be analyzed and correlated with data from other sensors to better understand the source and propagation of the infrasonic signal its relevance to tracking and detection and its possible relevance to tornadogenesis
Not Applicable (N/A)
University of Mississippi
This project sought to deploy infrasound/low-frequency sensor arrays as part of the 2017 VORTEX-SE data collection to better understand the source and propagation of the infrasonic signal, its relevance to tracking and detection, and its
0
Direct detection of tornadoes using Infrasound remote sensing: assessment of capabilities through comparison with dual polarization radar and other direct detection measurements
Rinehart
VORTEX
check_circle
2016
Level 3
The goal of this project was to validate previous research on the capability of infrasound (IS) to detect tornadoes, and assess the ability of networks of IS detector arrays to accurately map the time-dependent location (path) of tornadoes of varying intensity and size.
Hank Rinehart, Kevin Knupp
Complete
Sat Oct 01 2016 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Sep 30 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Radar, Verification & Validation
VORTEX FY16
The general objective of this project is to evaluate the capability of infrasound to detect tornadoes (validate and extend previous research), and assess the ability of networks of infrasound detector arrays to accurately map the time-dependent location (path) of tornadoes of varying intensity and size. The related research question is, "Does infrasound provide a viable and dependable method of direct tornado detection?"
The proposed research initiative focuses on field observations and data analysis to increase
understanding and characterization of infrasound emissions from tornadoes. The importance of timely damage surveys cannot be understated since the damage surveys will be used to determine the tornado formation and dissipation points and times, tornado intensity (EF scale), and damage path width.
The operational outputs of this initiative include the following:
Development of an initial test bed, anchored at Severe Weather Institute Radar and Lightning Laboratories (SWIRLL), to be used in subsequent years to
continue a more detailed evaluation of IS direct detection of tornadoes, and
A real-time display of infrasound emissions, from the permanent IS array at SWIRLL (at a
minimum) over the VORTEX-SE (VSE) operational domain.
The research team will also evaluate and provide a preliminary assessment of the following:
Efficiency of IS for detection of tornadoes, and accuracy of IS in tornado detection based
on data collected during the first year,
Effective range of IS detection and the dependence on tornado intensity,
Feasibility of IS to quantify tornado characteristics, such as EF-scale and width of damage
track, and
Efficacy of using IS detection capabilities, particularly when combined and validated with
dual-pol radar direct detection, to develop near real-time displays of tornado paths.
The project overview and results will be summarized on wall posters and electronic slide shows
in the SWIRLL building. A University of Alabama in Huntsville (UAH)-hosted web page describing the project, initial results, and a
summary of reports will be established, and initial documentation will be developed in the form
of conference presentation and journal manuscript describing the project and preliminary results,
the contents of which will be dependent on data acquired early in the project.
A description of the project and results derived from it will be documented on the SWIRLL
website that will be updated as new information becomes available. This project will also have a
permanent display (with project details and results) in the SWIRLL building that will be part of
routine SWIRLL tours to both K-12 and the general public groups (UAH has conducted about 45
SWIRLL tours since July 2015). We plan to report results at relevant conferences (e.g., AMS
Conference on Severe Local Storms, AMS Annual Meeting, Acoustical Society of America
[ASA]) and will submit manuscripts to AMS and ASA journals as publishable results are
produced. The project results will be used in a graduate class (Ground-Based Remote Sensing).
N/A
N/A
General Atomics
NA16OAR4590205
Severe Weather
Alabama
VORTEX
October 2016 - September 2017
Radar, Verification & Validation
direct detection of tornadoes using infrasound remote sensing assessment of capabilities through comparison with dual polarization radar and other direct detection measurements, the goal of this project was to validate previous research on the capability of infrasound is to detect tornadoes and assess the ability of networks of is detector arrays to accurately map the time dependent location path of tornadoes of varying intensity and size, the general objective of this project is to evaluate the capability of infrasound to detect tornadoes validate and extend previous research and assess the ability of networks of infrasound detector arrays to accurately map the time dependent location path of tornadoes of varying intensity and size the related research question is "does infrasound provide a viable and dependable method of direct tornado detection?"
Not Applicable (N/A)
General Atomics
The goal of this project was to validate previous research on the capability of infrasound (IS) to detect tornadoes, and assess the ability of networks of IS detector arrays to accurately map the time-dependent location
1
Evaluation and Optimization of Two New Scale-Aware PBL Schemes within WRF for the Prediction of Day- and Night-Time Storm Environment and Tornadic Storms during VORTEX-SE
Xue
VORTEX
check_circle
2017
Level 3
The goal of this project was to examine impacts of different planetary boundary layer (PBL) schemes, and tune the scale-aware PBL schemes for better tornadic storm simulation.
Mon Aug 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting
VORTEX FY17
"We propose to systematically evaluate the YSU and MYNN PBL schemes within the WRF model, including their scale-aware version, for the prediction of tornadic storm environment and the
tornadic storms themselves over the VORTEX-SE field campaign region. In particular, tornadic and near-tornadic rotating storm cases from the Intensive Operational Periods (IOPs) of the VORTEX-SE field
campaigns of early springs of 2016 and 2017 will be selected for the evaluation studies. Both day-time and night-time cases will be examined, where the performance of the PBL schemes for the unstable day-time
and stable night-time BL is important, respectively. Special attention will be paid to the evaluation of recently-developed enhancements to the YSU and MYNN PBL schemes that are designed to deal with the
'gray zone' issue of PBL schemes when they are applied to horizontal grid spacings ranging from a few hundreds of meters to a few kilometers. The performance of the original and the scale-aware version of the
two schemes will be evaluated in terms of their ability in correctly predicting the boundary layer structures that affect the storm environment, and in predicting tornadic storms. Additional experiments will be
performed with modified versions of the scale-dependent functions, and with the land cover replaced by uniform grass land or forest, in order to assess the effects of land cover inhomogeneity."
Results from the project can also provide guidance on the observing strategies to use in the major field campaign planned for ~2020, by providing information on regions of most sensitivity and/or uncertainty in the storm environment, and regions of most likely tornadic storms.
NA
NA
University of Oklahoma/Center for Analysis and Prediction of Storms
NA17OAR4590188
Severe Weather
Oklahoma
VORTEX
September 2017 - August 2020
Atmospheric Physics, Dynamics and Nesting
evaluation and optimization of two new scale aware pbl schemes within wrf for the prediction of day and night time storm environment and tornadic storms during vortex se, the goal of this project was to examine impacts of different planetary boundary layer pbl schemes and tune the scale aware pbl schemes for better tornadic storm simulation, "we propose to systematically evaluate the ysu and mynn pbl schemes within the wrf model including their scale aware version for the prediction of tornadic storm environment and the tornadic storms themselves over the vortex se field campaign region in particular tornadic and near tornadic rotating storm cases from the intensive operational periods iops of the vortex se field campaigns of early springs of 2016 and 2017 will be selected for the evaluation studies both day time and night time cases will be examined where the performance of the pbl schemes for the unstable day time and stable night time bl is important respectively special attention will be paid to the evaluation of recently developed enhancements to the ysu and mynn pbl schemes that are designed to deal with the 'gray zone' issue of pbl schemes when they are applied to horizontal grid spacings ranging from a few hundreds of meters to a few kilometers the performance of the original and the scale aware version of the two schemes will be evaluated in terms of their ability in correctly predicting the boundary layer structures that affect the storm environment and in predicting tornadic storms additional experiments will be performed with modified versions of the scale dependent functions and with the land cover replaced by uniform grass land or forest in order to assess the effects of land cover inhomogeneity "
Not Applicable (N/A)
University of Oklahoma/Center for Analysis and Prediction of Storms
The goal of this project was to examine impacts of different planetary boundary layer (PBL) schemes, and tune the scale-aware PBL schemes for better tornadic storm simulation.
6
An investigation of the effects of complex topography on storm environments, near-surface wind profiles in and near storms, and tornado vulnerability in the southeastern U.S., using existing data and observations from the VORTEX-Southeast field campaign
Markowski
VORTEX
check_circle
2017
Level 3
The goal of this project was to investigate (1) the effects of complex terrain on storm environments in the Southeast; (2) the characteristics of near-surface vertical wind profiles within and near convective storms sampled during VORTEX-Southeast; and (3) near-storm environments and societal vulnerability to tornadoes in the Southeast.
Mon Aug 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting, High-Performance Computing (HPC), Numerical Weather Prediction (NWP), Verification & Validation, Social Vulnerability
VORTEX FY17
This multi-pronged proposal seeks to advance our understanding of (i) the effects of complex terrain on storm environments, (ii) the characteristics of near-surface vertical wind profiles within
and near convective storms, and (iii) tornado vulnerability. Our efforts will exploit, in novel ways, operational numerical weather prediction model output from the high-resolution, rapid refresh (HRRR), vertical wind profiles obtained from Doppler lidars deployed during the VORTEX-SE intensive operations periods in 2016 and 2017, and an existing Storm Prediction Center database of severe weather events and their environments.
The findings obtained from the proposed research will help guide the future collection of targeted observations in VORTEX-SE, will improve our understanding of model errors in the prediction of near-surface horizontal vorticity (which has implications for Warn-on-Forecast), and better expose the meteorological factors that contribute to tornado fatalities and other societal impacts in the southeastern U.S.
NA
NA
Pennsylvania State University
NA17OAR4590189
Severe Weather
Pennsylvania
VORTEX
September 2017 - August 2020
Atmospheric Physics, Dynamics and Nesting, High-Performance Computing (HPC), Numerical Weather Prediction (NWP), Verification & Validation, Social Vulnerability
an investigation of the effects of complex topography on storm environments near surface wind profiles in and near storms and tornado vulnerability in the southeastern u s using existing data and observations from the vortex southeast field campaign, the goal of this project was to investigate 1 the effects of complex terrain on storm environments in the southeast 2 the characteristics of near surface vertical wind profiles within and near convective storms sampled during vortex southeast and 3 near storm environments and societal vulnerability to tornadoes in the southeast, this multi pronged proposal seeks to advance our understanding of i the effects of complex terrain on storm environments ii the characteristics of near surface vertical wind profiles within and near convective storms and iii tornado vulnerability our efforts will exploit in novel ways operational numerical weather prediction model output from the high resolution rapid refresh hrrr vertical wind profiles obtained from doppler lidars deployed during the vortex se intensive operations periods in 2016 and 2017 and an existing storm prediction center database of severe weather events and their environments
Not Applicable (N/A)
Pennsylvania State University
The goal of this project was to investigate (1) the effects of complex terrain on storm environments in the Southeast; (2) the characteristics of near-surface vertical wind profiles within and near convective storms sampled during
4
Understanding PBL Evolution and Surface-Driven Circulations with Simulations and VORTEX-SE Observations
Romine
VORTEX
check_circle
2017
Level 3
The goals of this project were to 1) investigate terrain impacts on the convective environment; 2) investigate interactions between terrain-induced circulations and severe convection; and 3) evaluate forecasts of rapid boundary layer evolution.
To address the needs of the VORTEX-SE 2017 program, the proposed work will: 1) investigate
NWP model forecast performance with PBL development in southeastern U.S. severe weather
events characterized by rapid increases in low-level shear and instability, and 2) examine,
through both real-data and idealized simulations, terrain-driven and inhomogeneous surface
circulations across the VORTEX-SE domain and their impact on moisture transport and
convection. Both conventional and VORTEX-SE observations will be used to validate the
forecasts. Further, we will 3) clarify instrumentation needs and define sampling strategies to
sample terrain-driven flows; and 4) present research results to the broader community through
presentations and formal publications.
The ultimate goal of the proposed work is to improve forecasts of tornado threat in regions with complex terrain, particularly within the southeastern U.S.
understanding pbl evolution and surface driven circulations with simulations and vortex se observations, the goals of this project were to 1 investigate terrain impacts on the convective environment 2 investigate interactions between terrain induced circulations and severe convection and 3 evaluate forecasts of rapid boundary layer evolution, to address the needs of the vortex se 2017 program the proposed work will 1 investigate nwp model forecast performance with pbl development in southeastern u s severe weather events characterized by rapid increases in low level shear and instability and 2 examine through both real data and idealized simulations terrain driven and inhomogeneous surface circulations across the vortex se domain and their impact on moisture transport and convection both conventional and vortex se observations will be used to validate the forecasts further we will 3 clarify instrumentation needs and define sampling strategies to sample terrain driven flows and 4 present research results to the broader community through presentations and formal publications
Not Applicable (N/A)
National Center for Atmospheric Research
The goals of this project were to 1) investigate terrain impacts on the convective environment; 2) investigate interactions between terrain-induced circulations and severe convection; and 3) evaluate forecasts of rapid boundary layer evolution.
1
Tornadoes and Mobile Homes: An Inter-science Approach to Reducing Vulnerabilities and Improving Capacities for the Southeast's Most Susceptible Population
Strader
VORTEX
check_circle
2017
Level 3
The goal of this project is to engage the entire Integrated Warning Team (IWT) to improve communication between IWT partners and mobile home residents, while increasing the ability of mobile home residents to prepare for, respond to, cope with, recover from, and adapt to tornado and other wind hazards.
Mon Aug 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Structural Engineering, Integrated Warning Team (IWT), Risk Communication, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY17
Interviews, Surveys, Spatial Analysis
Mixed Methods
Forecasters, Emergency Managers, Other Core Partners, Public
the investigation will: 1) determine the distribution of mobile homes and associated resident vulnerabilities across a full spatial spectrum-from Southeast regional to household levels-by leveraging millions of parcel data and attribute records through a geographic information science approach; 2) assess mobile home resident vulnerabilities and capacities by employing a mixed-methods approach-including researcherto-resident interviews building on previous VORTEX-SE studies, researcher-to-IWT partner interviews, and post-event tornado damage surveys-in areas that have the highest tornado mortality in the U.S.; and 3) identify and work to remove ommunication gaps that exist among mobile home residents and the IWT.
This proposed interdisciplinary work will address the overarching goal of VORTEX-SE by aiming to reduce damage, injuries, and the loss of life from tornadoes through synergistic research exploring the connections among information, vulnerabilities and capacities, and protective decision making among the most susceptible residential population of the Southeast. The research will directly engage with NWS personnel and emergency managers, providing them with insights about the populations they serve and identifying ways they can better meet their needs.
NA
NA
Villanova University
NA17OAR4590191
Severe Weather
Illinois, Oklahoma
VORTEX
September 2017 - August 2020
Structural Engineering, Integrated Warning Team (IWT), Risk Communication, Risk Perception & Response, "Social Science (SBES)", Interviews, Surveys, Spatial Analysis, Mixed Methods, Forecasters, Emergency Managers, Other Core Partners, Public
tornadoes and mobile homes an inter science approach to reducing vulnerabilities and improving capacities for the southeast's most susceptible population, the goal of this project is to engage the entire integrated warning team iwt to improve communication between iwt partners and mobile home residents while increasing the ability of mobile home residents to prepare for respond to cope with recover from and adapt to tornado and other wind hazards, the investigation will 1 determine the distribution of mobile homes and associated resident vulnerabilities across a full spatial spectrum-from southeast regional to household levels-by leveraging millions of parcel data and attribute records through a geographic information science approach 2 assess mobile home resident vulnerabilities and capacities by employing a mixed methods approach-including researcherto resident interviews building on previous vortex se studies researcher to iwt partner interviews and post event tornado damage surveys-in areas that have the highest tornado mortality in the u s and 3 identify and work to remove ommunication gaps that exist among mobile home residents and the iwt
Not Applicable (N/A)
Villanova University
The goal of this project is to engage the entire Integrated Warning Team (IWT) to improve communication between IWT partners and mobile home residents, while increasing the ability of mobile home residents to prepare for,
4
How Forecasters decide to warn: Insights on tornado risk communication from the Southeast U.S.
Liu
VORTEX
check_circle
2017
Level 3
The goal of this project is to understand how NWS forecasters make decisions, where they need more information, how they share information, and how they build relationships with local partners and community members in light of today's competitive media environment.
Our primary objective is to understand how NWS forecasters make decisions, where they need
more information, how they share information, and how they build relationships with local
partners and community members in light of today's competitive media environment. We
also are interested in differences between NWS forecasters in terms of their risk communication
decision-making processes.
Therefore the proposed project may be the first to examine NWS forecasters' decision-making processes, which in turn can improve tornado risk communication decision-making and practices.
NA
NA
University of Maryland/National Consortium for the Study of Terrorism and Responses to Terrorism (START)
how forecasters decide to warn insights on tornado risk communication from the southeast u s, the goal of this project is to understand how nws forecasters make decisions where they need more information how they share information and how they build relationships with local partners and community members in light of today's competitive media environment, our primary objective is to understand how nws forecasters make decisions where they need more information how they share information and how they build relationships with local partners and community members in light of today's competitive media environment we also are interested in differences between nws forecasters in terms of their risk communication decision making processes
Not Applicable (N/A)
University of Maryland/National Consortium for the Study of Terrorism and Responses to Terrorism (START)
The goal of this project is to understand how NWS forecasters make decisions, where they need more information, how they share information, and how they build relationships with local partners and community members in light
6
Understanding and Enhancing Public Interpretation and Use of Probabilistic Tornado Warnings in the Southeastern United States
Savelli
VORTEX
check_circle
2017
Level 3
The goal of this project is to evaluate how members of the public interpret and use different forms
of probabilistic tornado threat information, including uncertainty visualizations, with a
focus on the Southeastern United States.
Sonia Savelli, Julie Demuth
Complete
Sun Oct 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
Communicating Probabilities & Uncertainty, Decision-Making, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
VORTEX FY17
Interviews, Experiments
Mixed Methods
Public
This project will employ a combination of behavioral-experimental and semi-structured interview methods to evaluate how members of the public interpret and use different forms of probabilistic tornado threat information, including uncertainty visualizations, with a focus on the Southeastern United States. Specifically, we will investigate how selected communication formats, especially uncertainty visualizations, are understood and interpreted, influence risk perceptions, and enhance decision-making by members of the public.
Our findings will be used by the research team, in conjunction with NOAA collaborators, to develop recommendations for improving communication of probabilistic tornado forecast and warning information. In particular, the results of this work will have wide ranging implications for color-coded probabilistic tornado graphics proposed in the FACETs paradigm and uncertainty visualizations in general.
understanding and enhancing public interpretation and use of probabilistic tornado warnings in the southeastern united states, the goal of this project is to evaluate how members of the public interpret and use different forms of probabilistic tornado threat information including uncertainty visualizations with a focus on the southeastern united states, this project will employ a combination of behavioral experimental and semi structured interview methods to evaluate how members of the public interpret and use different forms of probabilistic tornado threat information including uncertainty visualizations with a focus on the southeastern united states specifically we will investigate how selected communication formats especially uncertainty visualizations are understood and interpreted influence risk perceptions and enhance decision making by members of the public
Not Applicable (N/A)
University of Washington
The goal of this project is to evaluate how members of the public interpret and use different forms
of probabilistic tornado threat information, including uncertainty visualizations, with a
focus on the Southeastern United States.
2
Improving accessibility and comprehension of tornado warnings in the Southeast for the Deaf, Blind, and Deaf-Blind
Sherman-Morris
VORTEX
check_circle
2017
Level 3
The goal of this project is to reduce the vulnerability of individuals who are Deaf, Hard of Hearing (HoH), Blind, Low Vision (LV), and Deaf-Blind to the tornado hazard by improving the warning communication process. More specifically, it investigates the communication of tornado warning information among these individuals and aims to improve our understanding of the sociological, linguistic, and cultural factors that influence how individuals from specific vulnerable groups receive, process, and respond to tornado information.
Mon Aug 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Language & Cultural Contexts, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY17
Interviews
Qualitative
Public, Vulnerable Populations
This project will investigate the communication of tornado warning information among
individuals who are Deaf, Hard of Hearing (HoH), Blind, Low Vision (LV), and Deaf-Blind. It
aims to improve our understanding of the sociological, linguistic, and cultural factors that
influence how individuals from specific vulnerable groups receive, process, and respond to
tornado information.
This knowledge will help meteorologists and emergency managers better communicate severe weather information to encourage a more effective response specific to the
Deaf/HoH and Blind populations, but more generally to the wider public. Our overall goal is to reduce the vulnerability of these individuals to tornadoes by improving the warning
communication process.
NA
NA
Mississippi State University/Northern Gulf Institute
NA17OAR4590198
Severe Weather
Alabama, Mississippi
VORTEX
September 2017 - August 2020
Language & Cultural Contexts, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Interviews, Qualitative, Public, Vulnerable Populations
improving accessibility and comprehension of tornado warnings in the southeast for the deaf blind and deaf blind, the goal of this project is to reduce the vulnerability of individuals who are deaf hard of hearing hoh blind low vision lv and deaf blind to the tornado hazard by improving the warning communication process more specifically it investigates the communication of tornado warning information among these individuals and aims to improve our understanding of the sociological linguistic and cultural factors that influence how individuals from specific vulnerable groups receive process and respond to tornado information, this project will investigate the communication of tornado warning information among individuals who are deaf hard of hearing hoh blind low vision lv and deaf blind it aims to improve our understanding of the sociological linguistic and cultural factors that influence how individuals from specific vulnerable groups receive process and respond to tornado information
Not Applicable (N/A)
Mississippi State University/Northern Gulf Institute
The goal of this project is to reduce the vulnerability of individuals who are Deaf, Hard of Hearing (HoH), Blind, Low Vision (LV), and Deaf-Blind to the tornado hazard by improving the warning communication process.
2
Collaborative Research: Using Vulnerability as Empirical Data to Improve Forecast and Warning Services
LaDue
VORTEX
check_circle
2017
Level 3
The goal of this project is to analyze and bring together knowledge generated by the PIs (as well as extant databases) on spatially-specific vulnerabilities across the Huntsville WFO CWA in order to develop a Brief Vulnerability Overview Tool (BVOT) for use by NWS operational forecasters to assess social and environmental vulnerabilities in their CWA.
The proposed research project (2017-2019) will analyze and bring together knowledge
generated by the PIs (as well as extant databases) on spatially-specific vulnerabilities across the
Huntsville WFO CWA in order to develop a Brief Vulnerability Overview Tool (BVOT) for use
by NWS operational forecasters to assess social and environmental vulnerabilities in their CWA.
Unlike similar tools (e.g., NWS's Impacts Catalogue), the BVOT is intended specifically for use
in the lead-up to and during warning operations. The BVOT will be a simple tool that can be
used by forecasters around potentially tornadic events, while also allowing for inputting changes
in the geography of vulnerabilities across the CWA.
This research project tested and refined methods for collecting discrete, spatially-specific vulnerability
information and mapping that information in a manner that would provide NWS WFO
meteorologists with the added spatial-situational awareness necessary to improve messaging to
core partners in an operationally-valuable manner. These methods evolved into the creation of
an experimental product - the Brief Vulnerability Overview Tool (BVOT) - that was provided
to the study NWS WFO with the goal of decreasing the forecast hedge-gap, providing more
information to core partners, and, ultimately, protecting lives and property.
collaborative research using vulnerability as empirical data to improve forecast and warning services, the goal of this project is to analyze and bring together knowledge generated by the pis as well as extant databases on spatially specific vulnerabilities across the huntsville wfo cwa in order to develop a brief vulnerability overview tool bvot for use by nws operational forecasters to assess social and environmental vulnerabilities in their cwa, the proposed research project 2017-2019 will analyze and bring together knowledge generated by the pis as well as extant databases on spatially specific vulnerabilities across the huntsville wfo cwa in order to develop a brief vulnerability overview tool bvot for use by nws operational forecasters to assess social and environmental vulnerabilities in their cwa unlike similar tools e g nws's impacts catalogue the bvot is intended specifically for use in the lead up to and during warning operations the bvot will be a simple tool that can be used by forecasters around potentially tornadic events while also allowing for inputting changes in the geography of vulnerabilities across the cwa
Not Applicable (N/A)
University of Oklahoma
The goal of this project is to analyze and bring together knowledge generated by the PIs (as well as extant databases) on spatially-specific vulnerabilities across the Huntsville WFO CWA in order to develop a Brief
1
Modulation of convective-draft characteristics and subsequent tornado intensity by the environmental wind and thermodynamics within the Southeast U.S.
Trapp
VORTEX
check_circle
2017
Level 3
The objective of this project was to investigate whether wide, intense tornadoes should form more readily out of wide, rotating updrafts by using numerical simulations of tornadic quasi-linear convective systems to determine the controls of tornado intensity.
Mon Aug 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting, Radar, Verification & Validation
VORTEX FY17
We will explore the links between convective-draft characteristics and tornado intensity, as modulated by the environmental wind and thermodynamics. Motivated by the observed correlation between tornado damage intensity and tornado track width, and supported by our emerging research, our specific hypothesis is that wide, intense tornadoes should form more readily out of wide, rotating updrafts. Analyses of a radar-based dataset and numerical modeling simulations will be used to test the applicability of this hypothesis to the mixture of tornado-bearing convective morphologies within southeastern U.S. environments. A new radar dataset that focuses on the pre-tornadic updraft width and mesocyclone diameter/intensity over a range of events will be generated. Relationships between these characteristics and tornado damage-path width and EF scale will then be tested.
The simulations and radar analyses will provide insight into sampling strategies that should be employed during field-data collection, and also will help direct development of new numerical weather prediction products. It is expected that the results of this research may have immediate operational use. Such knowledge transfer will be facilitated by close interactions with NWS personnel in the southeastern U.S. and elsewhere.
NA
NA
University of Illinois at Urbana-Champaign
NA17OAR4590195
Severe Weather
Illinois
VORTEX
September 2017 - August 2020
Atmospheric Physics, Dynamics and Nesting, Radar, Verification & Validation
modulation of convective draft characteristics and subsequent tornado intensity by the environmental wind and thermodynamics within the southeast u s, the objective of this project was to investigate whether wide intense tornadoes should form more readily out of wide rotating updrafts by using numerical simulations of tornadic quasi linear convective systems to determine the controls of tornado intensity, we will explore the links between convective draft characteristics and tornado intensity as modulated by the environmental wind and thermodynamics motivated by the observed correlation between tornado damage intensity and tornado track width and supported by our emerging research our specific hypothesis is that wide intense tornadoes should form more readily out of wide rotating updrafts analyses of a radar based dataset and numerical modeling simulations will be used to test the applicability of this hypothesis to the mixture of tornado bearing convective morphologies within southeastern u s environments a new radar dataset that focuses on the pre tornadic updraft width and mesocyclone diameter intensity over a range of events will be generated relationships between these characteristics and tornado damage path width and ef scale will then be tested
Not Applicable (N/A)
University of Illinois at Urbana-Champaign
The objective of this project was to investigate whether wide, intense tornadoes should form more readily out of wide, rotating updrafts by using numerical simulations of tornadic quasi-linear convective systems to determine the controls of
5
Improving radar wind retrievals and understanding local environmental influences on downdraft processes in potentially tornadic storms in the Southeast United States
Wurman
VORTEX
check_circle
2017
Level 3
The goal of this project is to better understand how interactions between the near-storm environment and storm outflow contribute to tornadogenesis and evolution over the complex terrain of the Southeast U.S., by refining 3D wind retrieval techniques that incorporate lower boundary conditions representative of complex terrain and integrating surface instrumentation to improve analysis accuracy.
Josh Wurman
Complete
Sun Oct 01 2017 03:00:00 GMT-0400 (Eastern Daylight Time)
This proposal from the Center for Severe Weather Research outlines a research plan to use core
radar, atmospheric profiling, and surface instruments fielded in the VORTEX-SE project to
answer questions relevant to core physical science program priorities, specifically regarding
the evolution of downdrafts and tornadoes in severe storms occurring in the Southeast U.S., and
to refine radar analysis techniques employed in such areas of complex terrain and vegetation.
Coupling new, adjustable multi-Doppler techniques with the high-resolution observations of the surrounding environment will provide an enhanced understanding of downdraft processes, and corresponding radar signatures, that influence tornadogenesis, improving operational weather forecasts and nowcasts of tornadogenesis in a variety of storm morphologies and regional environments.
improving radar wind retrievals and understanding local environmental influences on downdraft processes in potentially tornadic storms in the southeast united states, the goal of this project is to better understand how interactions between the near storm environment and storm outflow contribute to tornadogenesis and evolution over the complex terrain of the southeast u s by refining 3d wind retrieval techniques that incorporate lower boundary conditions representative of complex terrain and integrating surface instrumentation to improve analysis accuracy, this proposal from the center for severe weather research outlines a research plan to use core radar atmospheric profiling and surface instruments fielded in the vortex se project to answer questions relevant to core physical science program priorities specifically regarding the evolution of downdrafts and tornadoes in severe storms occurring in the southeast u s and to refine radar analysis techniques employed in such areas of complex terrain and vegetation
Not Applicable (N/A)
Center for Severe Weather Research
The goal of this project is to better understand how interactions between the near-storm environment and storm outflow contribute to tornadogenesis and evolution over the complex terrain of the Southeast U.S., by refining 3D wind
0
Exploration of Terrain Effects on Tornado and Supercell Dynamics in the Southeast United States
Bodine
VORTEX
check_circle
2017
Level 3
The goal of this project was to use tornado-scale and storm-scale modeling to simulate idealized and real cases using a Large-Eddy Simulation model to examine terrain effects on tornado potential and intensity.
Mon Aug 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Dynamics and Nesting, Verification & Validation
VORTEX FY17
The focus of the proposed work is to examine supercell-terrain and tornado-terrain
interactions through tornado-scale and storm-scale numerical simulations. The proposed work
will use high-resolution numerical simulations to assess how a wide range of idealized and
realistic topographic features impact supercell and tornado evolution, including tornadogenesis
potential.
The resulting analyses will enable a deeper understanding of the physical processes involved in supercell and tornado interactions with terrain, benefiting operational forecasters in
the Southeast U.S. The proposed parameter space will enable robust conclusions about the
extent to which terrain effects can be generalized.
NA
NA
University of Oklahoma
NA17OAR4590201
Severe Weather
Illinois, Oklahoma
VORTEX
September 2017 - August 2020
Atmospheric Physics, Dynamics and Nesting, Verification & Validation
exploration of terrain effects on tornado and supercell dynamics in the southeast united states, the goal of this project was to use tornado scale and storm scale modeling to simulate idealized and real cases using a large eddy simulation model to examine terrain effects on tornado potential and intensity, the focus of the proposed work is to examine supercell terrain and tornado terrain interactions through tornado scale and storm scale numerical simulations the proposed work will use high resolution numerical simulations to assess how a wide range of idealized and realistic topographic features impact supercell and tornado evolution including tornadogenesis potential
Not Applicable (N/A)
University of Oklahoma
The goal of this project was to use tornado-scale and storm-scale modeling to simulate idealized and real cases using a Large-Eddy Simulation model to examine terrain effects on tornado potential and intensity.
1
VORTEX-SE: Insights into the Structure and Predictability of Southeastern U.S. Tornadic Storms Afforded by Intensive Observation and High-Resolution Numerical Modeling
Weiss
VORTEX
check_circle
2017
Level 3
The overarching goal of this project is improved understanding tornado development in the southeastern United States using direct observables and high-resolution numerical modeling, specifically: cold pool characterization and predictability, the impact of StickNet data assimilation on HRRR Ensemble forecasts, the role of Sand Mountain in enhancing tornado potential, and lightning and vertical drafts.
Mon Aug 31 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Verification & Validation
VORTEX FY17
The overarching goal of this project is the improved understanding of how tornadoes develop in the southeastern United States using direct observables and high-resolution numerical modeling.
We anticipate a number of successful operational outcomes out of this research. Most directly, we will see the development of improved surface data assimilation-fed HRRRE products, which are currently available to forecasters and we anticipate will continue to be improved through the methods described in this proposal. We also aim to expand on the potential utility of surface thermodynamic and lightning data in the warning decision
environment by demonstrating associations with tornado occurrence.
NA
NA
Texas Tech University
NA17OAR4590206
Severe Weather
Colorado, Texas
VORTEX
September 2017 - August 2020
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting, Verification & Validation
vortex se insights into the structure and predictability of southeastern u s tornadic storms afforded by intensive observation and high resolution numerical modeling, the overarching goal of this project is improved understanding tornado development in the southeastern united states using direct observables and high resolution numerical modeling specifically cold pool characterization and predictability the impact of sticknet data assimilation on hrrr ensemble forecasts the role of sand mountain in enhancing tornado potential and lightning and vertical drafts, the overarching goal of this project is the improved understanding of how tornadoes develop in the southeastern united states using direct observables and high resolution numerical modeling
Not Applicable (N/A)
Texas Tech University
The overarching goal of this project is improved understanding tornado development in the southeastern United States using direct observables and high-resolution numerical modeling, specifically: cold pool characterization and predictability, the impact of StickNet data assimilation
2
Multi-disciplinary investigation of concurrent tornado and flash flood threats in landfalling tropical cyclones
Schumacher
VORTEX
check_circle
2018
Level 3
The goals of this project are to quantitatively analyze the processes supporting tornadoes at the same location as locally large rainfall rates, understand NWS forecast and warning challenges for multiple hazards during landfalling TCs, and understand public risk assessment and vulnerabilities for multi-hazard scenarios through the analysis of Twitter data.
Russ Schumacher, Jennifer Henderson
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Compound Hazards, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY18
Interviews, Social Media Analysis
Mixed Methods
Forecasters, Public
In this project, we will extend our previous multi-disciplinary research on this topic by achieving three specific objectives: (1) Quantitatively analyze the processes supporting tornadoes at the same location as locally large rainfall rates; (2) Understand NWS forecast and warning challenges for multiple hazards during landfalling TCs, in comparison with other weather situations; and (3) Understand public risk assessment and vulnerabilities for multi-hazard scenarios through the analysis of Twitter data.
One expected integrated outcome of the project is a fuller picture of the information flow and use during TORFF events (or potential TORFF events). This information can then complete the loop and inform the scientific community, including NWS forecasters, on how social media is used by the public to discuss, share, and act on risks. This project will also represent the continuation of ongoing collaborative research to address questions relevant to VORTEX-SE from multiple disciplinary methods and perspectives.
NA
NA
Colorado State University
NA18OAR4590308
Severe Weather, Water Extremes, Tropical Cyclones
Colorado, Texas
VORTEX
October 2018 - September 2021
Compound Hazards, "Social Science (SBES)", Interviews, Social Media Analysis, Mixed Methods, Forecasters, Public
multi disciplinary investigation of concurrent tornado and flash flood threats in landfalling tropical cyclones, the goals of this project are to quantitatively analyze the processes supporting tornadoes at the same location as locally large rainfall rates understand nws forecast and warning challenges for multiple hazards during landfalling tcs and understand public risk assessment and vulnerabilities for multi hazard scenarios through the analysis of twitter data, in this project we will extend our previous multi disciplinary research on this topic by achieving three specific objectives 1 quantitatively analyze the processes supporting tornadoes at the same location as locally large rainfall rates 2 understand nws forecast and warning challenges for multiple hazards during landfalling tcs in comparison with other weather situations and 3 understand public risk assessment and vulnerabilities for multi hazard scenarios through the analysis of twitter data
Not Applicable (N/A)
Colorado State University
The goals of this project are to quantitatively analyze the processes supporting tornadoes at the same location as locally large rainfall rates, understand NWS forecast and warning challenges for multiple hazards during landfalling TCs, and
4
Improving Forecaster and Partner Interpretation of Uncertainty and Confidence in Risk Information: Cool and Warm Season Tornado Threats in the Southeastern U.S
Henderson
VORTEX
check_circle
2018
Level 3
The goal of this project was to build knowledge about potential differences, gaps, and mismatches in communication of uncertainty and confidence that arise and propagate throughout the forecast system for a cool season and warm season tornado threat in the Southeastern U.S.
Jennifer Henderson, Julie Demuth
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon Feb 28 2022 03:00:00 GMT-0500 (Eastern Standard Time)
With the goal of helping improve understanding of forecasters' and their partners' interpretations and uses of different types of information, especially probabilistic information, in warning decisions, and to understand the warning process and information gaps that contribute to uncertainty, this project will examine at different spatial and organizational scales the valences and meanings of the concepts uncertainty and confidence that arise among multiple expert groups in the southeastern U.S. during both cool and warm tornado seasons.
"This project will develop new knowledge (a) about how meteorologists at national centers, WFOs,
and their core partners conceptualize uncertainty and confidence; and (b) how these concepts
emerge through various interactions and influence risk communication among these groups
leading up to and during tornado events."
improving forecaster and partner interpretation of uncertainty and confidence in risk information cool and warm season tornado threats in the southeastern u s, the goal of this project was to build knowledge about potential differences gaps and mismatches in communication of uncertainty and confidence that arise and propagate throughout the forecast system for a cool season and warm season tornado threat in the southeastern u s, with the goal of helping improve understanding of forecasters' and their partners' interpretations and uses of different types of information especially probabilistic information in warning decisions and to understand the warning process and information gaps that contribute to uncertainty this project will examine at different spatial and organizational scales the valences and meanings of the concepts uncertainty and confidence that arise among multiple expert groups in the southeastern u s during both cool and warm tornado seasons
Not Applicable (N/A)
CIRES/University of Colorado
The goal of this project was to build knowledge about potential differences, gaps, and mismatches in communication of uncertainty and confidence that arise and propagate throughout the forecast system for a cool season and warm
1
Collaborative Research: Human Behavior Before, During, and After Severe Weather Events: In Situ Observations in the U.S. Southeast
Friedman
VORTEX
check_circle
2018
Level 3
The goal of this project is to directly observe human behavior before, during, and after severe weather events in the U.S. southeast during the 2019-2020 severe weather season. This project also continued the assessment of and refinement of the work done on the Brief Vulnerability Overview Tool (BVOT).
Jack Friedman, Laura Myers
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
Forecasters, Emergency Managers, Other Core Partners, Public
This project will directly observe human behavior before, during, and after severe weather events in the U.S. southeast during the 2019-2020 severe weather season. Primarily, the unit of analysis will be the household. Sampling will involve recruiting 30 households in Tuscaloosa County in west-central Alabama. Households will be chosen based on two variables: vulnerability and participation in informed social networks.
This research will generate empirically-grounded, observationally-based profiles of real-world, real-time, in situ-gathered human behavior among the public in the southeast U.S. These data will be summarized into a number of "profiles" that will serve to ground future research on human behavior associated with severe weather events. In addition, these data will be provided to NWS forecasters, emergency managers, and other partners in the weather enterprise in order to serve as a foundation for reconsidering how they imagine the public's preparedness for, understanding of, and response to severe weather communication and the need to take action.
NA
NA
University of Oklahoma
NA18OAR4590316
Severe Weather
Alabama, Oklahoma
VORTEX
September 2018 - August 2021
In-situ Observation Technologies and Sensors, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Interviews, User-Centered Design, Ethnographic Observation, Mixed Methods, Forecasters, Emergency Managers, Other Core Partners, Public
collaborative research human behavior before during and after severe weather events in situ observations in the u s southeast, the goal of this project is to directly observe human behavior before during and after severe weather events in the u s southeast during the 2019 2020 severe weather season this project also continued the assessment of and refinement of the work done on the brief vulnerability overview tool bvot, this project will directly observe human behavior before during and after severe weather events in the u s southeast during the 2019 2020 severe weather season primarily the unit of analysis will be the household sampling will involve recruiting 30 households in tuscaloosa county in west central alabama households will be chosen based on two variables vulnerability and participation in informed social networks
Not Applicable (N/A)
University of Oklahoma
The goal of this project is to directly observe human behavior before, during, and after severe weather events in the U.S. southeast during the 2019-2020 severe weather season. This project also continued the assessment of
0
Using Machine Learning to Improve Warnings of Nonclassical Tornadic Storms
Markowski
VORTEX
check_circle
2018
Level 3
The goal of this project was to explore the feasibility of using machine-learning techniques to improve the tornado warnings in severe thunderstorms that lack the classic radar signatures routinely relied upon by forecasters for warning issuance.
Paul Markowski
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
"The proposed research will explore the feasibility of using machine-learning techniques to predict
tornadogenesis in storms that exhibit nonclassical radar signatures, such as storms organized as
quasilinear convective systems (QLCSs), convective clusters, or QLCSs with embedded
supercells."
"The work is a necessary first step toward a real-time operational prediction system that
eventually would rely on radar observations to elevate situational awareness and provide critical
guidance to forecasters faced with warn/no-warn decisions in challenging situations."
using machine learning to improve warnings of nonclassical tornadic storms, the goal of this project was to explore the feasibility of using machine learning techniques to improve the tornado warnings in severe thunderstorms that lack the classic radar signatures routinely relied upon by forecasters for warning issuance, "the proposed research will explore the feasibility of using machine learning techniques to predict tornadogenesis in storms that exhibit nonclassical radar signatures such as storms organized as quasilinear convective systems qlcss convective clusters or qlcss with embedded supercells "
Not Applicable (N/A)
Pennsylvania State University
The goal of this project was to explore the feasibility of using machine-learning techniques to improve the tornado warnings in severe thunderstorms that lack the classic radar signatures routinely relied upon by forecasters for warning
3
Understanding the influence of microphysical processes on the environment and behavior of southeastern-U.S. potentially tornadic storms
Dawson
VORTEX
check_circle
2018
Level 3
The goal of this project was to quantify the impact of microphysical processes on the evolution of non-classical tornadic storms in the SE-US. by evaluating retrospective storm-scale Ensemble Kalman Filter (EnKF) analysis and forecast experiments on select VORTEX-SE NCTS cases from the 2016 and 2017 field campaigns.
Daniel Dawson, Robin Tanamachi
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Radar
VORTEX FY18
"The primary objective of this project is to quantify the impact of microphysical processes on the evolution of non-classical tornadic storms (NCTS) in the SE-US. The primary means of accomplishing this objective is the production and evaluation of a series of retrospective storm-scale Ensemble Kalman Filter (EnKF) analysis and forecast experiments on select VORTEX-SE NCTS cases from the 2016 and 2017 field campaigns. Specific tasks in service of this objective include assessing 1) the performance of a triple-moment microphysics scheme through sensitivity experiments and comparison with polarimetric radar and surface disdrometer
observations, and 2) the impact on numerical model analyses and forecasts of assimilating
mobile radar and other special observations from the 2016-2017 campaigns.
"
The special observations will be used appropriately to constrain model physical parameterizations in some experiments, and in others, assimilated to improve the initial conditions of the model, increasing confidence in the portrayal of simulated storm evolution. These efforts will aid operational forecasting efforts by identifying model deficiencies, biases, and sensitivities to microphysical parameterizations, and quantify the impact on NWP analyses and forecasts of assimilating supplemental VORTEX-SE observations (particularly radar data).
NA
NA
Purdue University
NA18OAR4590313
Severe Weather
Indiana
VORTEX
September 2018 - August 2023
Atmospheric Physics, Data Assimilation and Ensembles, Radar
understanding the influence of microphysical processes on the environment and behavior of southeastern u s potentially tornadic storms, the goal of this project was to quantify the impact of microphysical processes on the evolution of non classical tornadic storms in the se us by evaluating retrospective storm scale ensemble kalman filter enkf analysis and forecast experiments on select vortex se ncts cases from the 2016 and 2017 field campaigns, "the primary objective of this project is to quantify the impact of microphysical processes on the evolution of non classical tornadic storms ncts in the se us the primary means of accomplishing this objective is the production and evaluation of a series of retrospective storm scale ensemble kalman filter enkf analysis and forecast experiments on select vortex se ncts cases from the 2016 and 2017 field campaigns specific tasks in service of this objective include assessing 1 the performance of a triple moment microphysics scheme through sensitivity experiments and comparison with polarimetric radar and surface disdrometer observations and 2 the impact on numerical model analyses and forecasts of assimilating mobile radar and other special observations from the 2016 2017 campaigns "
Not Applicable (N/A)
Purdue University
The goal of this project was to quantify the impact of microphysical processes on the evolution of non-classical tornadic storms in the SE-US. by evaluating retrospective storm-scale Ensemble Kalman Filter (EnKF) analysis and forecast experiments
3
VORTEX-SE Type 1: Characterization of storm structure and storm environments through the integration, improvement and analysis of multi-platform radar data
Wurman
VORTEX
check_circle
2018
Level 3
The goal of this project was to to assess mechanisms for
low-level vortex formation along the leading edge of quasi linear convective systems, focusing on horizontal shearing instability (HSI) and its role in vortexgenesis.
Josh Wurman
Complete
Mon Oct 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
"The proposed research will explore the quality of vertical wind shear retrievals using Velocity Azimuth Display (VAD) analyses and integrate them with other methods of vertical atmospheric
profiling to better understand the variability of the environment directly ahead of potentially tornadic storms occurring in the SE U.S."
Combining high-quality VAD retrievals with other profiling systems, shear and thermodynamics from nearby sondes, and multi-Doppler observations of QLCSs and lines of closely-spaced
independent convective cells will inform upon the scale and nature of mesoscale influences on the evolution of cold pool structure, downdraft processes, and corresponding radar signatures
associated with tornadogenesis and/or storm severity frequently observed in the SE U.S. Improved scientific understanding of these processes will help improve operational weather
forecasts and nowcasts of tornadogenesis and storm severity in a variety of storm morphologies and regional environments.
NA
NA
Center for Severe Weather Research
NA18OAR4590314
Severe Weather
Colorado
VORTEX
October 2018 - September 2021
Radar, Verification & Validation
vortex se type 1 characterization of storm structure and storm environments through the integration improvement and analysis of multi platform radar data, the goal of this project was to to assess mechanisms for low level vortex formation along the leading edge of quasi linear convective systems focusing on horizontal shearing instability hsi and its role in vortexgenesis, "the proposed research will explore the quality of vertical wind shear retrievals using velocity azimuth display vad analyses and integrate them with other methods of vertical atmospheric profiling to better understand the variability of the environment directly ahead of potentially tornadic storms occurring in the se u s "
Not Applicable (N/A)
Center for Severe Weather Research
The goal of this project was to to assess mechanisms for
low-level vortex formation along the leading edge of quasi linear convective systems, focusing on horizontal shearing instability (HSI) and its role in vortexgenesis.
0
Integrated Airborne and Ground-Based Analyses of Southeastern Tornadic Storms
Biggerstaff
VORTEX
check_circle
2018
Level 3
The goals of this project were to use data collected from spring 2018 to: (i) construct wind retrieval time series from ground-based and airborne radars for quasi-linear convective systems (QLCSs), and use wind retrievals to (ii) determine the role of vertical drafts, (iii) determine the role of enhanced rear-inflow, (iv) relate storm structure and evolution to environmental variability derived from Compact Ramen Lidar, radiosondes, and profilers, (v) relate tornadogenesis to surface roughness gradients, and (vi) evaluate the warning decision processes.
Michael Biggerstaff
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
We will analyze the tornadic QCLSs observed by airborne and ground-based radars in the southeastern US during spring 2018 to determine the role of downdraft on the formation of mesovortices, the role of rear-inflow enhancement in tornado production, the influence of surface roughness gradients on the timing of tornadogenesis, and the influence of thermodynamic heterogeneity on mesovortex evolution. These analyses will be related to tornado warnings (or lack of warnings) issued by the NWS offices to better understand what factors forecasters should consider in deciding to warn on observed QLCS mesovortices.
This project addresses three of the priority research areas in the VORTEX-SE FFO: (i) studies of the mesoscale environment related to tornado potential in the Southeast, (ii) storm processes affecting tornado potential, and (iii) research on operational NWS and partners forecast and warning decision-making process.
integrated airborne and ground based analyses of southeastern tornadic storms, the goals of this project were to use data collected from spring 2018 to i construct wind retrieval time series from ground based and airborne radars for quasi linear convective systems qlcss and use wind retrievals to ii determine the role of vertical drafts iii determine the role of enhanced rear inflow iv relate storm structure and evolution to environmental variability derived from compact ramen lidar radiosondes and profilers v relate tornadogenesis to surface roughness gradients and vi evaluate the warning decision processes, we will analyze the tornadic qclss observed by airborne and ground based radars in the southeastern us during spring 2018 to determine the role of downdraft on the formation of mesovortices the role of rear inflow enhancement in tornado production the influence of surface roughness gradients on the timing of tornadogenesis and the influence of thermodynamic heterogeneity on mesovortex evolution these analyses will be related to tornado warnings or lack of warnings issued by the nws offices to better understand what factors forecasters should consider in deciding to warn on observed qlcs mesovortices
Not Applicable (N/A)
University of Oklahoma
The goals of this project were to use data collected from spring 2018 to: (i) construct wind retrieval time series from ground-based and airborne radars for quasi-linear convective systems (QLCSs), and use wind retrievals to
1
VORTEX-SE: Establishing the Interdependence of Thermodynamic State, Lightning, and Low-Level Vorticity as a Foundation for Improved Forecaster Awareness of Southeast U.S. Storms
Weiss
VORTEX
check_circle
2018
Level 3
The goal of this project was to synthesize thermodynamic state and lightning coverage observations to improve understanding of how modulations in updraft and cold pool intensity affect the generation of low-level vorticity in southeast U.S. storms.
Christopher Weiss
Complete
Sat Sep 01 2018 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Aug 31 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting
VORTEX FY18
The proposed research builds upon the successful research outcomes from the first three years of the VORTEX-SE project to gain improved understanding of the dynamics and predictability of tornadic storms in the southeastern United States, and to realize these advances in an operational setting through the transfer of this understanding and research-grade observations in real time.
The PIs and the NWS forecast office in Huntsville will interact to integrate the outcomes into an improved conceptual model that informs operational expectations of the ties between mesoscale variability, cloud microphysics, lightning, and low-level vorticity, their improved real-time prediction and improved real-time simulations afforded through lightning data assimilation. To complement this transfer, we will also
establish a pathway for (expanded) StesoNet and GLM observations into the operations environment to improve mesoscale awareness.
NA
NA
Texas Tech University
NA18OAR4590318
Severe Weather
Colorado, Texas, Virginia
VORTEX
September 2018 - August 2021
Atmospheric Physics, Data Assimilation and Ensembles, Dynamics and Nesting
vortex se establishing the interdependence of thermodynamic state lightning and low level vorticity as a foundation for improved forecaster awareness of southeast u s storms, the goal of this project was to synthesize thermodynamic state and lightning coverage observations to improve understanding of how modulations in updraft and cold pool intensity affect the generation of low level vorticity in southeast u s storms, the proposed research builds upon the successful research outcomes from the first three years of the vortex se project to gain improved understanding of the dynamics and predictability of tornadic storms in the southeastern united states and to realize these advances in an operational setting through the transfer of this understanding and research grade observations in real time
Not Applicable (N/A)
Texas Tech University
The goal of this project was to synthesize thermodynamic state and lightning coverage observations to improve understanding of how modulations in updraft and cold pool intensity affect the generation of low-level vorticity in southeast U.S.
0
VORTEX-SE: Quantifying the Influence of Sea-Surface Temperature Uncertainty on Cool-Season Severe Weather Events
Evans
VORTEX
check_circle
2019
Level 3
The goal of this project was to to quantify the impact of surface ocean temperatures when simulating cold-season southeast United States severe weather events in high-shear/low-CAPE (HSLC) .
Clark Evans
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles
VORTEX FY19
The overarching goal of this project is to test the following hypothesis:
"Forecasts of cold-season southeast United States severe weather events in high-shear/low-CAPE (HSLC) environments are sensitive to the treatment of the underlying ocean surface, including uncertainty and limitations therein, given that the underlying water (Gulf of Mexico and Atlantic Ocean) is the primary control on the thermodynamic characteristics for the near-surface air parcels in cold-season HSLC severe weather environments."
A three-part methodology was proposed to accomplish this goal:
1. Numerical simulations for eight cold-season HSLC events identified by WFO Tallahassee colleagues to quantify forecast sensitivity to SST uncertainty (year one).
2. Ensemble numerical simulations for one of the eight cold-season HSLC events to quantify the respective contributions of SST uncertainty and initial atmospheric uncertainty to forecast variability (year two).
3. Forecast exercises using numerical simulation output to quantify the practical significance of the research findings to the operational forecast process (years one and two).
The proposed research will better quantify the nature of forecast uncertainty in the pre-thunderstorm, pre-tornadic hermodynamic environment for HSLC events, therein illuminating the types and locations of observations a future VORTEX-SE field campaign will need to obtain to constrain forecasts, improve predictive ability, and improve understanding.
NA
NA
University of Wisconsin-Madison
NA19OAR4590208
Severe Weather
Wisconsin
VORTEX
September 2019 - August 2022
Atmospheric Physics, Data Assimilation and Ensembles
vortex se quantifying the influence of sea surface temperature uncertainty on cool season severe weather events, the goal of this project was to to quantify the impact of surface ocean temperatures when simulating cold season southeast united states severe weather events in high shear low cape hslc, the overarching goal of this project is to test the following hypothesis "forecasts of cold season southeast united states severe weather events in high shear low cape hslc environments are sensitive to the treatment of the underlying ocean surface including uncertainty and limitations therein given that the underlying water gulf of mexico and atlantic ocean is the primary control on the thermodynamic characteristics for the near surface air parcels in cold season hslc severe weather environments " a three part methodology was proposed to accomplish this goal 1 numerical simulations for eight cold season hslc events identified by wfo tallahassee colleagues to quantify forecast sensitivity to sst uncertainty year one 2 ensemble numerical simulations for one of the eight cold season hslc events to quantify the respective contributions of sst uncertainty and initial atmospheric uncertainty to forecast variability year two 3 forecast exercises using numerical simulation output to quantify the practical significance of the research findings to the operational forecast process years one and two
Not Applicable (N/A)
University of Wisconsin-Madison
The goal of this project was to to quantify the impact of surface ocean temperatures when simulating cold-season southeast United States severe weather events in high-shear/low-CAPE (HSLC) .
1
VORTEX-SE: Characterization of microphysical processes in potentially tornadic Southeast U.S. storms via polarimetric radar - disdrometer - lightning synthesis
Tanamachi
VORTEX
check_circle
2019
Level 3
The goal of this project was to relate lightning activity to storm precipitation processes, vertical air motion, and cold pool characteristics, resulting in a new conceptual model of storm-scale cold pool processes and the likelihood of tornadogenesis.
Robin Tanamachi
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Data Assimilation and Ensembles, Radar
VORTEX FY19
The major goals of this project are to synthesize observations of lightning, cloud microphysics, thunderstorm environments and the
cold pools they produce, and to understand their interdependence. Uniquely, we will investigate whether lightning data are
informative about subsequent variations in cold pools. In parallel to analyses of observations collected in Northern Alabama in 2016
and 2017 in four cases, we will simulate those cases with a high-resoultion cloud model and a sophisticated microphysics and
electrification scheme to understand the physical links and sequence of events leading to modification of cold pools in a way that
favors tornadoes. We will develop a new conceptual model of factors affecting storm-scale cold pool processes and the likelihood of
tornadogenesis and disseminate it to forecasters and the wider research community.
We propose to generate new conceptual models of these linkages through intensive, EnKF-based numerical analysis of VORTEX-SE observations, thereby filling a gap in our confidence concerning impacts at the surface of microphysical changes aloft. These new conceptual models will be deployed operationally through updates to existing NWS and NESDIS training modules.
NA
NA
Purdue University
NA19OAR4590209
Severe Weather
Indiana, Texas
VORTEX
September 2019 - August 2022
Atmospheric Physics, Data Assimilation and Ensembles, Radar
vortex se characterization of microphysical processes in potentially tornadic southeast u s storms via polarimetric radar disdrometer lightning synthesis, the goal of this project was to relate lightning activity to storm precipitation processes vertical air motion and cold pool characteristics resulting in a new conceptual model of storm scale cold pool processes and the likelihood of tornadogenesis, the major goals of this project are to synthesize observations of lightning cloud microphysics thunderstorm environments and the cold pools they produce and to understand their interdependence uniquely we will investigate whether lightning data are informative about subsequent variations in cold pools in parallel to analyses of observations collected in northern alabama in 2016 and 2017 in four cases we will simulate those cases with a high resoultion cloud model and a sophisticated microphysics and electrification scheme to understand the physical links and sequence of events leading to modification of cold pools in a way that favors tornadoes we will develop a new conceptual model of factors affecting storm scale cold pool processes and the likelihood of tornadogenesis and disseminate it to forecasters and the wider research community
Not Applicable (N/A)
Purdue University
The goal of this project was to relate lightning activity to storm precipitation processes, vertical air motion, and cold pool characteristics, resulting in a new conceptual model of storm-scale cold pool processes and the likelihood
5
Eye-Tracking the Storm: Information Processing of Visual Risk Communication
Sutton
VORTEX
check_circle
2019
Level 3
The goal of this project is better understand the interpretations and intended uses of visual hazard and warning
information by members of the public for a tornado warning scenario.
Jeannette Sutton
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Jun 30 2020 03:00:00 GMT-0400 (Eastern Daylight Time)
Information Seeking & Processing, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
VORTEX FY19
Surveys, Think-Alouds, Experiments
Mixed Methods
Public
We will investigate how members of the public attend to and interpret warning messages that include visual imagery. We will conduct experiments
using a combination of eye-tracking and post-experiment survey to investigate how individuals attend to, perceive, and process visual risk information. We propose to 1) use eye-tracking technology to
measure visual attention allocation and search patterns of risk images (maps, legends, and written message content) during a fictitious tornado threat and 2) assess the relationship between participants' prior tornado experiences, image type, and tornado threat level on message perceptions and risk information seeking and processing outcomes.
We will develop recommendations for visual risk communication messages that can be implemented by Weather Forecast
Offices who communicate directly with the public at risk.
NA
NA
University of Kentucky
NA19OAR4590211
Severe Weather
Kentucky
VORTEX
September 2019 - June 2020
Information Seeking & Processing, Risk Perception & Response, "Social Science (SBES)", Visual Risk Communication, Surveys, Think-Alouds, Experiments, Mixed Methods, Public
eye tracking the storm information processing of visual risk communication, the goal of this project is better understand the interpretations and intended uses of visual hazard and warning information by members of the public for a tornado warning scenario, we will investigate how members of the public attend to and interpret warning messages that include visual imagery we will conduct experiments using a combination of eye tracking and post experiment survey to investigate how individuals attend to perceive and process visual risk information we propose to 1 use eye tracking technology to measure visual attention allocation and search patterns of risk images maps legends and written message content during a fictitious tornado threat and 2 assess the relationship between participants' prior tornado experiences image type and tornado threat level on message perceptions and risk information seeking and processing outcomes
Not Applicable (N/A)
University of Kentucky
The goal of this project is better understand the interpretations and intended uses of visual hazard and warning
information by members of the public for a tornado warning scenario.
2
Linking Survivor Stories to Forensic Engineering: An Inter-Science Approach to Reducing Tornado Vulnerability in Residential Shelters
LaDue
VORTEX
check_circle
2019
Level 3
The goal of this project is to integrate detailed accounts of tornado survivors' experiences with analysis of damage to the home they were in, contextualized within the broader tornado impacts and wind field, in order to reduce structural vulnerability and increase occupant survivability.
Daphne LaDue
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Structural Engineering, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY19
Interviews
Mixed Methods
Public
The major goal of this research project is to reduce structural vulnerability and increase occupant survivability. This broad goal is addressed with four specific objectives:
Establish an interdisciplinary protocol for coupling forensic engineering investigations with tornado survivor interviews.
Conduct 3-5 interdisciplinary post-tornado field deployments in each of the two years in strategic regions of the southeast United
States.
Analyze results of post-tornado field deployments within the context of the spatio-temporal wind field, with special emphasis on factors that reduce structural vulnerability and increase occupant survivability.
Translate protocols and findings to NOAA/NWS and the larger scientific communities, which will include enhanced messaging for taking shelter.
Further, this research will analyze post-tornado data to more fully characterise (a) how the risk from the tornado manifested in the particular setting(s) of tornadoes studied, (b) efficacy of each individual's sheltering decision and circumstance, (c) what influenced their particular sheltering decision, (d) residents' experiences with reconstruction and whether they sought to employ any damage mitigation techniques, and (e) what strategies survivors are using to cope post-tornado.
Ultimately, the proposed efforts will fulfill key priorities of the VORTEX-SE program, specifically to 1) reduce structural vulnerability in common residential structures in the Southeast including to the local environment and wind-driven debris, and 2) improve sheltering advice for residents in all types of structures.
NA
NA
University of Oklahoma
NA19OAR4590212
Severe Weather
Alabama, Illinois, Oklahoma
VORTEX
September 2019 - August 2023
Structural Engineering, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Interviews, Mixed Methods, Public
linking survivor stories to forensic engineering an inter science approach to reducing tornado vulnerability in residential shelters, the goal of this project is to integrate detailed accounts of tornado survivors' experiences with analysis of damage to the home they were in contextualized within the broader tornado impacts and wind field in order to reduce structural vulnerability and increase occupant survivability, the major goal of this research project is to reduce structural vulnerability and increase occupant survivability this broad goal is addressed with four specific objectives establish an interdisciplinary protocol for coupling forensic engineering investigations with tornado survivor interviews conduct 3-5 interdisciplinary post tornado field deployments in each of the two years in strategic regions of the southeast united states analyze results of post tornado field deployments within the context of the spatio temporal wind field with special emphasis on factors that reduce structural vulnerability and increase occupant survivability translate protocols and findings to noaa nws and the larger scientific communities which will include enhanced messaging for taking shelter further this research will analyze post tornado data to more fully characterise a how the risk from the tornado manifested in the particular setting s of tornadoes studied b efficacy of each individual's sheltering decision and circumstance c what influenced their particular sheltering decision d residents' experiences with reconstruction and whether they sought to employ any damage mitigation techniques and e what strategies survivors are using to cope post tornado
Not Applicable (N/A)
University of Oklahoma
The goal of this project is to integrate detailed accounts of tornado survivors' experiences with analysis of damage to the home they were in, contextualized within the broader tornado impacts and wind field, in order
1
Geospatial Threat Personalization and its Influence on Warning Risk Perception and Protective Actions
Sherman-Morris
VORTEX
check_circle
2019
Level 3
The goal of this project is to examine individuals' personalized risk area in a tornado warning compared to
their geographic awareness using a cognitive mapping strategy.
Kathleen Sherman-Morris
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Personalization of Weather Information, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
We propose to examine individuals' personalized risk area in a tornado warning compared to
their geographic awareness using a cognitive mapping strategy.
The proposed study has three broad research questions.
1. What are the geographic points of reference that help define one's personalized risk area?
2. Does this personalized risk area differ based on personal characteristics or characteristics
of the warnings?
3. Where a person's conceptualization of their personalized risk area is different from an
objective map view, does either have a dominant influence on risk perception?
Project findings could help make warnings more effective by helping meteorologists understand what reference points or locations are best to use. This is something the local office has control over and can be implemented immediately if desired. There is also potential to implement findings in future probabilistic warnings.
geospatial threat personalization and its influence on warning risk perception and protective actions, the goal of this project is to examine individuals' personalized risk area in a tornado warning compared to their geographic awareness using a cognitive mapping strategy, we propose to examine individuals' personalized risk area in a tornado warning compared to their geographic awareness using a cognitive mapping strategy the proposed study has three broad research questions 1 what are the geographic points of reference that help define one's personalized risk area? 2 does this personalized risk area differ based on personal characteristics or characteristics of the warnings? 3 where a person's conceptualization of their personalized risk area is different from an objective map view does either have a dominant influence on risk perception?
Not Applicable (N/A)
Mississippi State University
The goal of this project is to examine individuals' personalized risk area in a tornado warning compared to
their geographic awareness using a cognitive mapping strategy.
4
Environmental and Storm-Scale Characteristics of Tornado-Producing Rainbands within Landfalling Tropical Cyclones
Trier
VORTEX
check_circle
2019
Level 3
The goal of this project was to improve tornado forecasts from landfalling tropical cyclones in southeastern coastal regions by examining antecedent environments and their effect on storm structure, and the physical processes contributing to mesocyclone formation.
Stanley Trier
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, High-Performance Computing (HPC), Verification & Validation
VORTEX FY19
The proposed work would address the needs of the tropical-cyclone tornado component of the VORTEX-SE program by examining the antecedent environments supporting tornadoes in the
outer rainbands of landfalling tropical cyclones (LTC) and by analyzing novel simulations at convection-allowing and eddy-resolving scales.
There are several novel aspects of the proposed work. First, the compositing of different categorizations of TC tornado environments will supplement preexisting multi-decade statistical climatologies by providing a better understanding of mesoscale variability. Second, the case studies using the CAM ensemble will help link mesoscale variability in environmental conditions to the character (e.g., organization) of convection in LTC rainbands; we will also design and evaluate variables that are optimized to identify low-level rotation in tropical-cyclone environments. Third, the idealized simulations seek more fundamental understanding of the roles of a) the diurnal cycle, b) land-coastal differences, and c) coastal orientation, which would help forecasters with conceptual models of LTC supercell evolution, and could be helpful in the design of a future field program on LTC tornadoes.
NA
NA
National Center for Atmospheric Research
NA19OAR4590215
Severe Weather, Tropical Cyclones
Colorado, Mississippi, Oklahoma
VORTEX
September 2019 - August 2022
Data Assimilation and Ensembles, High-Performance Computing (HPC), Verification & Validation
environmental and storm scale characteristics of tornado producing rainbands within landfalling tropical cyclones, the goal of this project was to improve tornado forecasts from landfalling tropical cyclones in southeastern coastal regions by examining antecedent environments and their effect on storm structure and the physical processes contributing to mesocyclone formation, the proposed work would address the needs of the tropical cyclone tornado component of the vortex se program by examining the antecedent environments supporting tornadoes in the outer rainbands of landfalling tropical cyclones ltc and by analyzing novel simulations at convection allowing and eddy resolving scales
Not Applicable (N/A)
National Center for Atmospheric Research
The goal of this project was to improve tornado forecasts from landfalling tropical cyclones in southeastern coastal regions by examining antecedent environments and their effect on storm structure, and the physical processes contributing to mesocyclone
1
Evaluation of Structural Vulnerability in the Southeast United States Using High-Resolution Tornado Simulations with Buildings and Terrain
Bodine
VORTEX
check_circle
2019
Level 3
The goal of this project was to improve our understanding of how common residential structures are impacted by tornado winds and wind-driven debris.
David Bodine
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics
VORTEX FY19
The primary goal of this project is to improve our understanding of how common residential structures are impacted by the tornado's
winds and wind-driven debris. This addresses the VORTEX-SE priority of understanding and reducing societal vulnerability. To that
end, the proposed work uses an observational and numerical modeling based approach to examine how the wind and debris
distributions vary around common residential structures in the Southeast United States. This work includes generating a suite of
numerical simulations for different individual residence geometries, varied residential layouts (mock neighborhoods), and simulations
of structures at different stages of damage.
The resulting data will provide rich information to inform the structural engineering community about wind and pressure distributions around structures and what factors contribute to the damage process.
NA
NA
University of Oklahoma
NA19OAR4590216
Severe Weather
Illinois, Oklahoma
VORTEX
September 2019 - August 2022
Atmospheric Physics
evaluation of structural vulnerability in the southeast united states using high resolution tornado simulations with buildings and terrain, the goal of this project was to improve our understanding of how common residential structures are impacted by tornado winds and wind driven debris, the primary goal of this project is to improve our understanding of how common residential structures are impacted by the tornado's winds and wind driven debris this addresses the vortex se priority of understanding and reducing societal vulnerability to that end the proposed work uses an observational and numerical modeling based approach to examine how the wind and debris distributions vary around common residential structures in the southeast united states this work includes generating a suite of numerical simulations for different individual residence geometries varied residential layouts mock neighborhoods and simulations of structures at different stages of damage
Not Applicable (N/A)
University of Oklahoma
The goal of this project was to improve our understanding of how common residential structures are impacted by tornado winds and wind-driven debris.
1
Defining the capabilities of boundary layer profiling systems for operations in the southeastern United States
Smith
VORTEX
check_circle
2019
Level 3
The goal of this project was to examine mesoscale heterogeneity, rapid destabilization, and thunderstorm severity in Southeast severe weather events using ensemble sensitivity analysis.
Elizabeth Smith
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Feb 28 2023 03:00:00 GMT-0500 (Eastern Standard Time)
Atmospheric Physics, Data Assimilation and Ensembles
VORTEX FY19
This research aims to 1) increase understanding and utilization of boundary layer thermodynamic and kinematic profiling system observations for supporting National Weather Service (NWS)
operations and 2) use these data to understand the scales and character of mesoscale processes that locally enhance tornado potential in otherwise more benign-appearing environments in the
southeastern U.S. Using data collected from the multiple VORTEX-SE observing campaigns held in 2016-19, this project will explore the utility of boundary layer profiler retrievals of temperature,
humidity, and winds from several cases in multiple platform configurations.
Understanding the capability of boundary layer observing platforms to observe horizontal advection and transport can
provide new scientific insights into the scales and character of mesoscale processes in the southeastern U.S. and help inform the future operational use of such observing systems.
NA
NA
University of Oklahoma
NA19OAR4590218
Severe Weather
Colorado, Oklahoma
VORTEX
September 2019 - February 2023
Atmospheric Physics, Data Assimilation and Ensembles
defining the capabilities of boundary layer profiling systems for operations in the southeastern united states, the goal of this project was to examine mesoscale heterogeneity rapid destabilization and thunderstorm severity in southeast severe weather events using ensemble sensitivity analysis, this research aims to 1 increase understanding and utilization of boundary layer thermodynamic and kinematic profiling system observations for supporting national weather service nws operations and 2 use these data to understand the scales and character of mesoscale processes that locally enhance tornado potential in otherwise more benign appearing environments in the southeastern u s using data collected from the multiple vortex se observing campaigns held in 2016 19 this project will explore the utility of boundary layer profiler retrievals of temperature humidity and winds from several cases in multiple platform configurations
Not Applicable (N/A)
University of Oklahoma
The goal of this project was to examine mesoscale heterogeneity, rapid destabilization, and thunderstorm severity in Southeast severe weather events using ensemble sensitivity analysis.
0
Exploring the Influence of Surface Heterogeneities on Low-level Vertical Vorticity and Polarimetric Radar Signatures in Idealized Quasi-Linear Convective Systems
Lombardo
VORTEX
check_circle
2019
Level 3
The goal of this project was to better understand how structure, evolution, and low-level dual-polarization radar presentation of quasi-linear convective systems are affected by surface topographic and thermodynamic heterogeneities.
Kelly Lombardo
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Physics, Radar
VORTEX FY19
The main goal of the project is to characterize the influence of surface topographic heterogeneities on the evolution and structure of
simulated quasi-linear convective systems (QLCSs), with an emphasis on near-surface vertical vorticity and Okubo-Weiss trends and
simulated polarimetric radar signatures. Within this primary research goal, there are 2 objectives:
1. Characterize the influence of orographic heterogeneities on QLCS structure, evolution, and the development of vertical vorticity.
2. Characterize the evolution of simulated low-level dual-polarization radar fields associated with QLCS structural changes from
environmental heterogeneities.
The new knowledge gained by the proposed research ultimately will lead to improved tornado warnings not only in the Southeast, but across the entire United States.
NA
NA
Pennsylvania State University
NA19OAR4590222
Severe Weather
Pennsylvania
VORTEX
September 2019 - August 2023
Atmospheric Physics, Radar
exploring the influence of surface heterogeneities on low level vertical vorticity and polarimetric radar signatures in idealized quasi linear convective systems, the goal of this project was to better understand how structure evolution and low level dual polarization radar presentation of quasi linear convective systems are affected by surface topographic and thermodynamic heterogeneities, the main goal of the project is to characterize the influence of surface topographic heterogeneities on the evolution and structure of simulated quasi linear convective systems qlcss with an emphasis on near surface vertical vorticity and okubo weiss trends and simulated polarimetric radar signatures within this primary research goal there are 2 objectives 1 characterize the influence of orographic heterogeneities on qlcs structure evolution and the development of vertical vorticity 2 characterize the evolution of simulated low level dual polarization radar fields associated with qlcs structural changes from environmental heterogeneities
Not Applicable (N/A)
Pennsylvania State University
The goal of this project was to better understand how structure, evolution, and low-level dual-polarization radar presentation of quasi-linear convective systems are affected by surface topographic and thermodynamic heterogeneities.
5
Short-term Ensemble Prediction of Tornadoes in Landfalling Tropical Cyclones
Jones
VORTEX
check_circle
2019
Level 3
The goal of this project was to tune the Warn-on-Forecast System to forecast conditions associated with landfalling tropical cyclones through improvements in data assimilation, model configuration, and visualization tools, and compare to observations.
Thomas Jones
Complete
Sun Sep 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed Aug 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance, Verification & Validation, Visualizations
VORTEX FY19
Unified Forecast System (UFS), Warn on Forecast System (WoFS)
The principle deliverable of the proposed research is establishment of a landfalling tropical cyclone mode for production of real-time
WoFS guidance. Efforts to complete this tasking include testing various model configurations, data assimilation techniques with a
focus on GOES-R data, verification of WoFS with observations, and development of TC specific visualization tools for display to
forecasters. The overall goal being to create a skillful system to be used in realtime during landfalling TC events.
The proposed research will test the capabilities of NEWS-e to provide short-term, probabilistic guidance of tornado potential within TCs. Given the challenges associated with issuing accurate tornado warnings within the rapidly evolving and multi-hazard TC environment, it is expected that WoF guidance will benefit operational meteorologists in several ways:
1) Accurate, rapidly-updating, probabilistic tornado guidance within TCs offers the potential to improve NWS tornado warning operations and reduce the number of false alarms.
2) A WoF system will provide ensemble information on the near-storm environment within TCs, including locally favorable regions for tornado development (e.g. Knupp et al. 2006; Green et al. 2011), which can help forecasters rapidly prioritizing storms within a broad region of tornado potential.
3) Probabilistic guidance will be provided for multiple hazards, including damaging wind gusts and flash flooding. This multi-hazard guidance will assist forecasters in prioritizing and effectively communicating hazards posing the greatest risk to the public.
NA
NA
CIMMS/University of Oklahoma
NA19OAR4590223
Severe Weather, Tropical Cyclones
Colorado, Oklahoma
VORTEX
September 2019 - August 2022
Data Assimilation and Ensembles, Model & Forecast Guidance, Verification & Validation, Visualizations
short term ensemble prediction of tornadoes in landfalling tropical cyclones, the goal of this project was to tune the warn on forecast system to forecast conditions associated with landfalling tropical cyclones through improvements in data assimilation model configuration and visualization tools and compare to observations, the principle deliverable of the proposed research is establishment of a landfalling tropical cyclone mode for production of real time wofs guidance efforts to complete this tasking include testing various model configurations data assimilation techniques with a focus on goes r data verification of wofs with observations and development of tc specific visualization tools for display to forecasters the overall goal being to create a skillful system to be used in realtime during landfalling tc events
Not Applicable (N/A)
CIMMS/University of Oklahoma
The goal of this project was to tune the Warn-on-Forecast System to forecast conditions associated with landfalling tropical cyclones through improvements in data assimilation, model configuration, and visualization tools, and compare to observations.
1
Collaborative Research: Real-Time, In Situ Observations of Human Behavior and the Use of Probabilistically-Derived Guidance Among Publics in the U.S. Southeast
Friedman
VORTEX
check_circle
2020
Level 3
The goal of this project is to conduct pre-, during, and post-event interviews within hours of forecasted severe weather impacting the study region in order to understand real world behavior, perceptions, and awareness of tornado risk.
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
In-situ Observation Technologies and Sensors, Model & Forecast Guidance, Communicating Probabilities & Uncertainty, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY20
Interviews, Surveys, Focus Groups
Mixed Methods
Public
Assesses how regular members of the public interpret different types of probabilistic and deterministic guidance and products (graphical, text-based, and hybrid) in both experimental (non-storm) conditions and real-world severe potential storm conditions
Examines how regular people interpret experimental products in both experimental conditions and in real-time during actual severe wx conditions. This comparison will reveal how messaging may need to be revised for live versus pre-event situations.
NA
NA
University of Oklahoma
NA20OAR4590382
Severe Weather
Alabama, Oklahoma
VORTEX
September 2020 - August 2023
In-situ Observation Technologies and Sensors, Model & Forecast Guidance, Communicating Probabilities & Uncertainty, Risk Perception & Response, "Social Science (SBES)", Interviews, Surveys, Focus Groups, Mixed Methods, Public
collaborative research real time in situ observations of human behavior and the use of probabilistically derived guidance among publics in the u s southeast, the goal of this project is to conduct pre during and post event interviews within hours of forecasted severe weather impacting the study region in order to understand real world behavior perceptions and awareness of tornado risk, assesses how regular members of the public interpret different types of probabilistic and deterministic guidance and products graphical text based and hybrid in both experimental non storm conditions and real world severe potential storm conditions
Not Applicable (N/A)
University of Oklahoma
The goal of this project is to conduct pre-, during, and post-event interviews within hours of forecasted severe weather impacting the study region in order to understand real world behavior, perceptions, and awareness of tornado
0
Public Interpretation and Use of Evolving Probabilistic Tornado Forecasts in the Southeastern United States
Savelli
VORTEX
check_circle
2020
Level 3
The goal of this project are to understand how members of the public interpret and use evolving probabilistic tornado threat information, with a focus on the southeastern U.S. population.
This project will employ a combination of behavioral-experimental and semi- structured interview methods to understand interpretation and use of evolving probabilistic tornado forecast information by members of the public, with a focus on the southeastern U.S. population.
The findings will be used to develop actionable, evidence-based recommendations for evolving
probabilistic tornado forecast communication.
public interpretation and use of evolving probabilistic tornado forecasts in the southeastern united states, the goal of this project are to understand how members of the public interpret and use evolving probabilistic tornado threat information with a focus on the southeastern u s population, this project will employ a combination of behavioral experimental and semi structured interview methods to understand interpretation and use of evolving probabilistic tornado forecast information by members of the public with a focus on the southeastern u s population
Not Applicable (N/A)
University of Washington
The goal of this project are to understand how members of the public interpret and use evolving probabilistic tornado threat information, with a focus on the southeastern U.S. population.
0
Achieving Greater Tornado Resilience Through Informed Decision-Making About Reinforcing the Anchorage of Mobile and Manufactured Homes
Yan
VORTEX
check_circle
2020
Level 3
The goal of this project is to empower and effectively convey information to manufactured and mobile home (MMH) homeowners in an effort to facilitate informed decision-making regarding anchorage reinforcement of their MMHs.
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Structural Engineering, Community Preparedness & Resilience, Decision-Making, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY20
Surveys, Focus Groups
Mixed Methods
Public, Vulnerable Populations
This study will create compelling evidence for the value of proper anchoring systems and thus influence MMH owners' desires to reinforce their homes' anchoring systems. This multidisciplinary study would contribute to lowering the "enhanced threat of tornado mortality" in the Southeast region of the U.S.
The results will assist NOAA NWS Weather Forecast Offices, emergency management officials, and broadcast meteorologists in their Communications with their publics. Specifically, the knowledge gained from this research will help these entities design effective outreach materials for MMH residents, an especially vulnerable population throughout the Southeast US. Further, these findings will also reveal those MMH residents who are least likely or unable to anchor their homes, thus potentially identifying those most likely to benefit most from community tornado shelters. This also has consequent implications for determining adequate lead time messaging goals.
achieving greater tornado resilience through informed decision making about reinforcing the anchorage of mobile and manufactured homes, the goal of this project is to empower and effectively convey information to manufactured and mobile home mmh homeowners in an effort to facilitate informed decision making regarding anchorage reinforcement of their mmhs, this study will create compelling evidence for the value of proper anchoring systems and thus influence mmh owners' desires to reinforce their homes' anchoring systems this multidisciplinary study would contribute to lowering the "enhanced threat of tornado mortality" in the southeast region of the u s
Not Applicable (N/A)
Missouri University
The goal of this project is to empower and effectively convey information to manufactured and mobile home (MMH) homeowners in an effort to facilitate informed decision-making regarding anchorage reinforcement of their MMHs.
2
How Should Forecasters Warn about Tornadoes? Providing a Scientifically Validated Risk Communication Toolkit and Training to the National Weather Service
Seate
VORTEX
check_circle
2020
Level 3
The goal of this project is to provide a scientifically validated risk communication toolkit and training to the National Weather Service.
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Risk Communication, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY20
Experiments, Workshops
Mixed Methods
Forecasters, Broadcast Meteorologists
The project will experimentally assess a variety of risk communication strategies used by NWS forecasters during quiet weather and tornadic events. We will then examine how tailoring messages based on family relationships influences message effectiveness. We will also assess the extent to which risk communication messages are more efficacious when NWS forecasters and media partners send complementary messages.
Our primary objective is to provide a scientifically validated risk communication strategy toolkit and training to help create a Weather- Ready Nation.
how should forecasters warn about tornadoes? providing a scientifically validated risk communication toolkit and training to the national weather service, the goal of this project is to provide a scientifically validated risk communication toolkit and training to the national weather service, the project will experimentally assess a variety of risk communication strategies used by nws forecasters during quiet weather and tornadic events we will then examine how tailoring messages based on family relationships influences message effectiveness we will also assess the extent to which risk communication messages are more efficacious when nws forecasters and media partners send complementary messages
Not Applicable (N/A)
University of Maryland
The goal of this project is to provide a scientifically validated risk communication toolkit and training to the National Weather Service.
2
Toward an improved understanding of tornado formation within quasi-linear convective systems (QLCSs)
Trapp
VORTEX
check_circle
2020
Level 3
The goal of this project is to is to better understand how to skillful predict the genesis and intensity of Quasi-Linear Convective Storm tornadoes, by (1) analyzing mode of formation, season, time of day, environment, and damage-based intensity of tornadoes; and (2) quantify pre-tornadic radar-based characteristics.
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Radar
VORTEX FY20
This study will advance the understanding of tornadogenesis within QLCSs. The proposed research will be divided into four complementary research tasks: radar-based climatological analysis, assessment of the near-storm environment, quantifications of Doppler radar attributes , and analysis of illustrative case studies.
This project will advance the understanding of, and ability to predict, tornadogenesis within QLCSs.
NA
NA
University of Illinois Urbana-Champaign
NA20OAR4590384
Severe Weather
Illinois
VORTEX
September 2020 - August 2023
Radar
toward an improved understanding of tornado formation within quasi linear convective systems qlcss, the goal of this project is to is to better understand how to skillful predict the genesis and intensity of quasi linear convective storm tornadoes by 1 analyzing mode of formation season time of day environment and damage based intensity of tornadoes and 2 quantify pre tornadic radar based characteristics, this study will advance the understanding of tornadogenesis within qlcss the proposed research will be divided into four complementary research tasks radar based climatological analysis assessment of the near storm environment quantifications of doppler radar attributes and analysis of illustrative case studies
Not Applicable (N/A)
University of Illinois Urbana-Champaign
The goal of this project is to is to better understand how to skillful predict the genesis and intensity of Quasi-Linear Convective Storm tornadoes, by (1) analyzing mode of formation, season, time of day, environment,
2
VORTEX-SE: Assessment of the Role of Cold Pools in Low- Level Vorticity Production using Direct Observation and Ensemble Sensitivity Analysis
Weiss
VORTEX
check_circle
2020
Level 3
The goal of this project was to analyze observations from StickNet probes before and during cold pool passage, and to assess predictability of low-level rotation in southeast storms with ensemble sensitivity analysis.
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles
VORTEX FY20
The proposed project seeks to build upon successes of previous VORTEX-SE research to build a fundamental understanding of the character of thunderstorm cold pools in the southeastern United States, and how variations in this character control the development of near-ground rotation in these storms. In particular, we seek to confirm earlier findings that the rate of change of air density (largely, temperature) with these cold pools has association with tornado potential, and that the cold pool magnitude itself varies redictability according to the storm type.
The findings of this project should ultimately be realized in improved tornado warnings in the Southeast.
NA
NA
Texas Tech University
NA20OAR4590455
Severe Weather
Colorado, Texas
VORTEX
September 2020 - August 2023
Data Assimilation and Ensembles
vortex se assessment of the role of cold pools in low level vorticity production using direct observation and ensemble sensitivity analysis, the goal of this project was to analyze observations from sticknet probes before and during cold pool passage and to assess predictability of low level rotation in southeast storms with ensemble sensitivity analysis, the proposed project seeks to build upon successes of previous vortex se research to build a fundamental understanding of the character of thunderstorm cold pools in the southeastern united states and how variations in this character control the development of near ground rotation in these storms in particular we seek to confirm earlier findings that the rate of change of air density largely temperature with these cold pools has association with tornado potential and that the cold pool magnitude itself varies redictability according to the storm type
Not Applicable (N/A)
Texas Tech University
The goal of this project was to analyze observations from StickNet probes before and during cold pool passage, and to assess predictability of low-level rotation in southeast storms with ensemble sensitivity analysis.
2
Collaborative Research: Ensemble Sensitivity Analysis to Investigate Mesoscale Heterogeneity in Southeast US Tornado Events
Limpert
VORTEX
check_circle
2020
Level 3
The goal of this project was to examine links between mesoscale heterogeneity, rapid destabilization, and thunderstorm severity in Southeast severe weather events using ensemble sensitivity analysis (ESA), and also to determine forecast skill of ensemble means based on ESA and observations.
Thu Aug 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Forecast Skill Metrics
VORTEX FY20
Unified Forecast System (UFS)
This project intends to demonstrate and evaluate ensemble weighting on up to 10 VORTEX-SE events to assess their value to forecasts on watch and warning time scales. In particular, the goal is to test whether ensemble weighting is skillful in identifying which ensemble members will best represent the mesoscale heterogeneity, storm environments, and ultimately thunderstorm evolution.
An anticipated outcome of this work is better understanding of how mesoscale heterogeneities are linked with rapid destabilization and corresponding higher risk of severe and tornadic thunderstorms in the Southeast U.S. If successful, we hope to test the ensemble weighting system in the NOAA Hazardous Weather Testbed shortly after the conclusion of this work with the goal that it will be used in local NWS offices and by the Storm Prediction Center to improve severe weather forecasts at 0-6-hour lead times.
NA
NA
University of Nebraska-Lincoln
NA20OAR4590456
Severe Weather
Nebraska, Oklahoma
VORTEX
September 2020 - August 2023
Data Assimilation and Ensembles, Forecast Skill Metrics
collaborative research ensemble sensitivity analysis to investigate mesoscale heterogeneity in southeast us tornado events, the goal of this project was to examine links between mesoscale heterogeneity rapid destabilization and thunderstorm severity in southeast severe weather events using ensemble sensitivity analysis esa and also to determine forecast skill of ensemble means based on esa and observations, this project intends to demonstrate and evaluate ensemble weighting on up to 10 vortex se events to assess their value to forecasts on watch and warning time scales in particular the goal is to test whether ensemble weighting is skillful in identifying which ensemble members will best represent the mesoscale heterogeneity storm environments and ultimately thunderstorm evolution
Not Applicable (N/A)
University of Nebraska-Lincoln
The goal of this project was to examine links between mesoscale heterogeneity, rapid destabilization, and thunderstorm severity in Southeast severe weather events using ensemble sensitivity analysis (ESA), and also to determine forecast skill of ensemble
0
Environmental and Storm-generated Controls in Modulating Quasi-linear Convective System Vertical Vorticity: Dynamics and Detection
Weiss
VORTEX
search_activity
2021
Level 3
The goal of this project is to better understand quasi-linear
convective systems (QLCSs), and determining whether existing observational resources (data from disdrometers, radar, and lightning mapping arrays) can serve as proxies for cold pools.
Mon Aug 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
VORTEX FY21
The first component of our investigation will address our scientific understanding of processes influencing QLCS tornadoes, which, to this point, is based principally on idealized modeling studies that have identified relevant controls in both the ambient environment (e.g., low-level vertical wind shear, heterogeneities in buoyancy) and within the downdraft-driven cold pool structure (e.g., magnitude and position of downdrafts, associated buoyancy deficits and gradients). Observations are needed to corroborate these findings and identify limitations in existing operational forecasting tools (e.g., real-time analyses). Appreciating how the predictive skill for QLCS tornadoes is intimately tied to cold pool properties, our second focus for this work is trained on how existing observational resources, both operational and research grade, can effectively serve as proxies for cold pools. We utilize data from three research platforms (disdrometers, radar, and lightning mapping arrays) to connect fine-scale indications for QLCS tornadogenesis to operational observables.
We expect to significantly advance our conceptualization of vertical vorticity modulation within QLCSs. Further, via the inclusion of disdrometer and lightning data, we will develop the ability to detect and anticipate the processes central to this conceptualization, and detail the extent to which we can observe key processes in operationally-available datasets, as is necessary for a Weather Ready Nation.
NA
NA
Texas Tech University
NA21OAR4590151
Severe Weather
Texas
VORTEX
September 2021 - August 2026
environmental and storm generated controls in modulating quasi linear convective system vertical vorticity dynamics and detection, the goal of this project is to better understand quasi linear convective systems qlcss and determining whether existing observational resources data from disdrometers radar and lightning mapping arrays can serve as proxies for cold pools, the first component of our investigation will address our scientific understanding of processes influencing qlcs tornadoes which to this point is based principally on idealized modeling studies that have identified relevant controls in both the ambient environment e g low level vertical wind shear heterogeneities in buoyancy and within the downdraft driven cold pool structure e g magnitude and position of downdrafts associated buoyancy deficits and gradients observations are needed to corroborate these findings and identify limitations in existing operational forecasting tools e g real time analyses appreciating how the predictive skill for qlcs tornadoes is intimately tied to cold pool properties our second focus for this work is trained on how existing observational resources both operational and research grade can effectively serve as proxies for cold pools we utilize data from three research platforms disdrometers radar and lightning mapping arrays to connect fine scale indications for qlcs tornadogenesis to operational observables
Not Applicable (N/A)
Texas Tech University
The goal of this project is to better understand quasi-linear
convective systems (QLCSs), and determining whether existing observational resources (data from disdrometers, radar, and lightning mapping arrays) can serve as proxies for cold pools.
5
Observational Investigations of the Antecedent BL Characteristics/Variability and Related Internal Structure of Severe QLCSs
Knupp
VORTEX
search_activity
2021
Level 3
The goal of this project is to collect data associated with PERiLS field campaign for two years to improve improve understanding of the spatiotemporal variability of the boundary layer and lower free atmosphere in the near-storm environment associated with tornadic vs. nontornadic storms.
Kevin Knupp
Active
Fri Oct 01 2021 03:00:00 GMT-0400 (Eastern Daylight Time)
The long-term goal of this project is to further improve the understanding and forecasting of tornadoes and other convective hazards in the SE. The short-term goal is to improve understanding of the spatiotemporal variability of the boundary layer and lower free atmosphere in the near-storm environment associated with tornadic vs. nontornadic storms in the SE.
The outcomes of the proposed project will include experimental results along with conceptual
models that will improve and convey our understanding of the variability within tornadic
environments. This outcome is critical to improve tornado warning performance and thus
advancing the NWS mission of protecting life and property.
NA
NA
University of Alabama- Huntsville
NA21OAR4590323
Severe Weather
Alabama, Louisiana
VORTEX
October 2021 - September 2025
Verification & Validation
observational investigations of the antecedent bl characteristics variability and related internal structure of severe qlcss, the goal of this project is to collect data associated with perils field campaign for two years to improve improve understanding of the spatiotemporal variability of the boundary layer and lower free atmosphere in the near storm environment associated with tornadic vs nontornadic storms, the long term goal of this project is to further improve the understanding and forecasting of tornadoes and other convective hazards in the se the short term goal is to improve understanding of the spatiotemporal variability of the boundary layer and lower free atmosphere in the near storm environment associated with tornadic vs nontornadic storms in the se
Not Applicable (N/A)
University of Alabama- Huntsville
The goal of this project is to collect data associated with PERiLS field campaign for two years to improve improve understanding of the spatiotemporal variability of the boundary layer and lower free atmosphere in the
1
Faster, Clearer, Stronger Communication and Action: Building IWT and Vulnerable Resident Connections to Improve Severe Weather Literacy and Outcomes
Strader
VORTEX
search_activity
2021
Level 3
The goal of this project is to unite the efforts of the human data and narrative collections to present a unified, actionable plan aimed at strengthening the publics of affected communities in severe weather literacy and outcomes. Specifically, this project aims to improve the research and operational community's understanding of the complex interactions between tornado risk and societal vulnerability in the Southeast.
Stephen Strader, Jennifer Henderson, Alex M. Haberlie
Mon Aug 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Integrated Warning Team (IWT), Risk Communication, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY21
Interviews, Surveys, Experiments
Mixed Methods
Emergency Managers, Other Core Partners, Public
Objective A - Creating a novel geographic information system (GIS) and machine learning application that identifies the housing locations of the Southeast's most vulnerable and fatality-prone populace (mobile-manufactured home (MMH) residents) from aerial imagery, promoting the first census of this particularly vulnerable housing stock; Objective B1 - Spatiotemporally investigating sub-regional differences or variability in storm mode, tornado risk, MMH-tornado exposure, and societal vulnerability at fine spatial scale across the entire Southeast; Objective B2 - Examining current EM severe/tornado weather terminology comprehension and threat perceptions based on storm mode and time of day (daytime vs. nighttime). Objective C - Analyzing differences in EM communication, response, and mitigation strategies given the diverse populations they serve. Objective D -Assessing IWT-to-vulnerable resident tornado sheltering options, warning and communication needs, resilience-building strategies, and tornado survivability.
The research will employ an inter-science, mixed-methods approach aimed at reducing tornado impacts on particularly vulnerable Southeast residents, enhancing tornado survivability, and developing crucial end-to-end solutions that bridge the gap between researchers, decision makers, integrated warning team (IWT) members, and the publics they serve.
NA
NA
Villanova University
NA21OAR4590265
Severe Weather
Illinois, Pennsylvania, Texas
VORTEX
September 2021 - August 2026
Artificial Intelligence (AI) / Machine Learning (ML), Integrated Warning Team (IWT), Risk Communication, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Interviews, Surveys, Experiments, Mixed Methods, Emergency Managers, Other Core Partners, Public
faster clearer stronger communication and action building iwt and vulnerable resident connections to improve severe weather literacy and outcomes, the goal of this project is to unite the efforts of the human data and narrative collections to present a unified actionable plan aimed at strengthening the publics of affected communities in severe weather literacy and outcomes specifically this project aims to improve the research and operational community's understanding of the complex interactions between tornado risk and societal vulnerability in the southeast, objective a creating a novel geographic information system gis and machine learning application that identifies the housing locations of the southeast's most vulnerable and fatality prone populace mobile manufactured home mmh residents from aerial imagery promoting the first census of this particularly vulnerable housing stock objective b1 spatiotemporally investigating sub regional differences or variability in storm mode tornado risk mmh tornado exposure and societal vulnerability at fine spatial scale across the entire southeast objective b2 examining current em severe tornado weather terminology comprehension and threat perceptions based on storm mode and time of day daytime vs nighttime objective c analyzing differences in em communication response and mitigation strategies given the diverse populations they serve objective d assessing iwt to vulnerable resident tornado sheltering options warning and communication needs resilience building strategies and tornado survivability
Not Applicable (N/A)
Villanova University
The goal of this project is to unite the efforts of the human data and narrative collections to present a unified, actionable plan aimed at strengthening the publics of affected communities in severe weather literacy
2
Eye-Tracking the Storm: Information Processing of Visual Risk Communication
Sutton
VORTEX
check_circle
2021
Level 3
The goal of this project is to investigate the visual attention to a series of tweets that depict an emerging tornado threat to identify areas of visual interest and the properties of those visual cues that elicit attention.
Jeannette Sutton
Complete
Mon Mar 01 2021 03:00:00 GMT-0500 (Eastern Standard Time)
Thu Feb 29 2024 03:00:00 GMT-0500 (Eastern Standard Time)
Information Seeking & Processing, Risk Perception & Response, "Social, Behavioral, and Economic Sciences (SBES)", Visual Risk Communication
VORTEX FY21
Surveys, Focus Groups, Experiments
Mixed Methods
Public
We will investigate how members of the public attend to and interpret warning messages that include visual imagery. We will conduct experiments
using a combination of eye-tracking and post-experiment survey to investigate how individuals attend to, perceive, and process visual risk information. We propose to 1) use eye-tracking technology to
measure visual attention allocation and search patterns of risk images (maps, legends, and written message content) during a fictitious tornado threat and 2) assess the relationship between participants' prior tornado experiences, image type, and tornado threat level on message perceptions and risk information seeking and processing outcomes.
We will develop recommendations for visual risk communication messages that can be implemented by Weather Forecast
Offices who communicate directly with the public at risk.
NA
NA
State University of New York , University at Albany
eye tracking the storm information processing of visual risk communication, the goal of this project is to investigate the visual attention to a series of tweets that depict an emerging tornado threat to identify areas of visual interest and the properties of those visual cues that elicit attention, we will investigate how members of the public attend to and interpret warning messages that include visual imagery we will conduct experiments using a combination of eye tracking and post experiment survey to investigate how individuals attend to perceive and process visual risk information we propose to 1 use eye tracking technology to measure visual attention allocation and search patterns of risk images maps legends and written message content during a fictitious tornado threat and 2 assess the relationship between participants' prior tornado experiences image type and tornado threat level on message perceptions and risk information seeking and processing outcomes
Not Applicable (N/A)
State University of New York , University at Albany
The goal of this project is to investigate the visual attention to a series of tweets that depict an emerging tornado threat to identify areas of visual interest and the properties of those visual cues
6
Improved Nowcasting of QLCS Tornadoes in the United States
Murphy
VORTEX
search_activity
2022
Level 3
The goals of this project are to evaluate the 'three-ingredients method' (3IM) for nowcasting quasi-linear convective storm tornadoes, improve nowcasting, and provide recommendations on using 3IM.
Todd A. Murphy
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Model & Forecast Guidance, Verification & Validation
VORTEX FY22
Rapid Refresh (RAP), Rapid Update Cycle (RUC)
Seeks to improve understanding of (non-)tornadic QLCSs and how the NWS forecasts and nowcasts for QLCS tornadoes.
The project utilizes existing sounding datasets (e.g., 2016-18 VORTEX-SE field observations, MESO18-19 observations) and high-resolution model analyses (e.g., Rapid Update Cycle [RUC] and Rapid Refresh [RAP]) for data analysis.
Improve NWS nowcasts and warning performance skill scores of tornadic QLCSs, which is critical to advancing the NWS mission
NA
NA
University of Louisiana- Monroe
NA22OAR4590518
Severe Weather
Louisiana
VORTEX
August 2022 - July 2025
Data Assimilation and Ensembles, Model & Forecast Guidance, Verification & Validation
improved nowcasting of qlcs tornadoes in the united states, the goals of this project are to evaluate the 'three ingredients method' 3im for nowcasting quasi linear convective storm tornadoes improve nowcasting and provide recommendations on using 3im, seeks to improve understanding of non tornadic qlcss and how the nws forecasts and nowcasts for qlcs tornadoes the project utilizes existing sounding datasets e g 2016 18 vortex se field observations meso18 19 observations and high resolution model analyses e g rapid update cycle [ruc] and rapid refresh [rap] for data analysis
Not Applicable (N/A)
University of Louisiana- Monroe
The goals of this project are to evaluate the 'three-ingredients method' (3IM) for nowcasting quasi-linear convective storm tornadoes, improve nowcasting, and provide recommendations on using 3IM.
0
Spatiotemporal analysis of lightning and the mesoscale environment to identify significantly severe and potentially tornadic storms
Bruning
VORTEX
search_activity
2022
Level 3
The goals of this project are to understand lightning variability in mesoscale environments associated with high-shear, low-CAPE (convective available potential energy) environments in the Southeastern US, and how well it identifies tornadic thunderstorm cells.
Eric Bruning
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Examine the utility of lightning and other cloud top signatures in discriminating the tornadic potential and intensity in storms that persist across early evening transition (EET)
Aid forecasting operations by evaluating the ability of lightning and satellite observations to identify significantly severe and potentially tornadic storms. Aims to build forecaster confidence in utilizing lightning signals, backing them with sound environmental and dynamical understanding grounded in observations and physical conceptual models.
spatiotemporal analysis of lightning and the mesoscale environment to identify significantly severe and potentially tornadic storms, the goals of this project are to understand lightning variability in mesoscale environments associated with high shear low cape convective available potential energy environments in the southeastern us and how well it identifies tornadic thunderstorm cells, examine the utility of lightning and other cloud top signatures in discriminating the tornadic potential and intensity in storms that persist across early evening transition eet
Not Applicable (N/A)
Texas Tech University
The goals of this project are to understand lightning variability in mesoscale environments associated with high-shear, low-CAPE (convective available potential energy) environments in the Southeastern US, and how well it identifies tornadic thunderstorm cells.
0
Understanding and Reducing Latinx Vulnerabilities to Tornadoes in the Southeast
First
VORTEX
check_circle
2022
Level 3
The goal of this project is provide much-needed research into the various factors that place Latinx populations at risk for harm during tornado hazards and identify community-based practices that can enhance warning communication and protec\tive responses for Latinx communities.
Jennifer First
Complete
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Language & Cultural Contexts, Risk Communication, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY22
Interviews, Surveys, Spatial Analysis
Mixed Methods
Public, Vulnerable Populations
Seeks to provide much-needed research into the various factors that place Latinx populations at risk for harm during tornado hazards and identify community-based practices that can enhance warning communication and protective responses for Latinx communities.
Ensure Latinx needs and resources are included in the hazrad warning process; identify a community-based process for developing partnerships between local WFOs and Latinx communities
NA
NA
University of Tennessee
NA22OAR4590520
Severe Weather
Tennessee
VORTEX
August 2022 - July 2024
Language & Cultural Contexts, Risk Communication, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Interviews, Surveys, Spatial Analysis, Mixed Methods, Public, Vulnerable Populations
understanding and reducing latinx vulnerabilities to tornadoes in the southeast, the goal of this project is provide much needed research into the various factors that place latinx populations at risk for harm during tornado hazards and identify community based practices that can enhance warning communication and protec\tive responses for latinx communities, seeks to provide much needed research into the various factors that place latinx populations at risk for harm during tornado hazards and identify community based practices that can enhance warning communication and protective responses for latinx communities
Not Applicable (N/A)
University of Tennessee
The goal of this project is provide much-needed research into the various factors that place Latinx populations at risk for harm during tornado hazards and identify community-based practices that can enhance warning communication and protec\tive
0
Developing the timeline of information needs for the general public during tornado events
Ripberger
VORTEX
search_activity
2022
Level 3
The goal of this project includes the development of an information needs timeline for members of the public during tornado events using both stated and revealed preferences.
Joseph Ripberger
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Communicating Probabilities & Uncertainty, Information Seeking & Processing, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY22
Surveys, Social Media Analysis
Mixed Methods
Public
Provide a detailed roadmap of the most important information for members of the public relative to the event timeline
Future forecast products can be tailored to the needs of the general public (what information they need, when they need it). These outputs will be applicable to the development of new models and forecasting tools or techniques, and to the actual creation of the forecast information.
NA
NA
University of Oklahoma
NA22OAR4590519
Severe Weather
Oklahoma
VORTEX
August 2022 - July 2025
Communicating Probabilities & Uncertainty, Information Seeking & Processing, "Social Science (SBES)", Surveys, Social Media Analysis, Mixed Methods, Public
developing the timeline of information needs for the general public during tornado events, the goal of this project includes the development of an information needs timeline for members of the public during tornado events using both stated and revealed preferences, provide a detailed roadmap of the most important information for members of the public relative to the event timeline
Not Applicable (N/A)
University of Oklahoma
The goal of this project includes the development of an information needs timeline for members of the public during tornado events using both stated and revealed preferences.
1
Rural Region Readiness: Collaborative Learning through Integrated Warning Team Workshop Sessions on Tornado Safety
Hurst
VORTEX
search_activity
2023
Level 3
The goal of this project is to use social science research to develop and test an Integrated Warning Team (IWT) design, called Rural Region Readiness (RRR), specifically developed to learn about and communicate to the unique needs of vulnerable populations within the rural Appalachian region.
Elizabeth H. Hurst
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Integrated Warning Team (IWT), Risk Communication, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY23
Interviews, Workshops
Qualitative
Forecasters, Emergency Managers, Other Core Partners, Public, Vulnerable Populations
This three-year proposal utilizes social science research to develop and test an Integrated Warning Team (IWT) design, called Rural Region Readiness (RRR), specifically developed to learn about and communicate to the unique needs of vulnerable populations within the rural Appalachian region. RRR aims to integrate the public into IWT workshops, so that the workshops include: NWS Weather Forecasting Offices (WFOs), Emergency Managers (EMs) and other core partners (e.g. local media), and community member representatives.
The near-term societal benefits from this project include NWS WFO capability to gain culturally specific insight which may inform warning messaging to this population, such a forum may increase community trust towards the NWS and public understanding of tornado hazards.
NA
Danielle Nagele
Chris Maier
CASR/University of Oklahoma
NA23OAR4590349
Severe Weather
Oklahoma
VORTEX
August 2023 - July 2026
Integrated Warning Team (IWT), Risk Communication, Social Vulnerability, "Social Science (SBES)", Interviews, Workshops, Qualitative, Forecasters, Emergency Managers, Other Core Partners, Public, Vulnerable Populations
rural region readiness collaborative learning through integrated warning team workshop sessions on tornado safety, the goal of this project is to use social science research to develop and test an integrated warning team iwt design called rural region readiness rrr specifically developed to learn about and communicate to the unique needs of vulnerable populations within the rural appalachian region, this three year proposal utilizes social science research to develop and test an integrated warning team iwt design called rural region readiness rrr specifically developed to learn about and communicate to the unique needs of vulnerable populations within the rural appalachian region rrr aims to integrate the public into iwt workshops so that the workshops include nws weather forecasting offices wfos emergency managers ems and other core partners e g local media and community member representatives
Not Applicable (N/A)
CASR/University of Oklahoma
The goal of this project is to use social science research to develop and test an Integrated Warning Team (IWT) design, called Rural Region Readiness (RRR), specifically developed to learn about and communicate to the
0
Risk and Crisis Communication, Impact Assessment Standards, and Best Practices for Effective Sheltering Decisions in Response to Structural Vulnerability
Myers
VORTEX
search_activity
2023
Level 3
The goal of this project is to investigate the personalized and actionable risk and crisis information that the public needs to make the most effective sheltering decisions in conjunction with their unique structural vulnerability.
Laura Myers, Jennifer Collins, David Roueche
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Structural Engineering, Decision-Making, Risk Perception & Response, Social Vulnerability, "Social, Behavioral, and Economic Sciences (SBES)"
VORTEX FY23
Interviews, Surveys, Focus Groups
Mixed Methods
Public
This interdisciplinary study will investigate the personalized and actionable risk and crisis information that the public needs to make the most effective sheltering decisions for severe weather while referencing their unique structural vulnerability perceptions. Household decision-maker perceptions of structural vulnerability and severe weather impact risk will be analyzed to determine how personalized and actionable risk and crisis information informs their sheltering decision making.
Understanding the timing and types of risk and crisis information that makes a difference with the publics' perceived structural vulnerabilities will lead to a guidebook of standards and best practices in communicating structural vulnerability risk. Analysis results will lead to significant positive societal benefits by improving the content and timing of warning messaging that addresses the personalized needs of the public and their hazard adjustment adoption.
NA
Danielle Nagele
Leticia Williams
University of Alabama
NA23OAR4590345,NA23OAR4590346, NA23OAR4590347
Severe Weather
Alabama, Florida, Georgia, Mississippi, Wisconsin
VORTEX
August 2023 - July 2026
Structural Engineering, Decision-Making, Risk Perception & Response, Social Vulnerability, "Social Science (SBES)", Interviews, Surveys, Focus Groups, Mixed Methods, Public
risk and crisis communication impact assessment standards and best practices for effective sheltering decisions in response to structural vulnerability, the goal of this project is to investigate the personalized and actionable risk and crisis information that the public needs to make the most effective sheltering decisions in conjunction with their unique structural vulnerability, this interdisciplinary study will investigate the personalized and actionable risk and crisis information that the public needs to make the most effective sheltering decisions for severe weather while referencing their unique structural vulnerability perceptions household decision maker perceptions of structural vulnerability and severe weather impact risk will be analyzed to determine how personalized and actionable risk and crisis information informs their sheltering decision making
Not Applicable (N/A)
University of Alabama
The goal of this project is to investigate the personalized and actionable risk and crisis information that the public needs to make the most effective sheltering decisions in conjunction with their unique structural vulnerability.
3
VORTEX-USA: An Investigation of Forecast Improvements Realized Through Ensemble Sensitivity Analysis and Subsetting
Weiss
VORTEX
search_activity
2023
Level 3
The goal of this project are to simulate ten cases from previous VORTEX-SE field observations to produce high resolution WFR simulations, and evaluate the simulations with ensemble sensitivity analysis and ensemble subsetting.
Christopher Charles Weiss
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Data Assimilation and Ensembles, Dynamics and Nesting, High-Performance Computing (HPC)
VORTEX FY23
Weather Research and Forecasting Model (WRF)
Quantitative
We will produce high-resolution (1-km horizontal grid spacing) WRF simulations of up to ten cases from previous VORTEX-SE field programs (VORTEX-SE 2016, 2017, 2018, Meso18-19). These cases will be viewed through the lens of ESA to assess whether sensitivity structures have sufficient coverage and consistency. Then, considering the outlay of operational and field-grade observations from the aforementioned periods of data collection, cases will be selected for further study using ensemble subsetting.
The products from this investigation will benefit research, operations and the public overall. The ESA and subsetting techniques described will support a more streamlined forecast approach, whereby poorly performing members of an ensemble can be objectively diminished, allowing for greater accuracy and efficiency for forecasts which, in turn, will point to improved outcomes for the public via greater lead time and reduced false alarms for convective hazards.
NA
Israel Jirak
Texas Tech University
NA23OAR4590348
Severe Weather
Colorado, Texas
VORTEX
August 2023 - July 2026
Data Assimilation and Ensembles, Dynamics and Nesting, High-Performance Computing (HPC), Quantitative
vortex usa an investigation of forecast improvements realized through ensemble sensitivity analysis and subsetting, the goal of this project are to simulate ten cases from previous vortex se field observations to produce high resolution wfr simulations and evaluate the simulations with ensemble sensitivity analysis and ensemble subsetting, we will produce high resolution 1 km horizontal grid spacing wrf simulations of up to ten cases from previous vortex se field programs vortex se 2016 2017 2018 meso18 19 these cases will be viewed through the lens of esa to assess whether sensitivity structures have sufficient coverage and consistency then considering the outlay of operational and field grade observations from the aforementioned periods of data collection cases will be selected for further study using ensemble subsetting
Not Applicable (N/A)
Texas Tech University
The goal of this project are to simulate ten cases from previous VORTEX-SE field observations to produce high resolution WFR simulations, and evaluate the simulations with ensemble sensitivity analysis and ensemble subsetting.
0
Examining the environmental relationships and processes influencing tornado behavior in the Southeast United States
Miller
VORTEX
search_activity
2023
Level 3
The goal of this project is to better understand the impact of environmental heterogeneities on storm and tornado evolution and behavior to improve forecasts, using VORTEX-SE and PERiLS data to model and evaluate forecasts.
Rachel Miller
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Verification & Validation
VORTEX FY23
Quantitative
The overarching goal of this project is to better understand the extent of environmental influences on storm and tornado evolution in order to increase forecast performance. The overarching theme is the impact of environmental inhomogeneities (storm-induced or otherwise) on storm behavior in the southeast U.S. The short-term goal is to quantify differences in environmental and storm characteristics between different tornado events (e.g., short- vs. long-lived, discrete vs. other storm modes, etc.) and to examine their influence in experimental analyses and guidance.
These outcomes will further our basic understanding of processes influencing tornado behavior in the SE US. They will also provide novel analyses of how these processes are represented in experimental forecast guidance. To this end, this work is critical for improving general tornado warning performance across all convective storm modes, better protecting lives and property, and establishing a weather-ready nation.
NA
Israel Jirak
CIWRO/University of Oklahoma
NA23OAR4590344
Severe Weather
Oklahoma
VORTEX
August 2023 - July 2026
Model & Forecast Guidance, Verification & Validation, Quantitative
examining the environmental relationships and processes influencing tornado behavior in the southeast united states, the goal of this project is to better understand the impact of environmental heterogeneities on storm and tornado evolution and behavior to improve forecasts using vortex se and perils data to model and evaluate forecasts, the overarching goal of this project is to better understand the extent of environmental influences on storm and tornado evolution in order to increase forecast performance the overarching theme is the impact of environmental inhomogeneities storm induced or otherwise on storm behavior in the southeast u s the short term goal is to quantify differences in environmental and storm characteristics between different tornado events e g short vs long lived discrete vs other storm modes etc and to examine their influence in experimental analyses and guidance
Not Applicable (N/A)
CIWRO/University of Oklahoma
The goal of this project is to better understand the impact of environmental heterogeneities on storm and tornado evolution and behavior to improve forecasts, using VORTEX-SE and PERiLS data to model and evaluate forecasts.
0
Large Eddy Simulation of Tornado-Structure Interaction: Pathways to Reducing Societal Impacts
Bodine
VORTEX
search_activity
2023
Level 3
The goal of this project is to integrate observation and large eddy simulations to understand the tornado wind field and associated damage and debris in urban areas.
David Bodine
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Numerical Weather Prediction (NWP), Structural Engineering
VORTEX FY23
Cloud Model 1 (CM1)
Quantitative
This project focuses on low-level wind flow effects on and by structures during tornadic events. It also focuses on increasing the impact of existing simulations and the production of new, targeted and more realistic simulations to strengthen results about tornado wind fields, wind-structure interaction, and damage prediction. Enhanced realism in the simulations is included based on observations of tornado damage and radar, as well as a new simulation framework to improve turbulence representation using Cloud Model 1.
The outcomes will include the development of a real-time wind field and damage estimation tool and recommendations for changes to building codes and standards to increase resilience to tornado loading.
large eddy simulation of tornado structure interaction pathways to reducing societal impacts, the goal of this project is to integrate observation and large eddy simulations to understand the tornado wind field and associated damage and debris in urban areas, this project focuses on low level wind flow effects on and by structures during tornadic events it also focuses on increasing the impact of existing simulations and the production of new targeted and more realistic simulations to strengthen results about tornado wind fields wind structure interaction and damage prediction enhanced realism in the simulations is included based on observations of tornado damage and radar as well as a new simulation framework to improve turbulence representation using cloud model 1
Not Applicable (N/A)
University of Oklahoma
The goal of this project is to integrate observation and large eddy simulations to understand the tornado wind field and associated damage and debris in urban areas.
0
A novel method for improving fine particulate matter air quality forecasts during wildfires
Kumar
Synoptic
check_circle
2019
Level 3
This project aims to develop a novel method to significantly improve the accuracy of the NAQFC PM2.5 forecasts by improving initialization of the NAQFC via chemical data assimilation of satellite aerosol optical depth (AOD) retrievals in the NAQFC, and by generating a more accurate long-term forecast archive for better training of analog-based post-processing techniques.
Rajesh Kumar
Complete
Sat Jun 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Wed May 31 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Post-processing, Satellite & Remote Sensing Technologies
AQRF
NOAA's National Air Quality Forecasting Capability (NAQFC)
This project aims to improve National Air Quality Forecast Capability (NAQFC) model initialization through the assimilation of chemical data from satellite aerosol optical depth retrievals, to improve the accuracy of forecasts during wildfires.
Air quality forecasts during high-impact wildfires will be more accurate and help inform decision-makers during wildfire events.
EMC
Jeff McQueen
National Center for Atmospheric Research
NA19OAR4590083
Fire Weather
Colorado
June 2019 - May 2023
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Post-processing, Satellite & Remote Sensing Technologies
a novel method for improving fine particulate matter air quality forecasts during wildfires, this project aims to develop a novel method to significantly improve the accuracy of the naqfc pm2 5 forecasts by improving initialization of the naqfc via chemical data assimilation of satellite aerosol optical depth aod retrievals in the naqfc and by generating a more accurate long term forecast archive for better training of analog based post processing techniques, this project aims to improve national air quality forecast capability naqfc model initialization through the assimilation of chemical data from satellite aerosol optical depth retrievals to improve the accuracy of forecasts during wildfires
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
This project aims to develop a novel method to significantly improve the accuracy of the NAQFC PM2.5 forecasts by improving initialization of the NAQFC via chemical data assimilation of satellite aerosol optical depth (AOD) retrievals
1
Towards Optimal Configurations of NAQFC Chemistry and Aerosol Representations
Zhang
Synoptic
check_circle
2019
Level 3
To address a critical need of accurate operational forecasts of O3 and PM2.5 through improving chemical mechanisms and secondary organic aerosol (SOA) representations, identifying the best chemistry and physics configurations for operational forecasting and reducing persistent, systematic model biases domain-wide and for specific regions, seasons, and hours.
Yang Zhang, Daniel Tong
Complete
Sat Jun 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Atmospheric Physics
AQRF
Community Multiscale Air Quality (CMAQ) Model, Finite-Volume Cubed-Sphere Dynamical Core (FV3), NOAA's National Air Quality Forecasting Capability (NAQFC)
This project will test gas-phase chemistry and secondary organic aerosol (SOA) modules in FV3-CMAQ [Community Air Quality Multi-scale Model] v5.3; minimize systematic, persistent time- and region-specific, and gaseous precursor biases; and employ a novel bias reduction method (i.e., M2K3) to enhance the model's accuracy.
This research will advance the skill of operational forecasting from local to national levels, and inform the U.S. Environmental Protection Agency's (EPA) CMAQ stakeholders as they determine research priorities.
EMC
Jun Du
Northeastern University
(previously North Carolina State University);
George Mason University
Atmospheric Composition and Chemistry, Atmospheric Physics
towards optimal configurations of naqfc chemistry and aerosol representations, to address a critical need of accurate operational forecasts of o3 and pm2 5 through improving chemical mechanisms and secondary organic aerosol soa representations identifying the best chemistry and physics configurations for operational forecasting and reducing persistent systematic model biases domain wide and for specific regions seasons and hours, this project will test gas phase chemistry and secondary organic aerosol soa modules in fv3 cmaq [community air quality multi scale model] v5 3 minimize systematic persistent time and region specific and gaseous precursor biases and employ a novel bias reduction method i e m2k3 to enhance the model's accuracy
Environmental Modeling Center (EMC)
Northeastern University, (previously North Carolina State University);, George Mason University
To address a critical need of accurate operational forecasts of O3 and PM2.5 through improving chemical mechanisms and secondary organic aerosol (SOA) representations, identifying the best chemistry and physics configurations for operational forecasting and reducing
5
Development of the National Global Data Assimilation Ensemble-based System for Forecasting of Aerosols
Pagowski
Synoptic
check_circle
2019
Level 3
The purpose is to develop a national global aerosol data assimilation system for aerosol forecasting.
Mariusz Pagowski
Complete
Sat Jun 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue May 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles
AQRF
Unified Forecast System (UFS)
Researchers intend to build a global aerosol data assimilation system for real-time aerosol forecasting at the national level. Given the role of atmospheric aerosols in air pollution, weather and climate, researchers intend to enhance estimation of these aerosols through data assimilation.
Given the role of atmospheric aerosols in air pollution, weather and climate, researchers intend to enhance estimation of these aerosols
EMC
Daryl Kleist
CIRES/University of Colorado
NA19OAR4590080
Fire Weather
Colorado
June 2019 - May 2022
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles
development of the national global data assimilation ensemble based system for forecasting of aerosols, the purpose is to develop a national global aerosol data assimilation system for aerosol forecasting, researchers intend to build a global aerosol data assimilation system for real time aerosol forecasting at the national level given the role of atmospheric aerosols in air pollution weather and climate researchers intend to enhance estimation of these aerosols through data assimilation
Environmental Modeling Center (EMC)
CIRES/University of Colorado
The purpose is to develop a national global aerosol data assimilation system for aerosol forecasting.
2
NAQFC Community Emission Testbed (NCET): Accelerating anthropogenic emission updates for NAQFC FV3-CMAQ through community collaboration
Baek
Synoptic
check_circle
2019
Level 3
This project will develop a community emission system that can be used to generate high quality emissions cost-effectively. Once mature, each update can be easily ported to and tested at NOAA operational environment, as the proposed datasets and improvements are independent of the original systems from which they are developed.
Bok Baek, Daniel Tong
Complete
Sat Jun 01 2019 03:00:00 GMT-0400 (Eastern Daylight Time)
Sun Jul 31 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry
AQRF
Community Multiscale Air Quality (CMAQ) Model, Finite-Volume Cubed-Sphere Dynamical Core (FV3), NOAA's National Air Quality Forecasting Capability (NAQFC), Unified Forecast System (UFS)
Researchers intend to advance the National Air Quality Forecast Capability (NAQFC) Community Emission Testbed (NCET) to eventually serve as a platform for the emissions community to submit new datasets for use in NAQFC and FV3-chem. The researchers will employ NCET to expedite anthropogenic emission inventory processing and to reduce uncertainties related to wildfire emissions.
The goal of this project is to reduce uncertainties related to wildfire emissions, thereby creating more accurate and actionable air quality forecasts during high-impact wildfire events.
EMC
Jun Du
George Mason University
(previously University of North Carolina);
George Mason University
naqfc community emission testbed ncet accelerating anthropogenic emission updates for naqfc fv3 cmaq through community collaboration, this project will develop a community emission system that can be used to generate high quality emissions cost effectively once mature each update can be easily ported to and tested at noaa operational environment as the proposed datasets and improvements are independent of the original systems from which they are developed, researchers intend to advance the national air quality forecast capability naqfc community emission testbed ncet to eventually serve as a platform for the emissions community to submit new datasets for use in naqfc and fv3 chem the researchers will employ ncet to expedite anthropogenic emission inventory processing and to reduce uncertainties related to wildfire emissions
Environmental Modeling Center (EMC)
George Mason University, (previously University of North Carolina);, George Mason University
This project will develop a community emission system that can be used to generate high quality emissions cost-effectively. Once mature, each update can be easily ported to and tested at NOAA operational environment, as the
12
Enhancing high resolution forecasting capability of RRFS-CMAQ
Zhang
Synoptic
search_activity
2022
Level 3
Purpose is to enhance high resolution forecasting capability of the National Air Quality Forecast Capability (NAQFC) based on the inline RRFS-CMAQ model system by developing high resolution (3-km) anthropogenic emissions based on the US national emissions inventories (NEI) and an offline coupled RRFS-CMAQ.
Yang Zhang, Daniel Tong, Fanglin Yang
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Dynamics and Nesting
AQRF
Community Multiscale Air Quality (CMAQ) Model, Global Forecast System (GFS), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
Recent advancements for regional air quality modeling and forecasting include improved
model representations of major atmospheric processes based on up-to-date scientific
understanding, the use of online-coupled meteorology-chemistry models to more realistically
replicate the atmosphere than offline models, and the increased spatial grid resolutions from 12-
36 km to 1-4 km. For example, Appel et al.1 showed that the Community Multiscale Air Quality
(CMAQ) model version 5.3.1 helped reduce CMAQ v5.2.1 wintertime ozone (O3) bias (due
mainly to the reduced dry deposition to snow) and summertime fine particulate matter (PM2.5)
bias (due mainly to updated chemistry and aerosol representations). Chen et al.2 reported
reduced biases in O3 and PM2.5 forecasts in most months from CMAQv5.3.1 offline-coupled
with Global Forecast System (GFS) version 15 in National Air Quality Forecast
Capability (NAQFC) compared to those from CMAQv5.0.2. Online-coupled meteorology-
chemistry models have been developed to simulate chemistry feedbacks into meteorology.3-5
Compared to offline models, these models have shown improved skills in terms of meteorology
(e.g., shortwave radiation, temperature, wind speed, precipitation)6,7 and surface O3 and major
PM2.5 composition predictions.7 Atmospheric model performance can differ at different grid
resolutions.8-17 The use of a fine grid resolution (e.g., 3- and 4-km) generally performs better for
meteorological and chemical predictions than at coarser resolutions (e.g., 36- and 12-km),14-16 as
higher-resolution simulations can better resolve air pollutant emissions, land use and cover, and
stagnant meteorological conditions, in particular, the wintertime stable boundary layer with cold-
air pools in the complex terrain.13,17
This 3-yr project is expected to greatly enhance high resolution forecasting capability of RRFS-CMAQ via performing Tasks 1-2 in Yr 1, Task 3 in Yr 2, and Task 4 in Yr 3. This work will (1) produce 3-km emissions based on NEI2016 and NEI2020 over NW US and NE US, respectively; (2) generate an offline RRFS-CMAQ with consistent physics, chemistry, and aerosol representations with the inline RRFS-CMAQ; (3) contribute to the updated inline RRFS-CMAQ with improved O3 and PM2.5 forecasting skills at a Readiness Level (RL) 8 for R2O transition; (4) demonstrate the applications of inline RRFS-CMAQ at 13- and 3-km resolutions for FIREX-AQ 2019 and AEROMMA 2023. The methodology for high resolution emission generation can be generalized to produce model ready emissions over the CONUS at 3-km or finer. The offline RRFS-CMAQ will be a useful system to benchmark the inline RRFS-CMAQ to facilitate its development and improvement and to demonstrate its benefits of the coupled weather-air quality modeling for NOAA NAQFC team and the community users. The O3 and PM2.5 forecasts at 3-km over NW US and NE US can be used to assess their health impacts at a fine scale and analyze policy implications and societal impacts of air pollution for regions with environmental justice vs. injustice. The proposed work will not only benefit NAQFC, RRFS-UFS, and other stakeholders, but also inform future model improvement for EPA's CMAQ, NOAA's WRF-Chem, and other community models. The project products will be useful for atmospheric science communities, health communities, city decision makers, and general public.
EMC
Fanglin Yang (EMC)
Northeastern University,
George Mason University,
NOAA/NWS/NCEP/EMC
NA22OAR4590515, NA22OAR4590512
Fire Weather
Massachusetts, Virginia
August 2022 - July 2026
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Dynamics and Nesting
enhancing high resolution forecasting capability of rrfs cmaq, purpose is to enhance high resolution forecasting capability of the national air quality forecast capability naqfc based on the inline rrfs cmaq model system by developing high resolution 3 km anthropogenic emissions based on the us national emissions inventories nei and an offline coupled rrfs cmaq, recent advancements for regional air quality modeling and forecasting include improved model representations of major atmospheric processes based on up to date scientific understanding the use of online coupled meteorology chemistry models to more realistically replicate the atmosphere than offline models and the increased spatial grid resolutions from 12 36 km to 1 4 km for example appel et al 1 showed that the community multiscale air quality cmaq model version 5 3 1 helped reduce cmaq v5 2 1 wintertime ozone o3 bias due mainly to the reduced dry deposition to snow and summertime fine particulate matter pm2 5 bias due mainly to updated chemistry and aerosol representations chen et al 2 reported reduced biases in o3 and pm2 5 forecasts in most months from cmaqv5 3 1 offline coupled with global forecast system gfs version 15 in national air quality forecast capability naqfc compared to those from cmaqv5 0 2 online coupled meteorology chemistry models have been developed to simulate chemistry feedbacks into meteorology 3 5 compared to offline models these models have shown improved skills in terms of meteorology e g shortwave radiation temperature wind speed precipitation 6 7 and surface o3 and major pm2 5 composition predictions 7 atmospheric model performance can differ at different grid resolutions 8 17 the use of a fine grid resolution e g 3 and 4 km generally performs better for meteorological and chemical predictions than at coarser resolutions e g 36 and 12 km 14 16 as higher resolution simulations can better resolve air pollutant emissions land use and cover and stagnant meteorological conditions in particular the wintertime stable boundary layer with cold air pools in the complex terrain 13 17
Environmental Modeling Center (EMC)
Northeastern University, George Mason University, NOAA/NWS/NCEP/EMC
Purpose is to enhance high resolution forecasting capability of the National Air Quality Forecast Capability (NAQFC) based on the inline RRFS-CMAQ model system by developing high resolution (3-km) anthropogenic emissions based on the US national
12
A novel dynamical ensemble design for probabilistic air quality predictions during wildfires based on RRFS-CMAQ
Alessandrini
Synoptic
search_activity
2022
Level 3
This project aims to take NAQFC a step further in the direction of probabilistic air quality predictions during wildfires by exploring and quantifying the potential value of ensemble predictions of air quality with a novel ensemble design using RRFS-CMAQ.
Rajesh Kumar Stefano Alessandrini
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Dynamics and Nesting, High-Performance Computing (HPC), Probabilistic Weather Forecasting, Verification & Validation
AQRF
Community Multiscale Air Quality (CMAQ) Model, NOAA's National Air Quality Forecasting Capability (NAQFC), Rapid Refresh Forecast System (RRFS)
This project aims to take NAQFC a step further in the direction of probabilistic air
quality predictions during wildfires by exploring and quantifying the potential value of
ensemble predictions of air quality with a novel ensemble design using RRFS-CMAQ. We
will follow a methodology developed by the project PIs for probabilistic PM2.5 forecasts. The
project will build off of the next generation air quality model named RRFS-CMAQ. We will use
the extreme fire season of June-October 2020 as a testbed for developing, evaluating, and
optimizing the ensemble size for operational applications. Our ensemble members will perturb
key aspects of air quality modeling: (a) meteorological and chemical initial and lateral boundary
conditions, (b) anthropogenic, biogenic, and biomass burning emissions, (c) secondary organic
aerosol (SOA) response to temperature changes and updating the solubility of semi-volatile
organic compounds (SVOCs), and (d) removal processes including the hygroscopicity of
aerosols and dry deposition velocities of O3, precursors, and SVOCs. The combination of these
perturbations can result in a large number (> 50) of ensemble members which could be
computationally infeasible for the NAQFC operations. We will address this limitation with a
down-selection technique developed by the PIs to reduce the number of proposed ensemble
members down to 10 while preserving for the most part the skills and quality of the original
ensemble. We will also apply bias-correction and calibration techniques to reduce the ensemble
expected biases in both the first and second moment of the predictive distribution. To
demonstrate the operational value of probabilistic predictions, we will perform probabilistic air
quality predictions in real-time using the optimized 10-member ensemble during the fire season
of 2024. The proposed ensemble is expected to significantly improve the accuracy of operational
O3 and PM2.5 predictions, with the calibrated ensemble mean being the best forecast than any of
the ensemble members. Furthermore, the proposed quantification of O3 and PM2.5 prediction
uncertainty will be a key factor to support cost-effective air quality decision-making activities.
The proposed improvements in the accuracy of the NAQFC deterministic O3
and PM2.5 predictions, along with a reliable quantification of their uncertainties, will allow air quality forecasters across the nation to put more confidence in using the NAQFC products in their decision-making process and better protect public health. As shown in our recent studies, a calibrated dynamical ensemble exhibited almost zero MB across seasons with the reduction in MB by 40-100% and the RMSE by 13-40% in different parts of CONUS (Kumar et al., 2020a).
EMC
Jeff McQueen (EMC)
Fanglin Yang (EMC)
National Center for Atmospheric Research
NA22OAR4590513
Fire Weather
Colorado
August 2022 - July 2026
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Dynamics and Nesting, High-Performance Computing (HPC), Probabilistic Weather Forecasting, Verification & Validation
a novel dynamical ensemble design for probabilistic air quality predictions during wildfires based on rrfs cmaq, this project aims to take naqfc a step further in the direction of probabilistic air quality predictions during wildfires by exploring and quantifying the potential value of ensemble predictions of air quality with a novel ensemble design using rrfs cmaq, this project aims to take naqfc a step further in the direction of probabilistic air quality predictions during wildfires by exploring and quantifying the potential value of ensemble predictions of air quality with a novel ensemble design using rrfs cmaq we will follow a methodology developed by the project pis for probabilistic pm2 5 forecasts the project will build off of the next generation air quality model named rrfs cmaq we will use the extreme fire season of june october 2020 as a testbed for developing evaluating and optimizing the ensemble size for operational applications our ensemble members will perturb key aspects of air quality modeling a meteorological and chemical initial and lateral boundary conditions b anthropogenic biogenic and biomass burning emissions c secondary organic aerosol soa response to temperature changes and updating the solubility of semi volatile organic compounds svocs and d removal processes including the hygroscopicity of aerosols and dry deposition velocities of o3 precursors and svocs the combination of these perturbations can result in a large number > 50 of ensemble members which could be computationally infeasible for the naqfc operations we will address this limitation with a down selection technique developed by the pis to reduce the number of proposed ensemble members down to 10 while preserving for the most part the skills and quality of the original ensemble we will also apply bias correction and calibration techniques to reduce the ensemble expected biases in both the first and second moment of the predictive distribution to demonstrate the operational value of probabilistic predictions we will perform probabilistic air quality predictions in real time using the optimized 10 member ensemble during the fire season of 2024 the proposed ensemble is expected to significantly improve the accuracy of operational o3 and pm2 5 predictions with the calibrated ensemble mean being the best forecast than any of the ensemble members furthermore the proposed quantification of o3 and pm2 5 prediction uncertainty will be a key factor to support cost effective air quality decision making activities
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
This project aims to take NAQFC a step further in the direction of probabilistic air quality predictions during wildfires by exploring and quantifying the potential value of ensemble predictions of air quality with a novel
0
Beyond the "Big-Leaf" Model at NOAA: Use of Novel Satellite Data and In-Canopy Processes to Improve U.S. Air Quality Predictions
Campbell
Synoptic
search_activity
2022
Level 3
This project follows a three-step/year integrated process centered on improving RRFS-CMAQ.
Patrick Campbell
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Atmospheric Composition and Chemistry, Dynamics and Nesting, Parameterization, Satellite & Remote Sensing Technologies
AQRF
Community Multiscale Air Quality (CMAQ) Model, Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
The proposal, Beyond the "Big-Leaf" Model at NOAA: Use of Novel Satellite Data and In-
Canopy Processes to Improve U.S. Air Quality Predictions, is submitted to the FY2022 NOAA-
OAR-WPO Funding Opportunity, specifically the Atmospheric Composition (AC) Competition.
In this three-year project, we propose to advance the next-generation Rapid-Refresh Forecast
System (RRFS)-Community Multiscale Air Quality (CMAQ) model beyond its "Big-Leaf"
canopy approximation by using forest canopy processes and novel satellite data that can
ultimately improve ozone predictions in the U.S. This work has implications for the RRFS-
CMAQ, and other next-generation, Unified Forecast System-based Atmospheric Composition-
Limited Area Model (AC-LAM) predictions at NOAA, while further providing necessary
scientific advancements to improve chemical transport models in the scientific community.
This project follows a three-step/year integrated process centered on improving RRFS-CMAQ:
1. Advanced development of observation- and theoretically-based forest in-canopy
parameterizations that include photolysis attenuation, turbulence effects, and
emissions/scalar transport (Years 1-2).
2. Incorporation and testing of novel satellite-based land surface/vegetation data from the
Global Ecosystem Dynamics Investigation (GEDI), which includes updated forest canopy
heights, to further advance the canopy parameterizations (Year 2).
3. Full-year simulations and evaluation using both 2D and 3D observations to demonstrate
model performance changes and readiness for R2O transition (Year 3).
Addition of in-canopy effects and improved canopy representation will leverage the UFS-based coupling strategy between the meteorological, land, and chemistry model components in a more physically consistent fashion within the RRFS-CMAQ framework. In this way, for example, the land surface model component can be directly coupled (e.g., LAI) with the in-canopy parameterizations in the integrated physics and chemistry model components in RRFS-CMAQ. The addition of GEDI canopy information (e.g., canopy height) is also particularly important for operational NOAA AQF models. This is because of 1) relatively high complexity and spatial resolutions needed to accurately represent land cover and vegetative characteristics,
2) inclusion of comprehensive atmospheric chemistry and aerosol formation components in RRFS-CMAQ, and 3) interactions of 1 and 2 via atmosphere-biosphere exchange, chemistry, and scalar transport that are critical to chemical and aerosol predictions.
EMC
Fanglin Yang (EMC)
George Mason University
NA22OAR4590516
Extreme Temperatures
Virginia
August 2022 - July 2026
Atmospheric Composition and Chemistry, Dynamics and Nesting, Parameterization, Satellite & Remote Sensing Technologies
beyond the "big leaf" model at noaa use of novel satellite data and in canopy processes to improve u s air quality predictions, this project follows a three step year integrated process centered on improving rrfs cmaq, the proposal beyond the "big leaf" model at noaa use of novel satellite data and in canopy processes to improve u s air quality predictions is submitted to the fy2022 noaa oar wpo funding opportunity specifically the atmospheric composition ac competition in this three year project we propose to advance the next generation rapid refresh forecast system rrfs community multiscale air quality cmaq model beyond its "big leaf" canopy approximation by using forest canopy processes and novel satellite data that can ultimately improve ozone predictions in the u s this work has implications for the rrfs cmaq and other next generation unified forecast system based atmospheric composition limited area model ac lam predictions at noaa while further providing necessary scientific advancements to improve chemical transport models in the scientific community this project follows a three step year integrated process centered on improving rrfs cmaq 1 advanced development of observation and theoretically based forest in canopy parameterizations that include photolysis attenuation turbulence effects and emissions scalar transport years 1 2 2 incorporation and testing of novel satellite based land surface vegetation data from the global ecosystem dynamics investigation gedi which includes updated forest canopy heights to further advance the canopy parameterizations year 2 3 full year simulations and evaluation using both 2d and 3d observations to demonstrate model performance changes and readiness for r2o transition year 3
Environmental Modeling Center (EMC)
George Mason University
This project follows a three-step/year integrated process centered on improving RRFS-CMAQ.
12
Using Deep Learning to Improve Knowledge of Synoptic Conditions Leading to Extreme Fire Weather and Behavior
Kumler
Synoptic
search_activity
2022
Level 3
The goal of this project is to develop a deep learning model predicting the probability of extreme fire weather and behavior.
Christina Kumler, Ryan Lagerquist
Active
Thu Dec 01 2022 03:00:00 GMT-0500 (Eastern Standard Time)
Sun Nov 30 2025 03:00:00 GMT-0500 (Eastern Standard Time)
using deep learning to improve knowledge of synoptic conditions leading to extreme fire weather and behavior, the goal of this project is to develop a deep learning model predicting the probability of extreme fire weather and behavior, to improve short term and medium term forecasting associated with the near fire environment
Environmental Modeling Center (EMC)
CIRES/University of Colorado Boulder, CIRA/Colorado State University, NOAA/OAR/GSL
The goal of this project is to develop a deep learning model predicting the probability of extreme fire weather and behavior.
4
Real-time RRFS-Smoke hybrid data assimilation of VIIRS/GOES-16/17 AOD and surface PM2.5 observations for improved regional smoke and fire weather forecasting
Allen
Synoptic
search_activity
2022
Level 3
The goal of this project is to assimilate aerosol particulate matter (PM2.5) observations into RRFSv2 to improve smoke forecasts.
Blake Allen
Active
Thu Dec 01 2022 03:00:00 GMT-0500 (Eastern Standard Time)
Mon Nov 30 2026 03:00:00 GMT-0500 (Eastern Standard Time)
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Satellite & Remote Sensing Technologies
FireWx
Model for Prediction Across Scales (MPAS), Rapid Refresh Forecast System (RRFS)
to accelerate the next-generation RRFS-JEDI system's ability to accurately forecast smoke tracers through the assimilation of satellite and ground-based observations.
Users internal to NOAA and the general public
EMC
Youngsun Jung (EMC)
CIRA/Colorado State University
NOAA/OAR/GSL
NA23OAR4590111
Fire Weather
Colorado
December 2022 - November 2026
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Satellite & Remote Sensing Technologies
real time rrfs smoke hybrid data assimilation of viirs goes 16 17 aod and surface pm2 5 observations for improved regional smoke and fire weather forecasting, the goal of this project is to assimilate aerosol particulate matter pm2 5 observations into rrfsv2 to improve smoke forecasts, to accelerate the next generation rrfs jedi system's ability to accurately forecast smoke tracers through the assimilation of satellite and ground based observations
Environmental Modeling Center (EMC)
CIRA/Colorado State University, NOAA/OAR/GSL
The goal of this project is to assimilate aerosol particulate matter (PM2.5) observations into RRFSv2 to improve smoke forecasts.
0
Integrating Fire Weather Forecast System Verification into METplus
Siems-Anderson
Synoptic
search_activity
2022
Level 3
The goal of this project is to develop a fire weather verification dataset based on observed wildland fires, verification metrics integrated into METPlus, and METPlus use-cases.
Amanda Siems-Anderson
Active
Thu Dec 01 2022 03:00:00 GMT-0500 (Eastern Standard Time)
Sun Nov 30 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Verification & Validation
FireWx
Model Evaluation Tools Plus (METPlus), Unified Forecast System (UFS)
to build on the verification work performed for the Colorado Fire Prediction System (CO-FPS) project to make consistent metrics available for forecasters, IMETs, and other wildland fire response agencies.
benefit IMET deployments and local office fire weather operations
EMC
SME Needed
National Center for Atmospheric Research
NA23OAR4590112
Fire Weather
Colorado
December 2022 - November 2025
Verification & Validation
integrating fire weather forecast system verification into metplus, the goal of this project is to develop a fire weather verification dataset based on observed wildland fires verification metrics integrated into metplus and metplus use cases, to build on the verification work performed for the colorado fire prediction system co fps project to make consistent metrics available for forecasters imets and other wildland fire response agencies
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
The goal of this project is to develop a fire weather verification dataset based on observed wildland fires, verification metrics integrated into METPlus, and METPlus use-cases.
1
Development of Improved Short-Term, Probabilistic Fire Weather Prediction and Fire Forecast Sensitivity Analysis
Ancell
Synoptic
search_activity
2022
Level 3
The goal of this project is to develop a significant fire parameter (SFP) to implement into NWS Storm Prediction Center's fire weather forecasting operations.
Brian Ancell
Active
Thu Dec 01 2022 03:00:00 GMT-0500 (Eastern Standard Time)
Sun Nov 30 2025 03:00:00 GMT-0500 (Eastern Standard Time)
to integrate the proposed SFP into the NWS operational suite both nationally (through the Storm Prediction Center) and locally (through individual forecast offices), and thus a key activity will be the ingestion and evaluation of the SFP into the software infrastructure in place at the NWS
end users (e.g., emergency managers, governing bodies, etc.), Storm Prediction Center (SPC) and individual forecast offices
development of improved short term probabilistic fire weather prediction and fire forecast sensitivity analysis, the goal of this project is to develop a significant fire parameter sfp to implement into nws storm prediction center's fire weather forecasting operations, to integrate the proposed sfp into the nws operational suite both nationally through the storm prediction center and locally through individual forecast offices and thus a key activity will be the ingestion and evaluation of the sfp into the software infrastructure in place at the nws
National Weather Service (NWS)
Texas Tech University
The goal of this project is to develop a significant fire parameter (SFP) to implement into NWS Storm Prediction Center's fire weather forecasting operations.
2
Implementing a state-of-the-science fire behavior model in the Unified Forecast System
Munoz
Synoptic
search_activity
2022
Level 3
Efforts will be oriented to implement and properly verify the implementation of the fire behavior model.
Pedro Jimenez Munoz, Branko Kosovic
Active
Mon Aug 01 2022 03:00:00 GMT-0400 (Eastern Daylight Time)
Coupled Models & Techniques, Data Assimilation and Ensembles
FireWx
Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS), Weather Research and Forecasting Model (WRF), Weather Research and Forecasting Model - Fire (WRF-Fire)
We propose to contribute to mitigating the adverse effects of wildland fires by incorporating a
state-of-the-science fire behavior model into the Unified Forecast System (UFS). The fire model
was developed at NCAR during the 1990s, and it was enhanced and incorporated into the
Weather Research and Forecasting (WRF) model during the 2010s (i.e., WRF-Fire). More
recently (2016-2020), the present team used and enhanced WRF-Fire as the core predictive
capability in the development of the Colorado Fire Prediction system (CO-FPS). The fire
behavior model accounts for fire-atmosphere feedbacks. It receives winds and humidity from the
atmospheric model, and uses them to propagate the fire; in turn, the fire model feeds back to the
atmosphere by releasing heat and moisture, and produces smoke from the combustion process.
This fire-atmosphere coupling is necessary to properly predict the evolution of wildland fires.
The outcome of the project is to explicitly simulate the fire-atmospheric feedbacks with
UFS for the first time which will allow for simulating the 1) evolution of wildland fire spread, and 2) smoke transport and dispersion. This is not possible with the current UFS model and thus the project will add new capabilities to the UFS model.
EMC
Jeff McQueen (EMC)
Fanglin Yang (EMC)
National Center for Atmospheric Research
NA22OAR4590514
Fire Weather
Colorado
August 2022 - July 2026
Coupled Models & Techniques, Data Assimilation and Ensembles
implementing a state of the science fire behavior model in the unified forecast system, efforts will be oriented to implement and properly verify the implementation of the fire behavior model, we propose to contribute to mitigating the adverse effects of wildland fires by incorporating a state of the science fire behavior model into the unified forecast system ufs the fire model was developed at ncar during the 1990s and it was enhanced and incorporated into the weather research and forecasting wrf model during the 2010s i e wrf fire more recently 2016 2020 the present team used and enhanced wrf fire as the core predictive capability in the development of the colorado fire prediction system co fps the fire behavior model accounts for fire atmosphere feedbacks it receives winds and humidity from the atmospheric model and uses them to propagate the fire in turn the fire model feeds back to the atmosphere by releasing heat and moisture and produces smoke from the combustion process this fire atmosphere coupling is necessary to properly predict the evolution of wildland fires
Environmental Modeling Center (EMC)
National Center for Atmospheric Research
Efforts will be oriented to implement and properly verify the implementation of the fire behavior model.
3
Improving Subseasonal to Seasonal (S2S) Fire Emissions and Weather Forecasts
Tong
Synoptic
search_activity
2023
Level 3
Produce a new emission ensemble at the S2S timescale
Daniel Tong, Li Zhang, Shan Sun, Yunyao Li, Ziheng Sun, Georg Grell, Ravan Ahmadov, Jeffrey McQueen
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Composition and Chemistry, Data Assimilation and Ensembles
FireWx
Community Multiscale Air Quality (CMAQ) Model, Finite-Volume Cubed-Sphere Dynamical Core (FV3), Global Ensemble Forecast System (GEFS), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
As the frequency and severity of wildfires increase in many regions of the world, large
uncertainties still exist in model representations of key processes of fire weather, limiting our
collective capability to predict and mitigate fire damages at different time scales. Predictability of
fire weather at the sub-seasonal to seasonal (S2S) time scale is particularly desirable for response
management and weather forecast. While exerting immediate effects on local weather, large fires
unfold their full effects on weather and climate through complicated interactions in the Earth
system. Improving S2S wildfire forecasts will enhance the predictability of several NOAA
forecasting products for societal services (firefighting advisory, air quality and health early
warning). The objective of this work is to improve S2S wildfire emission forecasting, a key source
of uncertainty in Fire Weather S2S Forecasting.
There is a large uncertainty in the representation of biomass burning aerosol composition and
its optical properties in the Subseasonal to Seasonal (S2S) aerosol modeling. This comprehensive
fire dataset will help modelers to further adjust the parameterizations and reduce the uncertainty, and ultimately
improve the modeled aerosol effects on incoming solar radiation in the S2S forecast. Specifically,
the proposed project will improve the accuracy of emission inputs to NOAA Unified Forecast
System (UFS) models developed by GSL for the NWS. The current operational forecast models
for weather, S2S and air quality will also be improved with this new set of wildfire emission input,
e.g., Global Ensemble Forecast System (GEFS)-Aerosols, Unified Forecast System (UFS)-Aerosols,
regional Finite Volume Cubed Sphere-Community Multiscale Air Quality (FV3-CMAQ), and
Rapid Refresh Forecast System (RRFS). In addition, this work contributes to advancing emission
science, atmospheric chemistry modeling, and Earth system modeling. It will also benefit the research
communities globally who need fire emission.
NWS/NCEP will be the ultimate recipient. End users, however, goes beyond NCEP, since their
products are used routinely by state/local agencies for air quality forecasting guidance and
advisory.
EMC
Needs new POC;
Formerly McQueen
George Mason University,
NOAA/OAR/GSL,
NOAA/NWS/NCEP/EMC,
University of Colorado Boulder
NA23OAR4590388
NA23OAR4590387
Fire Weather
Colorado, Virginia
August 2023 - July 2026
Artificial Intelligence (AI) / Machine Learning (ML), Atmospheric Composition and Chemistry, Data Assimilation and Ensembles
improving subseasonal to seasonal s2s fire emissions and weather forecasts, produce a new emission ensemble at the s2s timescale, as the frequency and severity of wildfires increase in many regions of the world large uncertainties still exist in model representations of key processes of fire weather limiting our collective capability to predict and mitigate fire damages at different time scales predictability of fire weather at the sub seasonal to seasonal s2s time scale is particularly desirable for response management and weather forecast while exerting immediate effects on local weather large fires unfold their full effects on weather and climate through complicated interactions in the earth system improving s2s wildfire forecasts will enhance the predictability of several noaa forecasting products for societal services firefighting advisory air quality and health early warning the objective of this work is to improve s2s wildfire emission forecasting a key source of uncertainty in fire weather s2s forecasting
Environmental Modeling Center (EMC)
George Mason University, NOAA/OAR/GSL, NOAA/NWS/NCEP/EMC, University of Colorado Boulder
Produce a new emission ensemble at the S2S timescale
9
Subseasonal Predictability of Fire Weather Metrics for Decision Support
Kramer
Synoptic
search_activity
2023
Level 3
Predicting subseasonal fire weather for operational use
Samantha Kramer, Ben Kirtman, Brian Potter
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Mar 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting, Forecast Skill Metrics, Decision Support
FireWx
Unified Forecast System (UFS)
We propose determining the degree of success in predicting subseasonal fire weather
metrics (HDWI, wind, humidity, soil moisture [as a proxy for fuel moisture], precipitation,
and temperature) by using the existing retrospective forecasts from Unified Forecast
System (UFS) Subseasonal to Seasonal (S2S) and Subseasonal Experiment (SubX)
models. We will also evaluate the impact of calibration, statistical downscaling, dynamical
downscaling, and multiple ensemble members on forecast skill (accuracy, reliability, and
uncertainty) and usefulness for decision-making with stakeholders. The proposed project
will produce a database of retrospective S2S fire weather forecasts and address S2S fire
weather prediction tools for use in decision making. We will use existing data from NOAA
S2S projects (UFS, SubX) to assess uncertainty, calibration techniques, downscaling
(statistical [machine learning] and dynamical [EPIC]), and overall forecast skill and
reliability. We will directly engage with stakeholders (USFS, state and federal agencies,
industry) to evaluate community needs and interpret generated forecasts. The applied
research, proof of concept, and evaluation will provide an actionable path forward for
operational S2S fire weather forecasts and may inform additional extreme weather S2S
forecast products (e.g., heat, drought).
Increasing the lead time of fire weather metrics will benefit land managers (e.g., California
Department of Forestry and Fire Protection [CAL FIRE], US Forest Service (USFS),
Southeast Fire Exchange [SEFE]), air managers (e.g., California Air Resources Board [CARB],
U.S. Environmental Protection Agency [EPA]), emergency response personnel,
forecasters (e.g., National Interagency Fire Center [NIFC], National Interagency Coordination
Center [NICC]), and other sectors (e.g., Pacific Gas and Electric [PG&E]) by aiding in
planning and allocation of resources. In addition, longer lead times allow more time to
communicate potential risks to the public and pre-plan risk mitigation strategies for
disadvantaged communities. The forecasts will be generated for the continental U.S.,
and we expect local- to national-level use.
Sonoma Technology Inc.
US Forest Service
University of Miami
NA23OAR4590383
NA23OAR4590384
Fire Weather
California, Florida, Washington
August 2023 - March 2026
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Dynamics and Nesting, Forecast Skill Metrics, Decision Support
subseasonal predictability of fire weather metrics for decision support, predicting subseasonal fire weather for operational use, we propose determining the degree of success in predicting subseasonal fire weather metrics hdwi wind humidity soil moisture [as a proxy for fuel moisture] precipitation and temperature by using the existing retrospective forecasts from unified forecast system ufs subseasonal to seasonal s2s and subseasonal experiment subx models we will also evaluate the impact of calibration statistical downscaling dynamical downscaling and multiple ensemble members on forecast skill accuracy reliability and uncertainty and usefulness for decision making with stakeholders the proposed project will produce a database of retrospective s2s fire weather forecasts and address s2s fire weather prediction tools for use in decision making we will use existing data from noaa s2s projects ufs subx to assess uncertainty calibration techniques downscaling statistical [machine learning] and dynamical [epic] and overall forecast skill and reliability we will directly engage with stakeholders usfs state and federal agencies industry to evaluate community needs and interpret generated forecasts the applied research proof of concept and evaluation will provide an actionable path forward for operational s2s fire weather forecasts and may inform additional extreme weather s2s forecast products e g heat drought
Sonoma Technology Inc., US Forest Service, University of Miami
Predicting subseasonal fire weather for operational use
3
Development and Evaluation of the Warn-on-Forecast System for Smoke (WoFS-Smoke)
Jones
Synoptic
search_activity
2025
Level 3
Fully optimize the WoFS for fire and smoke characteristics
Thomas A. Jones, Patrick Skinner, Joshua Martin, Brian Matilla
Active
Sat Feb 01 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Sun Jan 31 2027 03:00:00 GMT-0500 (Eastern Standard Time)
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Verification & Validation
FireWx
Warn on Forecast System (WoFS), Weather Research and Forecasting Model - Fire (WRF-Fire)
This project is to further develop an ensemble-based forecast system for smoke aerosols generated
from wildfires using a modified version of the National Severe Storms Laboratory (NSSL) Warn-on-Forecast
System (WoFS). This system has proven very effective in generating short term (0-6 hour) forecasts
of high impact weather events during testing in several real-time testbeds such as the Hazardous
Weather Testbed starting in 2017. One feature currently not present within the WoFS version used
in real-time is the ability to forecast smoke. Forecasts of wildfire smoke can be vital to local emergency
managers to determine how best to fight a fire and when/where evacuations might be needed. Lofted
debris, ash, and smoke can reach well into the mid and upper troposphere and smoke aerosols may
persist in the atmosphere for days or weeks before deposition occurs or they break down chemically.
In addition, pyro-cumulus (pyro-Cu) and pyro-cumulonimbus (pyro-Cb) can be formed by buoyant
updrafts generated by the extreme heat, evaporated moisture from burning biomass, and smoke
aerosols acting as cloud condensation nuclei. The rapidly evolving nature of wildfires is one of many
reasons that forecasting these conditions is difficult, but recent advances in wildfire observations and
numerical weather prediction (NWP) models have begun to make progress in this endeavor.
This project will address the following NOAA science priorities for the Fire Weather
objective: FW-2: Smoke transport, evolution, and interaction with meteorology; FW-3: Data
assimilation of atmospheric and terrestrial conditions needed for all stages of fire weather
forecasts, and finally FW-5: Fire weather forecast system testing, verification, and
predictability.
The goal of this research is to create a skillful probabilistic forecasting tool for short-term
smoke forecasts through developing and evaluating new methods for ingesting GOES-R satellite
data into WoFS-Smoke. As such, it satisfies a key program requirement of creating a next
generation coupled wildfire-weather analysis and ensemble-based forecasting system that can lead
to improved forecasts of fire behavior and smoke. Assuming the successful completion of the
proposed objectives, WoFS-Smoke will be able to generate smoke plume forecasts associated with
rapidly evolving fires as well as the ability to forecast the link between wildfires and other high
impact weather. Currently, skillful realtime probabilistic forecasts of these potential impacts are
not readily available to the weather community. The forecasts generated by WoFS-Smoke can be
used by emergency managers, NWS forecasters, media, and other interested parties to help limit
wildfire impacts to both property and human lives. In particular, WoFS-Smoke will be developed
along with the WoFS, and as part of its R2O transition phase, it is planned to train users in its
smoke forecasting abilities in addition to its baseline severe weather forecasting abilities. The goal
is that WoFS-Smoke will become an integral tool in forecasting fire-weather impacts by the end
of the decade.
CIWRO/University of Oklahoma
NA25OARX459C0145
Fire Weather
Oklahoma
February 2025 - January 2027
Atmospheric Composition and Chemistry, Data Assimilation and Ensembles, Numerical Weather Prediction (NWP), Verification & Validation
development and evaluation of the warn on forecast system for smoke wofs smoke, fully optimize the wofs for fire and smoke characteristics, this project is to further develop an ensemble based forecast system for smoke aerosols generated from wildfires using a modified version of the national severe storms laboratory nssl warn on forecast system wofs this system has proven very effective in generating short term 0 6 hour forecasts of high impact weather events during testing in several real time testbeds such as the hazardous weather testbed starting in 2017 one feature currently not present within the wofs version used in real time is the ability to forecast smoke forecasts of wildfire smoke can be vital to local emergency managers to determine how best to fight a fire and when where evacuations might be needed lofted debris ash and smoke can reach well into the mid and upper troposphere and smoke aerosols may persist in the atmosphere for days or weeks before deposition occurs or they break down chemically in addition pyro cumulus pyro cu and pyro cumulonimbus pyro cb can be formed by buoyant updrafts generated by the extreme heat evaporated moisture from burning biomass and smoke aerosols acting as cloud condensation nuclei the rapidly evolving nature of wildfires is one of many reasons that forecasting these conditions is difficult but recent advances in wildfire observations and numerical weather prediction nwp models have begun to make progress in this endeavor this project will address the following noaa science priorities for the fire weather objective fw 2 smoke transport evolution and interaction with meteorology fw 3 data assimilation of atmospheric and terrestrial conditions needed for all stages of fire weather forecasts and finally fw 5 fire weather forecast system testing verification and predictability
CIWRO/University of Oklahoma
Fully optimize the WoFS for fire and smoke characteristics
2
Improving the aerosol representation and coupling to microphysics in the 3-km storm-scale Rapid Refresh Forecast System (RRFS)
Li
Synoptic
search_activity
2025
Level 3
Use the prognostic wildfire smoke, dust, sea salt, and anthropogenic aerosol emissions to accurately represent aerosols in the Thompson-Eidhammer microphysics scheme.
Haiqin Li, Anders Jensen, Ravan Ahmadov
Active
Sat Feb 01 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Sun Jan 31 2027 03:00:00 GMT-0500 (Eastern Standard Time)
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Verification & Validation
FireWx
Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
The primary planned products/outputs are an improved aerosol representation for
microphysics and microphysics indirect feedback, which will be consistent with aerosol direct
feedback for radiation. Cloud drop number and size depend, in part, on aerosol properties, and
these detailed cloud properties ultimately impact cloud albedo and energy budget, the hydrometeors
content, cloud cover and lifetime, and they can also further suppress or enhance precipitation.
This work is expected to result in improved aerosol-aware microphysics and understanding of the
impact of smoke on weather forecasts in the RRFS over CONUS and Alaska domains. Other physics, such
as cumulus convection will also be updated to fit this proposed aerosol representation for
microphysics. Retrospective runs and real-time parallel runs will be executed for verification. The
subroutines of the improved aerosol representation and modified physics will be committed to the
UFS community Github code repository. We will report the project progress at scientific conferences and meetings.
We will also publish scientific journal papers with the results and outputs from this project. The final goal of this proposal is to
implement an improved aerosol representation and microphysics indirect feedback into the next
generation RRFS v2 operational forecast system.
In the proposed modeling framework, the warm-rain/cold-rain processes of the
microphysics will be aware of the wildfire and dust events, and the physics parameterization
community will benefit from this development. This will allow for the ability to run retrospective
and real-time forecasts and better understand cloud-precipitation-aerosol-radiation impacts. It is
also helpful to quantify the impact of smoke/dust on clouds. The 3-km storm-scale weather
forecasting in RRFS over CONUS and Alaska will benefit from this more realistic aerosol
representation for microphysics, and the smoke/dust air quality forecast could also be further
improved with the improved meteorology of the numerical weather forecasting. We will aim to include
the product of this proposal into the next generation RRFS v2 operational implementation operated
by NOAA/EMC and NOAA/NCO, and the local weather offices will also be benefited.
The software and code from this proposal will be available in the UFS community GitHub code
repository. All the proposed code development in this project will follow the CCPP standards and should
should also benefit the CCPP and UFS community researchers, students and public.
CIRES/University of Colorado in Boulder
NOAA/OAR/GSL
NA25OARX459C0007
Fire Weather
Colorado
February 2025 - January 2027
Atmospheric Composition and Chemistry, Atmospheric Physics, Coupled Models & Techniques, Data Assimilation and Ensembles, Verification & Validation
improving the aerosol representation and coupling to microphysics in the 3 km storm scale rapid refresh forecast system rrfs, use the prognostic wildfire smoke dust sea salt and anthropogenic aerosol emissions to accurately represent aerosols in the thompson eidhammer microphysics scheme, the primary planned products outputs are an improved aerosol representation for microphysics and microphysics indirect feedback which will be consistent with aerosol direct feedback for radiation cloud drop number and size depend in part on aerosol properties and these detailed cloud properties ultimately impact cloud albedo and energy budget the hydrometeors content cloud cover and lifetime and they can also further suppress or enhance precipitation this work is expected to result in improved aerosol aware microphysics and understanding of the impact of smoke on weather forecasts in the rrfs over conus and alaska domains other physics such as cumulus convection will also be updated to fit this proposed aerosol representation for microphysics retrospective runs and real time parallel runs will be executed for verification the subroutines of the improved aerosol representation and modified physics will be committed to the ufs community github code repository we will report the project progress at scientific conferences and meetings we will also publish scientific journal papers with the results and outputs from this project the final goal of this proposal is to implement an improved aerosol representation and microphysics indirect feedback into the next generation rrfs v2 operational forecast system
CIRES/University of Colorado in Boulder, NOAA/OAR/GSL
Use the prognostic wildfire smoke, dust, sea salt, and anthropogenic aerosol emissions to accurately represent aerosols in the Thompson-Eidhammer microphysics scheme.
2
Towards improved representations of wildfire-ABL interactions in the UFS modeling suite:
parameterization development, emissions modeling, and air quality impact assessment
Ghannam
Synoptic
search_activity
2025
Level 3
To provide an accurate modeling system for NOAA forecasters, forest/emergency response managers, and local and state agencies to address wildfire risks and impacts.
Khaled Ghannam, Daniel Tong, Fanglin Yang
Active
Sat Feb 01 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Sun Jan 31 2027 03:00:00 GMT-0500 (Eastern Standard Time)
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Dynamics and Nesting, Parameterization, Satellite & Remote Sensing Technologies, Verification & Validation
FireWx
Community Multiscale Air Quality (CMAQ) Model, NOAA's National Air Quality Forecasting Capability (NAQFC), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
This project is to enhance RRFS's short-term wildfire forecasting capability by improving RRFS's
representation of fire-ABL interactions and incorporate two-way feedbacks between fire weather
and smoke emissions. Specific tasks are 1) implementation of a new parameterization in UFS/RRFS
that couples fire and meteorology through convective heat/moisture exchange to account for fire-
induced secondary circulations in the AB; 2) improved estimates of fire emission rates from NOAA's
weather satellite products (RAVE) and influence of new fire-ABL scheme on modeled plume rise
dynamics; and 3) simulating the evolution of an active fire to evaluate the new parameterization in
UFS/RRFS coupled with the Community Multiscale Air Quality modeling system (UFS/RRFS-CMAQ)
against intensively instrumented fire episodes over the western US (FIREX-AQ 2019 campaign).
Project products include an enhanced RRFS model version with higher fidelity in forecasting
fire-weather evolution (Tasks 1-2) and for evaluating impacts on air quality (Task 3), and the
production of publicly accessible datasets from simulation output for model diagnosis,
testing, and validation.
This project will provide NOAA National Weather Service and the Office of Oceanic and
Atmospheric Research with an improved RRFS model that accurately simulates short-term fire-
weather evolution to better prepare forest/emergency managers, local and state agencies, and the
public to address wildfire management and impacts. The new high fidelity RRFS model version
can also serve as a testbed for Medium-Range Weather (MRW) and sub-seasonal-to-seasonal
(S2S) applications in the UFS modeling suite. When coupled with CMAQ, the model provides
improved air quality forecasts of wildfire emissions and impacts.
The project is also intended to provide the scientific community with an open-source code
repository that advances the scientific rigor of wildfire-ABL modeling, provide open-access data
for benchmarking measurements and simulations, and for the development of model diagnostics.
Northeastern University, George Mason University, EMC
NA25OARX459C0013
NA25OARX459C0006
Fire Weather
Massachusetts, Virginia
February 2025 - January 2027
Atmospheric Composition and Chemistry, Coupled Models & Techniques, Dynamics and Nesting, Parameterization, Satellite & Remote Sensing Technologies, Verification & Validation
towards improved representations of wildfire abl interactions in the ufs modeling suite parameterization development emissions modeling and air quality impact assessment, to provide an accurate modeling system for noaa forecasters forest emergency response managers and local and state agencies to address wildfire risks and impacts, this project is to enhance rrfs's short term wildfire forecasting capability by improving rrfs's representation of fire abl interactions and incorporate two way feedbacks between fire weather and smoke emissions specific tasks are 1 implementation of a new parameterization in ufs rrfs that couples fire and meteorology through convective heat moisture exchange to account for fire induced secondary circulations in the ab 2 improved estimates of fire emission rates from noaa's weather satellite products rave and influence of new fire abl scheme on modeled plume rise dynamics and 3 simulating the evolution of an active fire to evaluate the new parameterization in ufs rrfs coupled with the community multiscale air quality modeling system ufs rrfs cmaq against intensively instrumented fire episodes over the western us firex aq 2019 campaign project products include an enhanced rrfs model version with higher fidelity in forecasting fire weather evolution tasks 1 2 and for evaluating impacts on air quality task 3 and the production of publicly accessible datasets from simulation output for model diagnosis testing and validation
Northeastern University, George Mason University, EMC
To provide an accurate modeling system for NOAA forecasters, forest/emergency response managers, and local and state agencies to address wildfire risks and impacts.
0
Developing Novel, Robust Hybrid Machine-Learning Postprocessing Schemes for Improving Skills of UFS Probabilistic Forecasts of Precipitation Amounts and Types
Zhang
Synoptic
search_activity
2023
Level 3
To introduce innovative machine learning (ML) algorithms to the realm of ensemble postprocessing.
Yu Zhang, Tom Hamill, Bruce Veenhuis, Nachiketa Acharya, Jeffrey Whitaker, Phil Schafer
Mon Aug 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting
Innovation
Unified Forecast System (UFS)
The project seeks to introduce innovative machine learning (ML) algorithms to the realm
of ensemble postprocessing; and more specifically, it explores the possibility of fusing the ML
with traditional parametric approach in producing skillful probabilistic guidance of precipitation
amounts and using ML alone for generating probabilistic precipitation types. The initiative builds
on past efforts by the project team that led to the development of the hybrid Artificial Neural
Network-Censored Shifted Gamma Distribution (ANN-CSGD) scheme at UT Arlington, and the
Regularized Bayesian Classifier (RBC) at PSL. The new schemes to be developed and tested will
include a novel LSTM-CSGD hybrid processor for precipitation forecast that uses advanced
recurrent neural networks to account for time-dependent impacts of meteorological variables, and
a tailored multi-class ANN algorithms for precipitation type forecasts. These innovations have the
potential of enhancing the skills of PQPF for thresholds beyond 3-5in/day for lead times out to day
8, and expanding the forecasted wintertime precipitation types and improving their accuracy and
specificity for day 1-7.
The proposed initiative will be carried out in collaboration with NOAA PSL, NWS WPC/MDL,
and IBM, with the latter two being potential end users. It will yield codes, documents and peer-
reviewed publications that will be shared with collaborators, thus allowing for further evaluations
and paving the way for potential transition to operation. The benefits will be multifold. First, the
algorithm for producing PQPFs addresses limited skills of current ensemble postprocessing
schemes in the National Blend and WPC in forecasting heavy-to-extreme precipitation and
precipitation types.
These innovations have the potential of enhancing the skills of PQPF for thresholds beyond 3-5in/day for lead times out to day 8, and expanding the forecasted wintertime precipitation types and improving their accuracy and specificity for day 1-7.
The anticipated impacts/benefits of the undertaking are six-fold:
1. The project will yield novel postprocessing algorithms of UFS that could address critical
gaps of extant operational schemes in predicting extreme and enhance the skills of NWP
forecasts in predicting the chance of extreme precipitation and wintertime precipitation
types over the short and medium range.
2. The collaborative evaluation of first-guess outlook products through hindcast experiments
that involve newly available GEFSv13 reforecasts will help the research team as well as
collaborators in NOAA and IBM in determining the potential of the schemes in effectively
delineating areas with hazardous weather conditions in the UFS era.
3. The scalability experiments, to be performed on different ensemble forecasts and analysis
products, will inform the potential of the existing as well as newly developed schemes in
adapting to larger ensemble from heterogeneous systems and for producing high-resolution
guidance products; and this will help with planning for computational resources required
to effectively operate these schemes within UFS.
4. The availability of the implementations of these schemes to collaborators will allow for
future, quasi-operational tests, and thereby paves the way for operational adoption by NWS
and commercial entities. In particular, WPC produces ERO and Winter Weather Outlooks
(WWO) to inform the public of the potential for hazardous flashing flooding and winter
weather conditions. The prototype schemes will be evaluated to determine their utility of
in informing potential enhancements of these products with involvement of WPC
collaborator.
5. The project will help train future workforces, and improve weather literacy among
engineers, water managers, and general public. In particular, graduate students from the
UTA research team will gain first-hand knowledge of the forecast processing. Components of the project will be featured in a course taught by the PI titled "Hydrometeorology and
Remote Sensing" by Y2, and the topic will help graduate students understand ways of
measuring forecast uncertainty. The project will use the help of summer student interns
with Hispanic origin and will motivate minority students to pursue advanced careers in
STEM.
6. The project will contribute to a concurrent Forecast Informed Reservoir Operation (FIRO)
initiative for the State of Texas led by the PI. Through quarterly meetings and annual
workshop series, the project team will demonstrate the new postprocessing scheme, its
products, and potential skill gains to regional collaborators and stakeholders to spur
interests in the adoption of probabilistic forecasts in decision making.
NWS WPC/MDL
IBM
University of Texas at Arlington
NOAA/NWS/NCEP/WPC
NOAA/OAR/PSL
NOAA/NWS/MDL
NA23OAR4590378
Relevant to All Hazards
Texas
September 2023 - August 2026
Artificial Intelligence (AI) / Machine Learning (ML), Data Assimilation and Ensembles, Model & Forecast Guidance, Post-processing, Probabilistic Weather Forecasting
developing novel robust hybrid machine learning postprocessing schemes for improving skills of ufs probabilistic forecasts of precipitation amounts and types, to introduce innovative machine learning ml algorithms to the realm of ensemble postprocessing, the project seeks to introduce innovative machine learning ml algorithms to the realm of ensemble postprocessing and more specifically it explores the possibility of fusing the ml with traditional parametric approach in producing skillful probabilistic guidance of precipitation amounts and using ml alone for generating probabilistic precipitation types the initiative builds on past efforts by the project team that led to the development of the hybrid artificial neural network censored shifted gamma distribution ann csgd scheme at ut arlington and the regularized bayesian classifier rbc at psl the new schemes to be developed and tested will include a novel lstm csgd hybrid processor for precipitation forecast that uses advanced recurrent neural networks to account for time dependent impacts of meteorological variables and a tailored multi class ann algorithms for precipitation type forecasts these innovations have the potential of enhancing the skills of pqpf for thresholds beyond 3 5in day for lead times out to day 8 and expanding the forecasted wintertime precipitation types and improving their accuracy and specificity for day 1 7 the proposed initiative will be carried out in collaboration with noaa psl nws wpc mdl and ibm with the latter two being potential end users it will yield codes documents and peer reviewed publications that will be shared with collaborators thus allowing for further evaluations and paving the way for potential transition to operation the benefits will be multifold first the algorithm for producing pqpfs addresses limited skills of current ensemble postprocessing schemes in the national blend and wpc in forecasting heavy to extreme precipitation and precipitation types
University of Texas at Arlington, NOAA/NWS/NCEP/WPC, NOAA/OAR/PSL, NOAA/NWS/MDL
To introduce innovative machine learning (ML) algorithms to the realm of ensemble postprocessing.
0
Unification of Boundary-Layer and Shallow Cumulus Mass Fluxes under Vertical Wind Shear for the Unified Forecast System Models
Chen
Synoptic
search_activity
2023
Level 3
Plan is to accurately represent the turbulent weather processes
Xiaomin Chen, George H Bryan, Andrew Hazelton
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Hurricane Analysis and Forecast System (HAFS), Unified Forecast System (UFS)
This project aims to develop a generalized eddy-diffusivity mass-flux planetary boundary layer (EDMF PBL) scheme that unifies the mass fluxes (MF) in the PBL and shallow cumulus schemes and includes the damping effects of vertical wind shear (VWS) on MF. The main advantage of this generalized EDMF PBL scheme is its broad application to various types of boundary layers, including clear boundary layers, shallow cumulus boundary layers, and even hurricane boundary layers. The MF component in the PBL scheme will decide whether the buoyant plumes in the subcloud layer turn into cloud core updrafts. There is no need to call a separate cumulus scheme until deep cumulus is triggered.
The proposed generalized EDMF PBL scheme will have several tangible benefits to the broader
UFS community.
First, the inclusion of a scale-adaptive feature under vertical wind shear within the generalized
EDMF PBL scheme can be seamlessly applied to different-resolution UFS models, including both
global and regional models. Second, the generalized EDMF PBL scheme can improve UFS models' ability to represent
boundary layer structure in non-tropical cyclone(TC) conditions, which can help address two forecast issues (5a and
1c) that were highlighted in the 2020-21 NOAA Office of Science and Technology Integration
Forecasters Workshops report. Third, the better-represented turbulent mixing in high-wind conditions will lead to improved
forecasts of TC PBL structure (including inflow strength and depth) and wind radii (e.g.,
Cangialosi and Landseer 2016) using HAFS, a nested version of System for High-resolution prediction on Earth-to-Local Domains (Shield) for TC prediction (T-SHiELD), and other UFS models. Four, the proposed generalized EDMF PBL scheme will boost the benefits of data assimilation (DA) and ensemble forecasting. Last, the improved short- and medium-range weather forecasts from UFS models will benefit downstream forecast users, including the National Hurricane Center (NHC), Weather Prediction Center (WPC), and Weather Forecast Offices (WFO). Additionally, with better forecasts, the
public will have more confidence in official forecasts when high-impact weather systems threaten, which will lead to better responsiveness and help save lives and property (Martinez 2020).
EMC
Zhan Zhang (EMC)
University of Alabama in Huntsville
National Center for Atmospheric Research
CIMAS/University of Miami
NA23OAR4590380
Tropical Cyclones
Alabama, Colorado, Florida
August 2023 - July 2026
High-Performance Computing (HPC)
unification of boundary layer and shallow cumulus mass fluxes under vertical wind shear for the unified forecast system models, plan is to accurately represent the turbulent weather processes, this project aims to develop a generalized eddy diffusivity mass flux planetary boundary layer edmf pbl scheme that unifies the mass fluxes mf in the pbl and shallow cumulus schemes and includes the damping effects of vertical wind shear vws on mf the main advantage of this generalized edmf pbl scheme is its broad application to various types of boundary layers including clear boundary layers shallow cumulus boundary layers and even hurricane boundary layers the mf component in the pbl scheme will decide whether the buoyant plumes in the subcloud layer turn into cloud core updrafts there is no need to call a separate cumulus scheme until deep cumulus is triggered
Environmental Modeling Center (EMC)
University of Alabama in Huntsville, National Center for Atmospheric Research, CIMAS/University of Miami
Plan is to accurately represent the turbulent weather processes
6
Developing enhanced diagnostic output for the UFS: Increasing information extraction with minimal expense
Correia
Synoptic
search_activity
2023
Level 3
Providing better insight into variable behavior on sub-IO timescales
James Correia, Yongming Wang, Xuguang Wang
Active
Tue Aug 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Mon Aug 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Innovation
Unified Forecast System (UFS)
The overarching goals of this project are to increase information content coming
out of the models and limit the number of output parameters. By providing better insight into
variable behavior on sub-IO timescales we hope to empower both developers and forecasters with
information that they can use to improve models and thus provide better forecasts. More broadly,
community members could benefit from better understanding of model behavior during
development, better use of model data by forecasters to focus on real model extremes as opposed to
numerical artifacts, and better monitoring of extreme model behavior once deployed operationally.
We seek to benefit the entire UFS community from forecasters, model developers, and
National Centers (from data storage and dissemination, to research and development, to phenomena
specific hazard areas like severe storms and flash flooding).
This work can help model developers increase the chance of successful R&D outcomes.
Having a baseline understanding of model tendencies is key for future developments in data
assimilation, extreme event forecasting, and model construction/tuning.This provides developers
and forecasters the opportunity to evaluate sub-hourly output for the detection of extremes in
forecasting mode, and understanding model problems during development, implementation, testing,
and Operational deployment and monitoring.
A net benefit to Operational Centers is that the model IO and/or data storage may be
reduced. There are a number of sparse, phenomena specific output Hourly Maximum variables that
could benefit from time step information - Updraft Helicity, Winds, Reflectivity, Temperature,
Vorticity, Hail Size, etc. Thus more information could be retained for other uses such as machine
learning.
CIRES/University of Colorado Boulder
University of Oklahoma
NA23OAR4590373,
NA23OAR4590374
Relevant to All Hazards
Colorado, Oklahoma
August 2023 - August 2026
developing enhanced diagnostic output for the ufs increasing information extraction with minimal expense, providing better insight into variable behavior on sub io timescales, the overarching goals of this project are to increase information content coming out of the models and limit the number of output parameters by providing better insight into variable behavior on sub io timescales we hope to empower both developers and forecasters with information that they can use to improve models and thus provide better forecasts more broadly community members could benefit from better understanding of model behavior during development better use of model data by forecasters to focus on real model extremes as opposed to numerical artifacts and better monitoring of extreme model behavior once deployed operationally
CIRES/University of Colorado Boulder, University of Oklahoma
Providing better insight into variable behavior on sub-IO timescales
0
Experimental Design of the Prototype RRFS-AR
Tallapragada
Synoptic
check_circle
2023
Level 3
to advance the design and development of the prototype Rapid Refresh Forecast System-Atmospheric River (RRFS-AR) within the Unified Forecast System (UFS) framework
Vijay Tallapragada
Complete
Thu Jun 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Numerical Weather Prediction (NWP), Water in the West
AR
Hurricane Analysis and Forecast System (HAFS), Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
EMC
EMC
WPOFY23-AR-EMC
Water Extremes, Winter Weather
Maryland
June 2023 - September 2024
Numerical Weather Prediction (NWP), Water in the West
experimental design of the prototype rrfs ar, to advance the design and development of the prototype rapid refresh forecast system atmospheric river rrfs ar within the unified forecast system ufs framework
Environmental Modeling Center (EMC)
EMC
to advance the design and development of the prototype Rapid Refresh Forecast System-Atmospheric River (RRFS-AR) within the Unified Forecast System (UFS) framework
0
Testbed Evaluation of the Prototype RRFS-AR
Nelson
Synoptic
check_circle
2023
Level 3
support an interactive retrospective evaluation of the performance of the RRFS-AR compared to other operational guidance (GFS) during the 2023 AR season
James Nelson, David Novak
Complete
Thu Jun 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat Aug 31 2024 03:00:00 GMT-0400 (Eastern Daylight Time)
Water in the West
AR
Rapid Refresh Forecast System (RRFS), Unified Forecast System (UFS)
advance the prototype Rapid Refresh Forecast System-Atmospheric River (RRFS-AR)
WPC
WPOFY23-AR-WPC
Water Extremes, Winter Weather
Maryland
June 2023 - August 2024
Water in the West
testbed evaluation of the prototype rrfs ar, support an interactive retrospective evaluation of the performance of the rrfs ar compared to other operational guidance gfs during the 2023 ar season, advance the prototype rapid refresh forecast system atmospheric river rrfs ar
WPC
support an interactive retrospective evaluation of the performance of the RRFS-AR compared to other operational guidance (GFS) during the 2023 AR season
0
Improving the process representations and predictive performance of the next generation Water Resources Modeling framework in cold regions: The Yukon River basin
Kumar
Synoptic
check_circle
2023
Level 3
Leverage expertise in modeling and predicting hydrologic processes in both Canada and the USA to improve the process representations and predictive performance of the Nextgen framework in cold regions
Mukesh Kumar, Steve Burian
Complete
Thu Jun 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat May 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Water in the West
AR
National Water Model (NWM)
University of Alabama, University of Saskatchewan, Colorado School of Mines
NA22NWS4320003-T3-01S037
Water Extremes, Winter Weather
Alabama, Colorado
June 2023 - May 2025
Water in the West
improving the process representations and predictive performance of the next generation water resources modeling framework in cold regions the yukon river basin, leverage expertise in modeling and predicting hydrologic processes in both canada and the usa to improve the process representations and predictive performance of the nextgen framework in cold regions
University of Alabama, University of Saskatchewan, Colorado School of Mines
Leverage expertise in modeling and predicting hydrologic processes in both Canada and the USA to improve the process representations and predictive performance of the Nextgen framework in cold regions
0
Deep Learning Ensemble Predictions of Forcings for Hydrological Modeling
Burian
Synoptic
check_circle
2023
Level 3
Develop, test, and implement novel, computationally efficient Deep Learning based postprocessing schemes to improve the Meteorological Ensemble Forecast Processor probabilistic forecasts of precipitation and temperature, which will result in significantly more reliable and skillful streamflow operational predictions for the RFCs
Steve Burian, Luca Delle Monache
Complete
Thu Jun 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat May 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Water in the West
AR
National Water Model (NWM)
University of Alabama, University of California San Diego
NA22NWS4320003-T3-01S034
Water Extremes, Winter Weather
Alabama, California
June 2023 - May 2025
Water in the West
deep learning ensemble predictions of forcings for hydrological modeling, develop test and implement novel computationally efficient deep learning based postprocessing schemes to improve the meteorological ensemble forecast processor probabilistic forecasts of precipitation and temperature which will result in significantly more reliable and skillful streamflow operational predictions for the rfcs
University of Alabama, University of California San Diego
Develop, test, and implement novel, computationally efficient Deep Learning based postprocessing schemes to improve the Meteorological Ensemble Forecast Processor probabilistic forecasts of precipitation and temperature, which will result in significantly more reliable and skillful streamflow
The project will focus on snow environments across CONUS (Sierra Nevada, Rockies, and Green Mountains), where we will build on existing CIROH projects to create a standardized workflow for evaluating snow and hydrological modeling prediction benefits from including more physically explicit representations of snow processes, and the application of data assimilation methods
Ryan Johnson
Complete
Thu Jun 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Sat May 31 2025 03:00:00 GMT-0400 (Eastern Daylight Time)
Water in the West
AR
National Water Model (NWM)
University of Alabama, Colorado School of Mines, University of Utah, University of Vermont
NA22NWS4320003-T3-01S055
Water Extremes, Winter Weather
Alabama, Colorado, Utah, Vermont
June 2023 - May 2025
Water in the West
advancing conus scale operational snow modeling capabilities, the project will focus on snow environments across conus sierra nevada rockies and green mountains where we will build on existing ciroh projects to create a standardized workflow for evaluating snow and hydrological modeling prediction benefits from including more physically explicit representations of snow processes and the application of data assimilation methods
University of Alabama, Colorado School of Mines, University of Utah, University of Vermont
The project will focus on snow environments across CONUS (Sierra Nevada, Rockies, and Green Mountains), where we will build on existing CIROH projects to create a standardized workflow for evaluating snow and hydrological modeling prediction
0
Proposal to Advance Atmospheric River Research and Prediction through a Partnership Between CW3E at Scripps institution of Oceanography and NCEP/EMC and WPC on Atmospheric River Reconnaissance
Ralph
Synoptic
check_circle
2023
Level 3
to plan and conduct an interagency/international program of airborne reconnaissance and modeling/data assimilation that is improving the understanding and prediction of landfalling atmospheric rivers on the US West Coast
Martin Ralph, Vijay Tallapragada
Complete
Sun Oct 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
Tue Dec 31 2024 03:00:00 GMT-0500 (Eastern Standard Time)
Water in the West
AR
CW3E
NA20OAR4320278
Water Extremes, Winter Weather
California
October 2023 - December 2024
Water in the West
proposal to advance atmospheric river research and prediction through a partnership between cw3e at scripps institution of oceanography and ncep emc and wpc on atmospheric river reconnaissance, to plan and conduct an interagency international program of airborne reconnaissance and modeling data assimilation that is improving the understanding and prediction of landfalling atmospheric rivers on the us west coast
CW3E
to plan and conduct an interagency/international program of airborne reconnaissance and modeling/data assimilation that is improving the understanding and prediction of landfalling atmospheric rivers on the US West Coast
1
Water in the West FY23 Omnibus AR-focused Activity
Mahoney
Synoptic
check_circle
2023
Level 3
Develop a base RRFS-AR capability
Robin Webb Jennifer Mahoney
Complete
Thu Jun 01 2023 03:00:00 GMT-0400 (Eastern Daylight Time)
developing a complete Unified Forecast System (UFS) based high-resolution regional analysis and forecast system for Atmospheric Rivers (AR-AFS)
EMC
WPOFY24-AR-EMC
Water Extremes, Winter Weather
Maryland
October 2024 - September 2025
Water in the West
advance the development of the prototype analysis and forecast system for atmospheric rivers, outputs from this work will focus on advancing ar afs with a goal of eventual transition to operations, developing a complete unified forecast system ufs based high resolution regional analysis and forecast system for atmospheric rivers ar afs
EMC
Outputs from this work will focus on advancing AR-AFS with a goal of eventual transition to operations
0
HMT Atmospheric Rivers Experiments
Novak
Synoptic
search_activity
2025
Level 3
This work will build on the 2023-2024 funded work to implement a verification scheme dedicated to the Atmospheric River forecast problem.
Sat Oct 31 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Water in the West
AR
Unified Forecast System (UFS)
Principally, this resource will support an interactive retrospective evaluation of the performance of the the coupled global / regional nest Atmospheric River (AR) Forecast System (UFS-AR) model output compared to other operational guidance (i.e. GFS, ECMWF, etc.) and experimental guidance (i.e. GraphCast, West-WRF, etc.).
WPC
WPOFY24-AR-WPC
Water Extremes, Winter Weather
Maryland
April 2025 - October 2026
Water in the West
hmt atmospheric rivers experiments, this work will build on the 2023 2024 funded work to implement a verification scheme dedicated to the atmospheric river forecast problem, principally this resource will support an interactive retrospective evaluation of the performance of the the coupled global regional nest atmospheric river ar forecast system ufs ar model output compared to other operational guidance i e gfs ecmwf etc and experimental guidance i e graphcast west wrf etc
WPC
This work will build on the 2023-2024 funded work to implement a verification scheme dedicated to the Atmospheric River forecast problem.
0
Advancing the Forecasting and Prediction of Atmospheric Rivers and Their Impacts: Data Assimilation, Reanalyses, Fully Coupled Modeling, and S2S Applications
Webb
Synoptic
search_activity
2024
Level 3
The resulting prototype atmospheric river forecast system is anticipated to reduce errors in the landfall position of Atmospheric Rivers, to improve the accuracy of QPF of land-falling Atmospheric Rivers, to reduce errors in the model intensity of land-falling Atmospheric Rivers, to reduce forecast errors in snow level for land-falling Atmospheric Rivers, and improve the prediction of snow accumulation.
Wed Dec 31 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Water in the West
AR
Unified Forecast System (UFS)
The research and development of innovative modeling and forecast system approaches will better position NOAA to optimize the use of AR Recon dropsonde data and other innovative observations, to apply investments in weather and water modeling development to deliver skillful AR forecasts and prediction of hydrologic impacts, and to develop actionable seasonal outlooks of AR activity.
PSL
WPOFY24-AR-PSL-01
Water Extremes, Winter Weather
Colorado
July 2024 - December 2025
Water in the West
advancing the forecasting and prediction of atmospheric rivers and their impacts data assimilation reanalyses fully coupled modeling and s2s applications, the resulting prototype atmospheric river forecast system is anticipated to reduce errors in the landfall position of atmospheric rivers to improve the accuracy of qpf of land falling atmospheric rivers to reduce errors in the model intensity of land falling atmospheric rivers to reduce forecast errors in snow level for land falling atmospheric rivers and improve the prediction of snow accumulation, the research and development of innovative modeling and forecast system approaches will better position noaa to optimize the use of ar recon dropsonde data and other innovative observations to apply investments in weather and water modeling development to deliver skillful ar forecasts and prediction of hydrologic impacts and to develop actionable seasonal outlooks of ar activity
PSL
The resulting prototype atmospheric river forecast system is anticipated to reduce errors in the landfall position of Atmospheric Rivers, to improve the accuracy of QPF of land-falling Atmospheric Rivers, to reduce errors in the model
0
Applying Statistical and Artificial Intelligence Methods to Deliver New Meteorological Forecasts for the Next Generation of Ensemble Streamflow Prediction Capabilities
Dixon
Synoptic
search_activity
2024
Level 3
develop and deliver an enhanced meteorological ensemble forecast processing package that can drive more skillful probabilistic streamflow predictions in the HEFS and NextGen platforms using computationally efficient and flexible algorithms
applying statistical and artificial intelligence methods to deliver new meteorological forecasts for the next generation of ensemble streamflow prediction capabilities, develop and deliver an enhanced meteorological ensemble forecast processing package that can drive more skillful probabilistic streamflow predictions in the hefs and nextgen platforms using computationally efficient and flexible algorithms, improve nws ensemble streamflow forecasting capabilities
University of California San Diego
develop and deliver an enhanced meteorological ensemble forecast processing package that can drive more skillful probabilistic streamflow predictions in the HEFS and NextGen platforms using computationally efficient and flexible algorithms
0
Convolutional neural networks for precipitation phase determination to enhance western US snowpack evolution within NextGen
Strong
Synoptic
search_activity
2024
Level 3
The project will develop additional CNNs for temperature lapse rate and precipitation phase determination, and integrate them into the NextGen framework to improve hydroclimate forcings for precipitation phase and assess impacts on snowpack evolution
Tue Jun 30 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Numerical Weather Prediction (NWP), Water in the West
AR
National Water Model (NWM)
Within the NWM framework, we anticipate that improvements in snow water storage will enhance medium to long-range forecasting skill to encourage the use of the NWM for water supply forecasting applications.
University of Utah
NA22NWS4320003-T3-01S079
Water Extremes, Winter Weather
Utah
July 2024 - June 2026
Artificial Intelligence (AI) / Machine Learning (ML), Numerical Weather Prediction (NWP), Water in the West
convolutional neural networks for precipitation phase determination to enhance western us snowpack evolution within nextgen, the project will develop additional cnns for temperature lapse rate and precipitation phase determination and integrate them into the nextgen framework to improve hydroclimate forcings for precipitation phase and assess impacts on snowpack evolution, within the nwm framework we anticipate that improvements in snow water storage will enhance medium to long range forecasting skill to encourage the use of the nwm for water supply forecasting applications
University of Utah
The project will develop additional CNNs for temperature lapse rate and precipitation phase determination, and integrate them into the NextGen framework to improve hydroclimate forcings for precipitation phase and assess impacts on snowpack evolution
0
Gridded precipitation post-processing for Ensemble Streamflow Forecasts to improve the prediction of anomalously high precipitation and inflow
Park
Synoptic
search_activity
2024
Level 3
This project will then allow the assessment of the improved streamflow forecast skills with emphasis on extreme events of interest to reservoir operators and emergency managers using the Tools for Exploratory Evaluation in Hydrologic Research (TEEHR)
Tue Jun 30 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Model & Forecast Guidance, Water in the West
AR
The proposed project aims to develop an enhanced ensemble forecast system in HEFS that can be implemented in the NextGen framework if viable.
RTI International
NA22NWS4320003-T3-01S081
Water Extremes, Winter Weather
Colorado
July 2024 - June 2026
Model & Forecast Guidance, Water in the West
gridded precipitation post processing for ensemble streamflow forecasts to improve the prediction of anomalously high precipitation and inflow, this project will then allow the assessment of the improved streamflow forecast skills with emphasis on extreme events of interest to reservoir operators and emergency managers using the tools for exploratory evaluation in hydrologic research teehr, the proposed project aims to develop an enhanced ensemble forecast system in hefs that can be implemented in the nextgen framework if viable
RTI International
This project will then allow the assessment of the improved streamflow forecast skills with emphasis on extreme events of interest to reservoir operators and emergency managers using the Tools for Exploratory Evaluation in Hydrologic Research
0
Near-Real-Time Monitoring of Key Reservoir Variables by Integrating Wide-Swath SWOT Altimetry and Deep Learning Techniques
Liu
Synoptic
search_activity
2024
Level 3
Use satellite and other data to develop an innovative hybrid CNN-LSTM deep learning model
Tue Jun 30 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Artificial Intelligence (AI) / Machine Learning (ML), Numerical Weather Prediction (NWP), Satellite & Remote Sensing Technologies, Water in the West
AR
Our overarching goal is to develop and implement cutting-edge algorithms, software tools, and corresponding high-temporal-resolution estimates of reservoir water levels, surface areas, storage volumes, inflows, and outflows in a near-real-time framework.
University of Alabama
NA22NWS4320003-T3-01S062
Water Extremes, Winter Weather
Alabama
July 2024 - June 2026
Artificial Intelligence (AI) / Machine Learning (ML), Numerical Weather Prediction (NWP), Satellite & Remote Sensing Technologies, Water in the West
near real time monitoring of key reservoir variables by integrating wide swath swot altimetry and deep learning techniques, use satellite and other data to develop an innovative hybrid cnn lstm deep learning model, our overarching goal is to develop and implement cutting edge algorithms software tools and corresponding high temporal resolution estimates of reservoir water levels surface areas storage volumes inflows and outflows in a near real time framework
University of Alabama
Use satellite and other data to develop an innovative hybrid CNN-LSTM deep learning model
0
Research to Operations Coordination
Womble
Synoptic
search_activity
2024
Level 3
The research team will collaborate with CIROH Principal Investigators (PIs) and engage with representatives from the Office of Water Prediction (OWP) to foster further integration and alignment between research and operational entities involved in CIROH and best position research products from CIROH to be seamlessly transitioned to OWP.
Tue Jun 30 2026 03:00:00 GMT-0400 (Eastern Daylight Time)
Water in the West
AR
establishment of a comprehensive program aimed at bridging theory to practice and supporting CIROH's objective of enabling and supporting the transition of research into operations (R2O) within the National Oceanic and Atmospheric Administration (NOAA)
Alabama Water Institute
NA22NWS4320003-T3-01S075
Water Extremes, Winter Weather
Alabama
July 2024 - June 2026
Water in the West
research to operations coordination, the research team will collaborate with ciroh principal investigators pis and engage with representatives from the office of water prediction owp to foster further integration and alignment between research and operational entities involved in ciroh and best position research products from ciroh to be seamlessly transitioned to owp, establishment of a comprehensive program aimed at bridging theory to practice and supporting ciroh's objective of enabling and supporting the transition of research into operations r2o within the national oceanic and atmospheric administration noaa
Alabama Water Institute
The research team will collaborate with CIROH Principal Investigators (PIs) and engage with representatives from the Office of Water Prediction (OWP) to foster further integration and alignment between research and operational entities involved in CIROH
0
Vortex tracker Integration to Global Workflow and Community Support
Benson
EPIC
search_activity
2025
Level 3
The work will involve exploring various multi-tile setups to determine how the FMS infrastructure handles these cases. The FV3 dycore will need updates to ensure the proper calling of the FMS nesting infrastructure consistent with a multi-tile approach to nests.
Rusty Benson
Active
Mon Dec 01 2025 03:00:00 GMT-0500 (Eastern Standard Time)
Mon Nov 30 2026 03:00:00 GMT-0500 (Eastern Standard Time)
Water in the West
AR
Finite-Volume Cubed-Sphere Dynamical Core (FV3)
The intended operational end state is a nesting system with minimal to no restrictions on movement of nests throughout the global system. The criteria for operational acceptance will be determined by the computational performance of the system in conjunction with meeting the necessary technology and science outcomes.
GFDL
WPOFY25-AR-GFDL-01
Water Extremes, Winter Weather
New Jersey
Earth Prediction Innovation Center (EPIC)
December 2025 - November 2026
Water in the West
vortex tracker integration to global workflow and community support, the work will involve exploring various multi tile setups to determine how the fms infrastructure handles these cases the fv3 dycore will need updates to ensure the proper calling of the fms nesting infrastructure consistent with a multi tile approach to nests
GFDL
The work will involve exploring various multi-tile setups to determine how the FMS infrastructure handles these cases. The FV3 dycore will need updates to ensure the proper calling of the FMS nesting infrastructure consistent with
0
Advancing the prediction of Atmospheric Rivers via data assimilation, physics, and machine learning
Alexander
Synoptic
search_activity
2025
Level 3
The overall proposal goal is to improve the prediction of landfalling AR events in terms of precipitation type, timing, duration, and location. For the improvement of AR-AFS, GSL will contribute with physics innovations and testing. For MPAS-AR, improvements will be accomplished through optimal use of additional observation types in the DA system and evaluation of their relative importance, development of ensemble-based DA and forecasts, and development and coupling of physics parameterizations for the complex problem of predicting long-distance vapor transport and long-duration terrain-forced precipitation processes. We will continue our collaboration with PSL on deep cumulus parameterization development and testing. In addition to physical model approaches to AR prediction, this proposal includes the use of an AI-based model to explore the possibility of leveraging it to increase AR predictability.
Thu Dec 31 2026 03:00:00 GMT-0500 (Eastern Standard Time)
Model & Forecast Guidance, Water in the West
AR
Model for Prediction Across Scales (MPAS), Unified Forecast System (UFS)
For all three main areas (MPAS-AR, AR-AFS, and data-driven Models), the technical approach to achieve the major outcomes will be to develop and apply science-based enhancements and/or new procedures, then test and refine procedures based on qualitative and quantitative assessments of results, followed by transition of the enhancements to the end-to-end modeling systems.
Development and testing of AR-AFS will contribute to a high-readiness on-demand system that can be deployed by the NWS in real time to create additional guidance in case of AR events. Development and further enhancements to the MPAS-AR prototype forecast system, including its scale-aware physics, JEDI-based DA, and stochastic components, will lead to improved AR forecast skill for both large- and small-scale features at longer lead times. In addition, the development of high-resolution regional and variable-resolution global / regional ensemble forecast components to MPAS-AR will allow for the improved characterization of forecast uncertainty, which will be very beneficial to emergency managers and other officials tasked with making critical water-related decisions with differing lead-time constraints. Furthermore, the development of an AI-based model component will augment traditional ensemble formulations, allowing potential further increases in both forecast skill and forecast uncertainty characterization. Datasets from these forecast components will also be beneficial to researchers examining methods for creating optimal decision support tools.
GSL
WPOFY25-AR-GSL
Water Extremes, Winter Weather
Colorado
July 2025 - December 2026
Model & Forecast Guidance, Water in the West
advancing the prediction of atmospheric rivers via data assimilation physics and machine learning, the overall proposal goal is to improve the prediction of landfalling ar events in terms of precipitation type timing duration and location for the improvement of ar afs gsl will contribute with physics innovations and testing for mpas ar improvements will be accomplished through optimal use of additional observation types in the da system and evaluation of their relative importance development of ensemble based da and forecasts and development and coupling of physics parameterizations for the complex problem of predicting long distance vapor transport and long duration terrain forced precipitation processes we will continue our collaboration with psl on deep cumulus parameterization development and testing in addition to physical model approaches to ar prediction this proposal includes the use of an ai based model to explore the possibility of leveraging it to increase ar predictability, for all three main areas mpas ar ar afs and data driven models the technical approach to achieve the major outcomes will be to develop and apply science based enhancements and or new procedures then test and refine procedures based on qualitative and quantitative assessments of results followed by transition of the enhancements to the end to end modeling systems
GSL
The overall proposal goal is to improve the prediction of landfalling AR events in terms of precipitation type, timing, duration, and location. For the improvement of AR-AFS, GSL will contribute with physics innovations and testing.
