Testbeds are unique collaborative spaces that allow researchers and forecasters to work together to improve weather prediction systems. They accomplish this by working alongside each other to integrate new observing systems into models, test and streamline data assimilation methods, test weather model improvements, and strategize new developments. The Weather Program Office funds research projects and infrastructure to support the Hydrometeorology Testbed, the Hazardous Weather Testbed, and the Hurricane & Ocean Testbed. This year, our review panel has selected several projects for the Testbeds Program. These projects involve the use of machine learning, data assimilation techniques, physics, post-processing, and model-coupling to improve forecasts, along with improving messaging of high impact weather.
The award total* for the 10 selected projects equals $5.5M in cooperative agreements and each project will be funded for 3 years beginning Aug. 1, 2022.
*Award totals are distributed over the life of the projects and conditional on appropriations
|Project Title||PI’s & Co-PI’s||Affiliations|
|Advancing Probabilistic Prediction of Snow-to-Liquid Ratio and Snowfall during High-Impact Winter Storms- Hydrometeorology Testbed||W. James Steenburgh (PI), Peter G. Veals||University of Utah|
|Developing the “Next-Generation” Winter Weather Experiment Testbed: Integrating Road Hazards- Hydrometeorology Testbed||Dr. Dana Tobin (PI), Dr. H. Reeves, Dr. K. Klockow-McClain, Dr. J. Correia, Dr. K. Harnos||University of Oklahoma, University of Colorado, NOAA/NWS Weather Prediction Center (WPC)|
|FV3-LAM CAM Ensemble Forecast System and Improving Ensemble Probabilistic and Consensus Forecast Products in Support of HMT Winter Weather and Heavy Precipitation Forecasting- Hydrometeorology Testbed||Keith Brewster (PI), Nathan Snook (Co-PI), Timothy Supinie (Co-PI) OU, Ming Xue (Co-PI)||University of Oklahoma|
|Informing UFS-based Rapid Refresh Forecast System (RRFS) ensemble development through evaluation of analysis uncertainty representation methods- Hazardous Weather Testbed||Jeff Beck (PI), Jamie Wolff (Co-PI), Craig Schwartz (Co-PI), Xuguang Wang (Co-PI), Aaron Johnson (Co-PI), Michelle Harrold (Co-I)||Cooperative Institute for Research in the Atmosphere (CIRA)/ Colorado State University, National Center for Atmospheric Research (NCAR), University of Oklahoma|
|Bridging Watch-to-Warning Forecast Operations with Refined Probabilistic Guidance- Hazardous Weather Testbed||Eric Loken (PI), Katie Wilson, Kristin Calhoun, Thea Sandmael||University of Oklahoma, NOAA/OAR/ National Severe Storm Laboratory (NSSL)|
|Probabilistic medium-range hazards guidance with an FV3-based convection-allowing ensemble and machine learning -Hazardous Weather Testbed||Craig Schwartz (PI), Ryan Sobash, Lucas Harris||National Center for Atmospheric Research (NCAR), NOAA/OAR/Geophysical Fluid Dynamics Laboratory (GFDL)|
|Forecaster Support Products for Analysis of Tropical Cyclone Intensity and Structure from Aircraft Reconnaissance Observations- Hurricane Ocean Testbed (previously the Joint Hurricane Testbed)||Jonathan L. Vigh (PI), Michael M. Bell, Jun A. Zhang, Eric A. Hendricks, Christopher M. Rozoff||National Center for Atmospheric Research (NCAR), Colorado State University, Cooperative Institute for Marine and Atmospheric Studies (CIMAS)/ University of Miami|
|A Machine Learning Model for Estimating Tropical Cyclone Track and Intensity Forecast Uncertainty- Hurricane Ocean Testbed (previously the Joint Hurricane Testbed)||Dr. Mark DeMaria (PI), Dr. Elizabeth Barnes||Cooperative Institute for Research in the Atmosphere (CIRA)/ Colorado State University|
|Expansion of Ensemble-based Sensitivity to TC Hazard Forecasts- Hurricane Ocean Testbed (previously the Joint Hurricane Testbed)||Dr. Ryan Torn, (PI)||University at Albany-SUNY|
|The Impact of Targeted Synoptic Dropsondes on Tropical Cyclone Forecasts in HAFS- Hurricane Ocean Testbed (previously the Joint Hurricane Testbed)||Dr. Sarah D. Ditchek (PI)||Cooperative Institute for Marine and Atmospheric Studies (CIMAS)/ University of Miami|