The Weather Research and Forecasting Innovation Act of 2017 calls for NOAA to improve its Subseasonal to Seasonal (S2S) capabilities, and defines subseasonal to seasonal as the range between two weeks and two years. Additionally, the FY22 Appropriation mandated an increased emphasis on improvements to hydrologic prediction for the Western states. To meet these needs, WPO funds research addressing a spectrum of issues on the subseasonal to seasonal time frame ranging from foundational research to the transition of research to operations. This year’s competition focused on projects that increase capabilities related to precipitation, its excess, shortfalls, duration, and precision of spatial and temporal placement on the subseasonal to seasonal scale. Factors exacerbating fire weather precursors such as vegetation for fuel development were also of interest as they impact the interlinked phenomena of precipitation and hydrology. WPO emphasized improvements to models and components within NOAA’s Unified Forecast System (UFS) to address improvements in data assimilation, numerical model processes and component interaction via the community-based UFS, and within the North American Multi-Model Ensemble (NMME) for multi-model ensemble methods and postprocessing.
This year’s competition 3-year award total* for the 6 selected projects equals $5.40 M in both grants and cooperative agreements. These projects will run for 3 years starting in August 2022.
*Award totals are distributed over the life of the projects and conditional on appropriations
|Project Title||PI’s & Co-PI’s||Affiliations|
|Enhancing NOAA UFS subseasonal to seasonal predictions of precipitation and drought via improved representation of snowpack processes||Cenlin He (PI), Fei Chen, Ronnie Abolafia-Rosenzweig||National Center for Atmospheric Research (NCAR)|
|Assessing the impact of dynamic vegetation on drought forecasts||Jason Otkin (PI), Michael Ek, Tara Jensen||University of Wisconsin, National Center for Atmospheric Research (NCAR)|
|The Road to the Seasonal Forecast System: Improving Prediction of Precipitation Extremes||Benjamin Cash (PI), James Kinter, David Straus, Chul-Su Shin||George Mason University|
|Characterizing the impact of UFS model error and bias on S2S CONUS forecast skill using a hybrid UFS-machine learning approach||John Albers (PI), Matt Newman||Cooperative Institute for Research in Environmental Sciences (CIRES)/ University of Colorado|
|Integrated surface physics for coupled hydrometeorology in the UFS for S2S prediction||David Gochis (Co-PI), Paul Dirmeyer, Michael Ek||National Center for Atmospheric Research (NCAR), George Mason University|
|Identifying efficient perturbations for initializing subseasonal-to-seasonal ensemble forecasts with the UFS||Stephen Penny (PI), Thomas Hamill, Arun Kumar||University of Colorado, NOAA/OAR/ Physical Sciences Laboratory (PSL), NOAA/NWS/ Climate Prediction Center (CPC)|