WPO funds work to advance our understanding of Air Quality and Fire Weather to improve forecasts and warnings for the public. The projects selected in this competition involve the use of machine learning, data assimilation techniques, physics, and post-processing of data to address gaps in the models such as the UFS, create a coupled fire-atmosphere high resolution modeling system and ensemble forecast, and provide tools to the community. Air quality focused proposals are aimed at improving the National Air Quality Forecasting Capability predictions of ozone, suspended fine particulate matter, and wildfire smoke, as well as airborne dust from dust storms over the contiguous lower 48 states.
The award total* for the 4 selected projects equals $3.53 M in cooperative agreements. Each project will be funded for 3 years beginning Aug. 1, 2022 and ending July 31, 2025.
*Award totals are distributed over the life of the projects and conditional on appropriations
|Project Title||PI’s & Co-PI’s||Affiliations|
|Implementing a state-of-the-science fire behavior model in the Unified Forecast System||Pedro A. Jimenez Munoz (PI), Branko Kosovic, Maria Frediani, Timothy Juliano||National Center for Atmospheric Research (NCAR)|
|Enhancing high resolution forecasting capability of RRFS-CMAQ||Yang Zhang (PI), Daniel Q. Tong, Fanglin Yang||Northeastern University, George Mason University, NOAA/NWS Environmental Modeling Center (EMC)|
|A novel dynamical ensemble design for probabilistic air quality predictions during wildfires based on RRFS-CMAQ||Rajesh Kumar (PI), Stefano Alessandrini||National Center for Atmospheric Research (NCAR)|
|Beyond the “Big-Leaf” Model at NOAA: Use of Novel Satellite Data and In-Canopy Processes to Improve U.S. Air Quality Predictions||Patrick C. Campbell||George Mason University|