Air Quality Modeling & Forecasting

Assimilating High Spatiotemporal Resolution Observations of Atmospheric Composition for Regional Air Quality Forecasting with Dynamic Emissions Adjustment

Dr. Arthur Mizzi, Senior Research Fellow, NASA Ames Research Center/ORAU
Poor air quality (AQ) is one of the most important environmental issues facing the U.S. AQ managers use AQ forecasts (and other tools) to better understand, plan for, and mitigate poor AQ events. Accurate AQ forecasts depend, in part, on high spatiotemporal near-real-time (NRT) observations to initialize chemical transport models and dynamically adjust the emissions. TEMPO will provide high spatiotemporal AQ observations that will revolutionize AQ forecasting and emissions adjustment in the U.S. In anticipation of the TEMPO launch, in collaboration with researchers at NASA, NOAA, and various universities, we are using WRF-Chem/DART with regional domain resolutions of 12 km, 4 km to assimilate synthetic TEMPO observations together with conventional meteorological, AQ in situ, and AQ remote-sensing (ground-based profilers and/or satellites) observations in observing system simulation experiments (OSSEs) and retrospective forecast/assimilation/verification experiments with dynamic emissions adjustment to determine the benefit of TEMPO for improving: (1) AQ forecast skill and predictability; (2) anthropogenic emission estimates, and (3) wildfire emission estimates.

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