Project goal: the overall goal is to improve spatial and temporal variations of anthropogenic and fire emissions for the unified forecast system-air quality model (UFS-AQM) to support the national air…

The National Oceanic and Atmospheric Administration awarded a cooperative agreement to George Mason University on August 1, 2026, totaling $880,000 to improve spatial and temporal variations of anthropogenic and fire emissions for the Unified Forecast System-Air Quality Model (UFS-AQM) under the Weather and Air Quality Research program (CFDA 11.459).

George Mason University will deliver anthropogenic emission data in model-ready format at 13 kilometers, 9 kilometers, or 3 kilometers resolution for both 2023 baseline and forecast-year conditions, incorporating 2023 National Emission Inventories, dynamic weather adjustments, and satellite rapid refresh. The recipient will also update fire emissions with new chemical speciation, fire plume chemistry, improved plume rise parameterization, and small fire assessment, producing improved nitrogen oxides and volatile organic compounds emissions from regional Advanced Baseline Imager and Visible Infrared Imaging Radiometer Suite fire emissions data. George Mason University will evaluate the effects of individual and combined updates on air quality predictions within UFS-AQM and produce peer-reviewed papers and presentations. The work is performed in Fairfax, Virginia, with a period of performance extending from August 1, 2026, through July 31, 2029.

This funding supports the National Air Quality Forecast Capability by reducing spatiotemporal uncertainty in emission inputs to improve accuracy of national air quality forecasts used by state and local agencies for early warnings to mitigate adverse impacts of air pollution on human health and the economy.

Project goal: the overall goal is to improve spatial and temporal variations of anthropogenic and fire emissions for the unified forecast system-air quality model (UFS-AQM) to support the national air quality forecast capability (naqfc). Problem/opportunity statement: due to fast changes in emission sources and the complexity in compiling neis, anthropogenic emission data are often outdated and does not account for weather influence, imposing large uncertainty on forecasting results. Substantial uncertainties also exist in fire emissions, including emission factors, plume chemistry and plume rise calculation. Methodology/activities to be performed: we will improve anthropogenic emissions with 2023 national emission inventories, dynamic weather adjustments and satellite rapid refresh. Fire emissions will be updated with new chemical speciation, fire plume chemistry, plume rise parameterization and small fire assessment. Effects of individual and combined updates on air quality predictions will be evaluated within UFS-AQM. Primary project products/outputs: 1) anthropogenic emission data, one for 2023 and another in forecast year, in model-ready format at 13KM, 9KM or 3KM resolutions; 2) improved nitrogen oxides (NOX) and volatile organic compounds (vocs), emissions from regional ABI and viirs fire emissions (rave); 3) new plume rise parameterization; 4) peer-reviewed papers and presentations. Expected results, outcomes, and benefits: this planned work is expected to reduce spatiotemporal uncertainty in the emission inputs and improve the accuracy of national air quality forecasts, which are widely used to support state and local early warnings to mitigate adverse impacts of air pollution on human health and the economy. Intended beneficiaries and recipients: 1) NOAA air resources laboratory who is leading noaas development of UFS-AQM along with EMC and other labs; 2) NWS/ncep who will provide guidance for research to operation transition; 3) nesdis/star: the fire emission work can help further improve rave products produced by nesdis. The high-resolution emission datasets will also benefit the broader air quality modeling community. High-performance computing and testbed: GMU and sdsu have existing computing infrastructure, including the GMU hopper cluster, to conduct the proposed emission modeling. Team members also have access to NOAA supercomputer resources. This work will use the usfaqm testbed developed NOAA ARL and EMC.