NOAA’s Next-Generation Air Quality Predictions for the United States

NOAA is developing the next generation air quality (AQ) prediction system for the United States (U.S.) and global aerosol predictions within the Unified Forecast System (UFS) framework to better represent and forecast impacts of wildfires on AQ and impacts of aerosols globally on weather from hourly to subseasonal scales. A new regional UFS weather model is online coupled with chemistry represented by the EPA’s Community Multiscale AQ (CMAQ) modeling system with Carbon Bond VI and AERO6 mechanisms to form this new UFS-AQM system. Wildfire emissions are specified by satellite-observed  hourly Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). Anthropogenic emissions are based on U.S. EPA’s National Emissions Inventories over the contiguous 48 U.S. states and global inventories elsewhere. Lateral boundary conditions for aerosols are provided by NOAA’s Global Ensemble Forecast System which includes the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. A bias correction post-processing procedure is included in UFS-AQM to improve prediction accuracy. Testing is performed over a large regional domain covering the U.S., and evaluation is done in near-real time and for retrospective periods. Recent examples indicate much improved representation of impacts of wildfires on AQ predictions, especially during Quebec fires in the summer of 2023. 

 

Some of the planned refinements for UFS-AQM include better representation of plume rise for wildfire smoke and for point source emissions, increased resolution consistent with the Rapid Refresh Forecast System (RRFS), which is under development, and using aerosol lateral boundary conditions from a 6-way coupled atmosphere - ocean -  land - sea-ice - waves - aerosols global UFS system, also under development. Due to challenging computational requirements for UFS-AQM at high resolution, a machine learning emulator is being developed to improve computational efficiency for prediction of chemical transformations and tracer transport. Of most interest for this session, data assimilation capabilities are being developed to constrain initial conditions for pollutant concentrations in UFS-AQM. Observations being assimilated include fine particulate matter (PM2.5) observations from AirNow, VIIRS Aerosol Optical Depth (AOD) retrievals and TROPOspheric Monitoring Instrument (TROPOMI) NO2 retrievals.