Evaluation of the Unified Forecast System Air Quality Model (UFS‐AQM) Online Air Quality Prediction System During the 2020 US Wildfire Season

Abstract In 2020, a record‐breaking 10.1 million acres burned in US wildfires. To investigate this prolific fire season's impacts on US air quality, the National Oceanic and Atmospheric Administration (NOAA)'s 72‐hr Unified Forecast System Air Quality Model (UFS‐AQM) is run once‐daily from 15 August–30 September 2020. A meteorological and air quality‐based forecast verification is carried out for this period. The 72‐hr forecasts of near‐surface temperature, moisture, and winds perform quite well against observations in terms of correlation and bias. The UFS‐AQM surface ozone and fine particulate matter (PM 2.5 ) often concur with US Environmental Protection Agency (EPA) AirNow station data, though the model ozone displays a consistent positive bias ranging from 8.5 to 15.4 ppb. Anomalously poor PM 2.5 predictions are identified during extreme wildfire activity in the Pacific Northwest between 13 and 20 September. Here, in addition to periods of widespread cloudiness, smoke is quite thick. As a result, fire activity is obscured from satellite detection. The UFS‐AQM thus underestimates wildfire emissions in the Pacific Northwest. The lack of dynamic aerosol–radiation interactions in UFS‐AQM coupled with uncertainties in fire emissions leads to a large underestimation of surface PM 2.5 there, along with high temperature and low humidity biases. Finally, satellite‐based measurements are also employed to evaluate the UFS‐AQM's performance in the deeper troposphere. The Visible Infrared Imaging Radiometer Suite 550‐nm aerosol optical depth provides additional insights into the UFS‐AQM's handling of smoke transport, while the TROPOspheric Monitoring Instrument suggests UFS‐AQM suffers from overpredictions of NO 2 and CO in active fire regions.