PM2.5 assimilation within JEDI for NOAA's regional Air Quality Model (AQMv7): application to the September 2020 Western US wildfires Geoscientific Model Development

This paper describes efforts to establish aerosol data assimilation capabilities for NOAA's National Air Quality Forecasting Capability (NAQFC), a regional online air quality modeling (AQM) system under NOAA's Unified Forecast System (UFS), by assimilating measurements of fine particulate matter (PM2.5, particles with aerodynamic diameters less than 2.5 µm). PM2.5 assimilation is developed within the Joint Effort for Data assimilation Integration (JEDI) framework and tested using its 3D-Var data assimilation (DA) component. The PM2.5 observation operator is constructed by combining newly developed PM2.5 transformation recipes in the JEDI Variable Derivation Repository (VADER) with a general spatial interpolation operator in the Unified Forward Operator (UFO). Cycled DA and forecast experiments were conducted from 1–21 September 2020, during a period of Western US wildfires, to assess the impact of assimilating PM2.5 observations from the AirNow and PurpleAir networks. The control and analysis variables include individual aerosol species, with background error standard deviations generated by scaling their respective background values. Prognostic variables such as aerosol particle number and total particulate surface area are updated accordingly following each analysis update. All DA experiments use a 3-hourly cycling interval, with PM2.5 observations assimilated every 3 h. The control experiment uses the same configuration but without any data assimilation. Results show that assimilating either AirNow or PurpleAir PM2.5 data reduces 1–24 h forecast errors in terms of mean absolute error (MAE) and root mean square error (RMSE) compared to the control run over Continental United States (CONUS). Substantial improvements are in regions where fire events took place and largely affected by transported smoke. Overall, the assimilation of PurpleAir observations in addition to AirNow data leads to a slight reduction in 3–24 h MAE. Grant no. NA23OAR4050200D Grant no. NA22OAR4320151