PM 2.5 assimilation within JEDI for NOAA's regional Air Quality Model (AQMv7): application to the September 2020 Western US wildfires

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Authors: Hongli Wang, Cory Martin, Jérôme Barré, Ruifang Li, Steve Weygandt, Jianping Huang, Youhua Tang, Hyun-Deok Choi, Andrew Tangborn, Kai Wang, Haixia Liu, Jeffrey Lee
Cited by: 3
References: 33

Abstract

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 (PM 2.5 , particles with aerodynamic diameters less than 2.5 µm). PM 2.5 assimilation is developed within the Joint Effort for Data assimilation Integration (JEDI) framework and tested using its 3D-Var data assimilation (DA) component. The PM 2.5 observation operator is constructed by combining newly developed PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 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.

Fields of Study & Subjects

Atmospheric aerosols and clouds Meteorological Phenomena and Simulations Atmospheric chemistry and aerosols

Publication details

Journal / source
Geoscientific model development
Publisher
Copernicus GmbH
Volume
19
Issue
6
Pages
2479–2495
Publication date
27 March 2026
Language
EN