Optimizing the Numerical Simulation of the Dust Event of March 2021: Integrating Aerosol Observations through Multi-Scale 3D Variational Assimilation in the WRF-Chem Model

Abstract

The integration of high-resolution aerosol measurements into an atmospheric chemistry model can improve air quality forecasting. However, traditional data assimilation methods are challenged in effectively incorporating such detailed aerosol information. This study utilized the WRF-Chem model to conduct data assimilation and prediction experiments using the Himawari-8 satellite’s aerosol optical depth (AOD) product and ground-level particulate matter concentration (PM) measurements during a record-breaking dust event in the Beijing–Tianjin–Hebei region from 14 to 18 March 2021. Three experiments were conducted, comprising a control experiment without assimilation (CTL), a traditional three-dimensional variational (3DVAR) experiment, and a multi-scale three-dimensional variational (MS-3DVAR) experiment. The results indicated that the CTL method significantly underestimated the intensity and extent of the severe dust event, while the analysis fields and forecasting fields of PM concentration and AOD can be significantly improved in both 3DVAR and MS-3DVAR assimilation. Particularly, the MS-3DVAR assimilation approach yielded better-fitting extreme values than the 3DVAR method, mostly likely due to the multi-scale information from the observations used in the MS-3DVAR method. Compared to the CTL method, the correlation coefficient of MS-3DVAR assimilation between the assimilated PM10 analysis fields and observations increased from 0.24 to 0.93, and the positive assimilation effect persisted longer than 36 h. These findings suggest the effectiveness and prolonged influence of integrating high-resolution aerosol observations through MS-3DVAR assimilation in improving aerosol forecasting capabilities.