Improved PM2.5 predictions of WRF-Chem via the integration of Himawari-8 satellite data and ground observations

The new-generation geostationary satellites feature higher radiometric, spectral, and spatial resolutions, thereby making richer data available for the improvement of PM2.5 predictions. Various aerosol optical depth (AOD) data assimilation methods have been developed, but the accurate representation of the AOD-PM2.5 relationship remains challenging. Empirical statistical methods are effective in retrieving ground-level PM2.5, but few have been evaluated in terms of whether and to what extent they can help improve PM2.5 predictions. Therefore, an empirical and statistics-based scheme was developed for optimizing the estimation of the initial conditions (ICs) of aerosol in WRF-Chem (Weather Research and Forecasting/Chemistry) and for improving the PM2.5 predictions by integrating Himawari-8 data and ground observations. The proposed method was evaluated via two one-year experiments that were conducted in parallel over eastern China. The contribution of the satellite data to the model performance was evaluated via a 2-week control experiment. The results demonstrate that the proposed method improved the PM2.5 predictions throughout the year and mitigated the underestimation during pollution episodes. Spatially, the performance was highly correlated with the amount of valid data. (c) 2020 Elsevier Ltd. All rights reserved.