Assimilating Fengyun-4A observations to improve WRF-Chem PM2.5 predictions in China

Fengyun-4A (FY-4A) is a new generation geostationary satellite that provides high temporal resolution atmo-spheric observations of China and the adjacent regions. This study proposed to assimilate FY-4A observations via a combined utilization of a 3D variational method and a random forest approach. The ground-level PM2.5 concentrations were estimated as an intermediate variable and subsequently assimilated based on the Gridpoint Statistical Interpolation (GSI) system. Four parallel experiments were conducted to verify the proposed method, including a control experiment and three data assimilation experiments that assimilated satellite observations and ground observations alone and simultaneously. Results showed that the proposed approach improved PM2.5 predictions for most sites, especially in the highly polluted Beijing-Tianjin-Hebei and Yangtze River Delta re-gions. Assimilating PM2.5 estimations from satellite showed an advantage over the assimilation of ground PM2.5 observations in places where local or upstream regional PM2.5 monitoring sites are sparse. Simultaneous assimilation of the PM2.5 from satellite and ground observations further improved the PM2.5 predictions accu-racy in most places.