Aerosol observation data assimilation is key to atmospheric environmental prediction. The original CMA chemistry–weather (CMA-CW) 3DVar data assimilation system uses simply fine and coarse particulate matter (PM_2.5 and PM_2.5–10) as control variables, with only concentrations of PM_2.5 and PM₁₀ being assimilated. In this study, a new 3DVar assimilation framework is developed, which considers seven aerosol species (black carbon, organic carbon, soil-dust, sea salt, sulfate, nitrate, and ammonium, instead of PM_2.5 and PM_2.5–10) in refined size segments as control variables, aiming to direct assimilate more aerosol related variables such as aerosol optical depth and lidar extinction and to provide accurate chemical initial fields for the CMA-CW model. Moreover, in the new assimilation framework, a novel equal-proportion distribution and compensation for background error increment is proposed and implemented, which produces physically more reasonable background error and thereby more reliable atmospheric–chemical analysis increments. The new assimilation framework is validated by idealized and real-case assimilation experiments, with reasonable performance. Based on the CMA-CW coupled model with the new assimilation framework and a rapid update cycle configuration, five-day CMA-CW simulation experiments for a widespread heavy fog–haze event in winter 2016 are carried out. The results demonstrate that assimilation of the surface aerosol observation data significantly improves the short-time forecast of atmospheric pollutants, due to refined and more precise information on aerosol compositions brought by the new assimilation framework. Meanwhile, the surface aerosol data assimilation also makes a positive contribution to visibility forecasting, significantly improving the visibility forecast in the heavy pollution areas.

With advancements in remote sensing technology and retrieval algorithms, many high-performance aerosol observation satellites have enriched the spatial and temporal coverage of aerosol optical depth (AOD) data, providing rich assimilation data for numerical model simulation and forecast. In this study, the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was employed to simulate the hourly AOD across China and its neighboring regions during summer of 2017 and winter of 2017/2018. The AOD observations from the Himawar-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites were assimilated by using a three-dimensional variational assimilation method in the Gridpoint Statistical Interpolation (GSI) system. The results implied that the AOD data assimilation from either Himawar-8 or MODIS was more consistent with Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data and ground station observations. The performance of AOD data assimilation was highly dependent on effective satellite data. When both the MODIS and Himawar-8 AOD data were assimilated, the simulation showed significant improvement in summer, while this enhancement was less pronounced in winter. For severe polluted areas (e.g., the Sichuan basin, and central and eastern China), simultaneous assimilation of both satellites data led to better performance than did individual satellite data assimilation, particularly over the Sichuan basin. In the clean region of the Qinghai–Xizang Plateau, the improvement was even more significant in winter. Moreover, the simultaneous assimilation of both satellites produced more consistent results with site-based observations than the assimilation of data from either satellite alone. This study reveals that missing satellite remote sensing data significantly impacts assimilation performance. Enhancing the assimilation data ratio through artificial intelligence-based multi-source data fusion represents a key focus for future research.