Assessment of AOD Simulation by Assimilating Himawari and MODIS Products

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 models 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 Himawari 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 Himawari 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 Himawari 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.