Enhanced retrieval of aerosol optical/microphysical parameters for Himawari-8 geostationary satellite measurements with data-driven deep learning method

The high temporal resolution of Himawari-8 (H8) geostationary satellite has distinctive advantages in capturing diurnal variation of aerosol properties. However, the operational H8 aerosol products including Aerosol Optical Depth (AOD) are subject to considerable uncertainties due to assumptions and simplified surface reflectance. To fully exploit the H8 multi-spectral measurements, a data-driven retrieval framework based on a Deep Belief Network (DBN) was developed to simultaneously retrieve aerosol optical and microphysical parameters. By directly modeling H8 spectral reflectance at the top of the atmosphere (TOA) with matched AERONET products in 82 sites across diverse surface types and emission sources, four aerosol parameters including AOD, fine and coarse AOD (FAOD and CAOD), and single scattering albedo (SSA) are retrieved. Ground-based validation shows that the H8 DBN retrievals achieve high accuracies for AOD and FAOD, with correlation coefficients of 0.935 and 0.930, respectively, substantially outperforming the operational JAXA product (R = 0.703 for AOD). The retrieved CAOD and SSA also show good agreement with AERONET observations. Comparisons with MODIS and JAXA H8 aerosol products show very high consistency in both spatial and temporal variations. H8 DBN retrievals can clearly distinguish biomass burning smoke and dust plumes by their particle size and absorption. Additionally, hourly H8 DBN retrievals accurately reflect diurnal variations of aerosol properties. Consequently, the developed DBN-based approach provides a flexible and robust retrieval method for dynamic aerosol properties over East Asia.