Retrieval Of The Fine-Mode Aerosol Optical Depth Over East China Using A Grouped Residual Error Sorting (Gres) Method From Multi-Angle And Polarized Satellite Data

The fine-mode aerosol optical depth (AOD(f)) is an important parameter for the environment and climate change study, which mainly represents the anthropogenic aerosols component. The Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) instrument can detect polarized signal from multi-angle observation and the polarized signal mainly comes from the radiation contribution of the fine-mode aerosols, which provides an opportunity to obtain AOD(f) directly. However, the currently operational algorithm of Laboratoire d'Optique Atmospherique (LOA) has a poor AOD(f) retrieval accuracy over East China on high aerosol loading days. This study focused on solving this issue and proposed a grouped residual error sorting (GRES) method to determine the optimal aerosol model in AOD(f) retrieval using the traditional look-up table (LUT) approach and then the AOD(f) retrieval accuracy over East China was improved. The comparisons between the GRES retrieved and the Aerosol Robotic Network (AERONET) ground-based AOD(f) at Beijing, Xianghe, Taihu and Hong_Kong_PolyU sites produced high correlation coefficients (r) of 0.900, 0.933, 0.957 and 0.968, respectively. The comparisons of the GRES retrieved AOD(f) and PARASOL AOD(f) product with those of the AERONET observations produced a mean absolute error (MAE) of 0.054 versus 0.104 on high aerosol loading days (AERONET mean AOD(f) at 865 nm = 0.283). An application using the GRES method for total AOD (AOD(t)) retrieval also showed a good expandability for multi-angle aerosol retrieval of this method.