Abstract. A new aerosol retrieval algorithm using multi-angular total and polarized measurements is presented. The algorithm retrieves aerosol optical depth (AOD), fine-mode fraction (FMF) for studying the impact of aerosol on climate change. The retrieval algorithm is based on a lookup table (LUT) method, which assumes that one fine and one coarse lognormal aerosol modes can be combined with proper weightings to represent the ambient aerosol properties. To reduce the ambiguity in retrieval algorithm, the key characteristics of aerosol model over East Asia are constrained using the cluster analysis technique based on the AERONET sun-photometer observation over East Asia, and the fine and coarse modes are not fixed but can vary. A mixing model of bare soil and green vegetation spectra and the Nadal and Breon model for the bidirectional polarized reflectance factor (BPDF) were used to simulate total and polarized surface reflectance of East Asia. By applying the present algorithm to POLDER measurements, three different aerosol cases of clear, polluted and dust are analyzed to test the algorithm. The comparison of retrieved aerosol optical depth (AOD) and fine-mode fraction (FMF) with those of AERONET sun-photometer observations show reliable results. Preliminary validation is encouraging. Using the new aerosol retrieval algorithm for multi-angular total and polarized measurements, the spatial and temporal variability of anthropogenic aerosol optical properties over East Asia, which were observed during a heavy polluted event, were analyzed. Exceptionally high values of aerosol optical depth contributed by fine mode of up to 0.5 (at 0.865 μm), and high values of fine-mode fraction of up to 0.9, were observed in this case study.
Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia
