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. The Yonsei Aerosol Retrieval (YAER) algorithm for theGeostationary Ocean Color Imager (GOCI) retrieves aerosol optical propertiesonly over dark surfaces, so it is important to mask pixels with brightsurfaces. The Advanced Himawari Imager (AHI) is equipped with threeshortwave-infrared and nine infrared channels, which is advantageous forbright-pixel masking. In addition, multiple visible and near-infraredchannels provide a great advantage in aerosol property retrieval from theAHI and GOCI. By applying the YAER algorithm to 10 min AHI or 1 h GOCIdata at 6 km×6 km resolution, diurnal variations and aerosoltransport can be observed, which has not previously been possible fromlow-Earth-orbit satellites. This study attempted to estimate the optimalaerosol optical depth (AOD) for East Asia by data fusion, taking intoaccount satellite retrieval uncertainty. The data fusion involved two steps:(1) analysis of error characteristics of each retrieved result with respectto the ground-based Aerosol Robotic Network (AERONET), as well as bias correctionbased on normalized difference vegetation indexes, and (2) compilation ofthe fused product using ensemble-mean and maximum-likelihood estimation (MLE)methods. Fused results show a better statistics in terms of fractionwithin the expected error, correlation coefficient, root-mean-square error (RMSE),and median bias error than the retrieved result for each product. If the RMSE and mean AOD bias values used for MLE fusion are correct, the MLE fused products show better accuracy, but the ensemble-mean productscan still be useful as MLE.

Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns