Abstract
Abstract. The Yonsei Aerosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 min AHI or 1 h GOCI data at 6 km×6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-Earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), as well as bias correction based on normalized difference vegetation indexes, and (2) compilation of the fused product using ensemble-mean and maximum-likelihood estimation (MLE) methods. Fused results show a better statistics in terms of fraction within 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 products can still be useful as MLE.
Highlights
Aerosols are generated by human activities and natural processes on local to global scales, and they have a lifetime of several to tens of days
aerosol optical properties (AOPs) data fusion in East Asia may be achieved using aerosol products of Advanced Meteorological Imager (AMI), Geostationary Ocean Color Imager (GOCI)-2, and the Geostationary Environment Monitoring Spectrometer (GEMS) on board the GK-2A and GK-2B satellites launched by South Korea in 2018 and 2020, respectively, with accuracy over bright surfaces being improved by the GEMS aerosol product
GV1 tends to show the opposite pattern to AES, and GV2 shows a positive bias over the ocean and results in a similar pattern to FM1 over the land
Summary
Aerosols are generated by human activities and natural processes on local to global scales, and they have a lifetime of several to tens of days. There is a critical surface reflectance at which aerosol signals disappear, depending on the single-scattering albedo (Kim et al, 2016) Over the ocean, both the MRM and ESR methods give high accuracy, but ESR results are robust with the Cox and Munk model. AOP data fusion in East Asia may be achieved using aerosol products of AMI, GOCI-2, and the Geostationary Environment Monitoring Spectrometer (GEMS) on board the GK-2A and GK-2B satellites launched by South Korea in 2018 and 2020, respectively, with accuracy over bright surfaces being improved by the GEMS aerosol product.
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