The implementation of Yonsei Aerosol Retrieval (YAER) Algorithm to GK-2A/AMI and FY-4A/AGRI.

Abstract:

The Yonsei AErosol Retrieval Algorithm (YAER) have been developed and improved with geostationary satellites such as GOCI/COMS (Lee et al., 2010; Choi et al., 2017) and AHI/Himawari-8 (Lim et al., 2018). A new era of the geostationary meteorological satellites GEO-KOMPSAT 2A (GK-2A), Feng-Yun 4A (FY-4A), were launched by Korea Meteorological Administration (KMA) in 2018 and China Meteorological Administration (CMA) in 2016, respectively. An Advanced Meteorological Imager (AMI) onboard GK-2A has 16 channels from 470nm to 13.3 μm, performing Extended Local Area (ELA) observation for every 2 minutes from geostationary Earth orbit at 128.2⁰E longitude. Meanwhile, Advanced Geostationary Radiation Imager (AGRI) onboard FY-4A has 14 channels from 470 nm to 13.5 μm, performing full-disk imaging for 40 times a day from geostationary orbit at 104.7⁰E longitude. In this study, we implement the YAER algorithm for both AMI and AGRI. Estimating surface reflectivity is important in retrieving aerosol optical depth (AOD). Surface reflectance estimation over land adopts a minimum reflectance(MRM) method, while ocean surface reflectance adopted the Cox and Munk method. Current YAER algorithm can retrieve aerosol properties only over dark surfaces. Therefore, masking of bright surfaces including snow, desert, turbid water, and that of cloud pixels is important. With the 12 (AMI; 1.37, 1.61, 3.83, 6.21, 6.94, 7.33, 8.59, 9.62, 10.3, 11.2, 12.3, 13.3 μm) and 10 (AGRI; 1.37, 1.61, 2.25, 3.75, 6.25, 7.10, 8.50, 10.7, 12.0, 13.5 μm) infrared channels, observed data from both sensors can mask those pixels with decent accuracy. Especially, detection of cirrus cloud pixels is more advantageous on AMI and AGRI than other satellites (e.g., GOCI, AHI) since they have a 1.37 μm shortwave infrared channel. Despite of only 2 visible channels on AGRI as compared to 3 channels for AHI, retrieved AOD products still show a good qualitative agreement with the AHI. This study presents a preliminary result of the aerosol property retrieval from AMI and AGRI. The retrieval of aerosol properties from AMI and AGRI YAER algorithm will bridge the spatial and temporal gap of the GOCI and the AHI. Also, the robustness of the YAER algorithm presents a possibility of aerosol optical property retrieval improvement by stereo-viewing geostationary satellites.