250 A Study on the Synergistic Use of a Meteorological Imager for Improving Aerosol Type Classification and the Aerosol Retrieval Algorithm of GEMS

Monday, 13 January 2020
Hall B (Boston Convention and Exhibition Center)
Sujung Go, Yonsei Univ., Seoul, Korea, Republic of (South); and J. Kim, M. Kim, S. Park, H. Lim, S. Lee, and Y. S. Choi

The Advanced Meteorological Imager (AMI) onboard Geokompsat-2A satellite were launched in 2018. Furthermore, the Geostationary Ocean Color Imager-2 (GOCI-2), and the Geostationary Environmental Monitoring Spectrometer (GEMS) onboard Geokompsat-2B satellites will be launched in 2020 in order to monitor ocean color and air quality over Asia. The above three payloads will observe aerosol over Asia from geostationary earth orbit with high spatial and temporal resolution, however their aerosol products have different characteristics with different aerosol algorithms. In this study, GEMS, a UV-visible spectrometer, aerosol retrieval algorithm with synergistic use of aerosol information of AMI are investigated. Low earth orbit satellites instruments are used to test the algorithm as a prototype. First, synergistic use of cloud masking from meteorological imager IR channel is tested. IR channels of meteorological imager help mask cirrus clouds for GEMS aerosol retrieval results. Second, Total Dust Confidence Index (TDCI) is developed for IR channels of meteorological imager to separate dust aerosols, and then applied to improve GEMS aerosol type algorithm. Statistical analysis showed that accuracy for dust aerosols of GEMS are changed from 72% to 91% by using TDCI for aerosol type selection. Third, corrected aerosol types are applied to GEMS algorithm. After applying TDCI as a dust type detection of GEMS aerosol algorithm, the retrieved results of aerosol optical depth (AOD) and single scattering albedo (SSA) are improved especially for smoke and dust aerosols. These improved results are consistent with GEMS aerosol algorithm sensitivity test about aerosol type misclassification. Finally, AOD products from GEMS and AMI are combined using the maximum likelihood estimate (MLE) method using weights derived from the root mean square error (RMSE) of the original AOD products. The combined AOD products showed increasing spatial coverage compared to the any of the original products, and the accuracy was comparable to the any of the original AOD products. Pixel-level error estimation of GEMS AOD before and after synergistic use of meteorological imager are also investigated.
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