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Research Article

Aerosol data assimilation and forecast using Geostationary Ocean Color Imager aerosol optical depth and in-situ observations during the KORUS-AQ observing period

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Pages 1175-1194 | Received 16 May 2021, Accepted 11 Aug 2021, Published online: 10 Sep 2021

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Seohui Park, Jungho Im, Jhoon Kim & Sang-Min Kim. (2022) Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia. Environmental Pollution 306, pages 119425.
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Seunghee Lee, Ganghan Kim, Myong-In Lee, Yonghan Choi, Chang-Keun Song & Hyeon-Kook Kim. (2022) Seasonal Dependence of Aerosol Data Assimilation and Forecasting Using Satellite and Ground-Based Observations. Remote Sensing 14:9, pages 2123.
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Seunghee Lee, Seohui Park, Myong‐In Lee, Ganghan Kim, Jungho Im & Chang‐Keun Song. (2021) Air Quality Forecasts Improved by Combining Data Assimilation and Machine Learning With Satellite AOD. Geophysical Research Letters 49:1.
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Hyeon-Kook Kim, Seunghee Lee, Kang-Ho Bae, Kwonho Jeon, Myong-In Lee & Chang-Keun Song. (2022) An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data. Remote Sensing 14:2, pages 389.
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Yoojin Kang, Miae Kim, Eunjin Kang, Dongjin Cho & Jungho Im. (2022) Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia. ISPRS Journal of Photogrammetry and Remote Sensing 183, pages 253-268.
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