Evaluation for the machine learning based PM2.5 estimation using different spatial resolution of geostationary satellite AOD over megacities in Korea - Juhee Lee; Jhoon Kim; Yeseul Cho; Seoyoung Lee; Drew C. Pendergrass; Daniel J. Jacob; Hyunkwang Lim; Hahn Chul Jung; Kwang-Min Myung; Young-Doh Kim; Kyung Man Han; Ja-Ho Koo (Atmospheric Environment, 2026)

Improvement of Dust Retrieval Algorithm Using GK-2 A Geo Satellite Thermal Infrared Channels by Establishing Observation-based Refractive Index Dataset Tailored for East Asian Dust - Ehsan Parsa Javid; Sang Seo Park; Hyunkwang Lim; Kwang-Mog Lee (Asia-Pacific Journal of Atmospheric Sciences, 2025)
Applying assumptions about the optical properties of dust, particularly the refractive index (RI), introduces significant uncertainty in thermal infrared dust-retrieval algorithms. To address this, we present a tailored RI dataset (ERML 2025) for Asian dust, derived from long-term chemical composition measurements in South Korea. An enhanced algorithm was developed using this Asian dust RI and thermal infrared channels from the GK-2 A Korean geostationary satellite. This LUT-based algorithm integrates three methods for dust layer height estimation: the Unified Model (UM), the Asian Dust Aerosol Model 3 (ADAM3), and a fixed-height approach. Operational dust detection processes and consistent assumptions were applied to minimize confounding variables in sensitivity tests. Qualitative validation using GK-2 A RGB and IASI-LMD products showed strong alignment in some regions and notable mismatches elsewhere, likely due to dust detection performance. Quantitative comparisons were conducted using MODIS data. Sensitivity analyses demonstrated that the combined use of the updated algorithm and UM model improved the operational method in most cases. Results also indicated that the updated algorithm retrieved higher AOD values, attributable to the increased absorption in the new RI dataset. Furthermore, comparisons with widely cited RI datasets revealed that while the real part of the Asian dust RI showed similar trends, its imaginary part differed markedly in magnitude and shape—reflecting the variability in dust origins. This region-specific RI dataset will help reduce inconsistencies in future studies caused by using RI values from remote sources that may not accurately represent Asian dust characteristics.

Retrieval of aerosol optical properties from GOCI-II observations: Continuation of long-term geostationary aerosol monitoring over East Asia - Seoyoung Lee; Myungje Choi; Jhoon Kim; Young-Je Park; Jong-Kuk Choi; Hyunkwang Lim; Jeewoo Lee; Minseok Kim; Yeseul Cho (Science of The Total Environment, 2023)

Temporal variation of surface reflectance and cloud fraction used to identify background aerosol retrieval information over East Asia - Sang Seo Park; Ji-Eun Yu; Hyunkwang Lim; Yun Gon Lee (Atmospheric Environment, 2023)

Fine particulate concentrations over East Asia derived from aerosols measured by the advanced Himawari Imager using machine learning - Yeseul Cho; Jhoon Kim; Jeewoo Lee; Myungje Choi; Hyunkwang Lim; Seoyoung Lee; Jungho Im (Atmospheric Research, 2023)