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

Abstract

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.