Combined Dust Detection Algorithm for Asian Dust Events Over East Asia Using GK2A/AMI: a Case Study in October 2019

• Published in: Asia-Pacific journal of atmospheric sciences Vol. 58; no. 1; pp. 45 - 64
• Main Authors: Jang, Jae-Cheol, Lee, Soobong, Sohn, Eun-Ha, Noh, Yoo-Jeong, Miller, Steven D.
• Format: Journal Article
• Language: English
• Published: Seoul Korean Meteorological Society 01.02.2022; Springer Nature B.V; 한국기상학회
• Subjects: Advection; Algorithms; Atmospheric particulates; Atmospheric Sciences; Brightness temperature; Climatology; Clouds; Colour; Detection; Dust; Dust storms; Earth and Environmental Science; Earth orbit; Earth orbits; Earth Sciences; Geophysics/Geodesy; Imagery; Lidar; Monitoring methods; Optical analysis; Optical thickness; Original Article; Probability theory; Satellites; Storms; Surface radiation temperature; Temperature differences; Tests; 대기과학; East Asia; Dust detection; GK2A/AMI; Combined algorithm; Asian dust
• ISSN: 1976-7633; 1976-7951; 1976-7951
• DOI: 10.1007/s13143-021-00234-5

A combined algorithm comprising multiple dust detection methods was developed using infrared (IR) channels onboard the GEOstationary Korea Multi-Purpose SATellite 2A equipped with the Advanced Meteorological Imager (GK2A/AMI). Six cloud tests using brightness temperature difference (BTD) were utilized to reduce errors caused by clouds. For detecting dust storms, three standard BTD tests (i.e., , , and ) were combined with the polarized optical depth index (PODI). The combined algorithm normalizes the indices for cloud and dust detection, and adopts weighted combinations of dust tests depending on the observation time (day/night) and surface type (land/sea). The dust detection results were produced as quantitative confidence factors and displayed as false color imagery, applying a dynamic enhancement background reduction algorithm (DEBRA). The combined dust detection algorithm was qualitatively assessed by comparing it with dust RGB imageries and ground-based lidar data. The combined algorithm especially improved the discontinuity in weak dust advection to the sea and considerably reduced false alarms as compared to previous dust monitoring methods. For quantitative validation, we used aerosol optical thickness (AOT) and fine mode fraction (FMF) derived from low Earth orbit (LEO) satellites in daytime. For both severe and weakened dust cases, the probability of detection (POD) ranged from 0.667 to 0.850 and it indicated that the combined algorithm detects more potential dust pixels than other satellites. In particular, the combined algorithm was advantageous in detecting weak dust storms passing over the warm and humid Yellow Sea with low dust height and small AOT.