Identification of Key Meteorological Variables and Synoptic Patterns of High-Concentration Asian Dust Storms

High-concentration Asian dust storm (ADS) events significantly increase atmospheric particulate matter concentrations, adversely affecting respiratory health and causing substantial economic losses in environmental and agricultural productivity. Understanding the mechanisms, pathways, and behavior patterns of ADS is essential for providing early warnings for periods and regions where severe dust events are anticipated, enabling appropriate preventive measures. This research aims to identify key meteorological variables and classify synoptic patterns of high-concentration sand dust events (dust warnings) during the beginning stages at the source regions and the peak concentration stages observed over Korea, using unsupervised learning methods such as Principal Component Analysis (PCA) and K-means clustering. The study analyzed the ADS cases observed from 2002 to 2022, utilizing the ECMWF reanalysis data (ERA5). The results indicate that during the origin stage, the primary meteorological variables were the geopotential height and temperature at lower layers (900-1000hPa), while temperature and humidity were key variables during the peak concentration observation stage. Each stage was classified into five cluster patterns. Temporal analysis of these patterns revealed that most ADSs occurred more frequently at night, except for pattern 4 that was predominantly observed during daytime, and pattern 5 that was exclusively observed at night. During the peak concentration stage, three out of five patterns showed more frequent occurrences at night, while patterns 1 and 4 had more frequent daytime occurrences, and pattern 5 was predominantly observed at night. This research contributes to the improvement of high-concentration ADS prediction by classifying and analyzing the synoptic patterns related to their occurrences.