IDENTIFYING THE MAIN VARIABLES TO CLASSIFYING THE SYNOPTIC PATTERNS OF ASIAN DUST STORM OVER SOUTH KOREA BY PRINCIPAL COMPONENT ANALYSIS (PCA)

In air quality prediction, it is essential to find the upstream areas from which a small initial error can grow into large forecast errors in the region of interest. The conditional nonlinear optimal perturbation for initial conditions (CNOP-I) is a suitable tool for targeted (adaptive) observations. To calculate the CNOP-I several variables are included; those are used from the multiple energy equations (eg. kinetic energy, dry energy, moist energy, etc). In particular, the previous studies on improving the Asian dust storms (ADSs) prediction mainly considered the kinetic energy calculation, which included only two variables (u-wind and v-wind). However, variables affecting the actual ADS’s development and movement also include the other variables. This study aims to identify the main variables and classify the synoptic patterns for given ADS cases using the principal component analysis (PCA). We focus on the ADS events that occurred during the last 32 years (1990—2021) over South Korea. Through the PCA, we identified the top five variables — temperature, specific humidity, divergence, ozone mass mixing ratio, and eastward wind — which affect origination and translocation of the ADSs. Furthermore, the ADSs are significantly affected by vertical velocity, divergence, relative humidity, eastward wind, and potential vorticity; for example, strong downdraft and divergence make ADSs finally land on the Korean Peninsula. For a further study, we plan to identify the sensitive areas in targeted observations for air quality prediction via CNOP-I. We expect to improve air quality forecasts by classifying the synoptic situations that bring about severe Asian dust storm outbreaks in South Korea and by identifying the upstream areas for targeted observations to which we can enhance observations, potentially through international collaborations.