Evaluation of Dust Emission and Land Surface Schemes in Predicting a Mega Asian Dust Storm over South Korea Using WRF-Chem (v4.3.3)

Abstract. This study evaluates the performance of the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) in forecasting a mega Asian Dust Storm (ADS) event that occurred over South Korea on March 28–29, 2021. We specifically evaluated a combination of five dust emission schemes and four land surface schemes, which are crucial for predicting ADSs. Using in-situ and remote sensing data, we assessed surface meteorological and air quality variables, including 2 m temperature, 2 m relative humidity, 10 m wind speed, particulate matter 10 (PM10), and aerosol optical depth (AOD) over South Korea. Our results indicate that prediction of surface meteorological variables is more influenced by the land surface scheme than by the dust emission scheme—generally showing good performance when dust emission schemes are combined with the Noah land surface model with Multiple Parameterization options (Noah-MP). In contrast, prediction of air quality variables, including PM10 and AOD, is strongly affected by the dust emission schemes, which is directly related to the generation and amount of dust through interaction with surface properties. Among the total of 20 available scheme combinations, the University of Cologne 2004 combined with the Community Land Model version 4.0 (UoC04-CLM4) showed the best performance, closely followed by the University of Cologne 2001 combined with CLM4 (UoC01-CLM4). UoC04-CLM4 outperformed the other scheme combinations by reducing the root mean square errors of PM10 up to 29.6 %. However, both UoC04-CLM4 and UoC01-CLM4 simulated values closest to the MODIS AOD but tended to overestimate the AOD in some regions during the origination and transportation processes. In contrast, other scheme combinations significantly underestimated the AOD throughout the entire simulation process of ADSs.