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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. [ABSTRACT FROM AUTHOR]

The snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas the LSMs evaluate the snow cover fraction, snow albedo, and snow depth through interaction with atmospheric conditions. When the initially-developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek for the optimal parameter values in the snow-related processes - snow cover fraction, snow albedo, and snow depth - of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in-situ surface observations and remotely-sensed satellite data. Snow data from surface observation stations representing five land cover types - deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands - are used to optimize five snow-related parameters that calculate the snow cover fraction, maximum snow albedo of fresh snow, and the fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of snow cover fraction on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root-mean squared errors by 17.0 %, 8.2 %, and 5.6 % on snow depth, snow cover fraction, and snow albedo, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 45.1 % and 32.6 %, respectively. [ABSTRACT FROM AUTHOR]