Improving the Asian dust storm prediction using WRF-Chem through combinational optimization of physical parameterization schemes

Abstract

This study aims to enhance the accuracy of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) in forecasting Asian dust storms (ADSs) by using the micro-Genetic Algorithm (μGA). We developed an optimization system---the WRF-Chem-μGA system---to seek the optimal combination of the planetary boundary layer (PBL) and land surface parameterization schemes, which are crucial for numerical forecast of dust storms. The optimization was conducted concerning meteorological and air quality variables, i.e., aerosol optical depth, PBL height, 2 m temperature, 2 m relative humidity, and 10 m wind speed, simultaneously for three ADS cases over the optimization domain, including South Korea. Among a total of 32 available combinations of physical parameterization scheme options (8 from PBL and 4 from land surface schemes), the optimized set through the WRF-Chem-μGA system consists of the Asymmetrical Convective Model version 2 (ACM2) for the PBL scheme and the Noah land surface model with Multiple Parameterization options (Noah-MP) for the land surface scheme. The optimized set showed an improvement ratio of up to 22.5 % in terms of the normalized RMSE for all meteorological and air quality variables, compared to various non-optimized sets of physical parameterization schemes for two additional ADS cases. The optimal set proposed in this study can be used comprehensively in numerical forecasts of various meteorological and air quality problems in the East Asian region, using the WRF-Chem model.