IMPROVING AIR QUALITY PREDICTIONS THROUGH OPTIMIZATION OF OPTIONAL PHYSICAL PARAMETERIZATION SCHEMES IN WRF-CHEM USING MICRO-GENETIC ALGORITHM
This study aims to improve the air quality forecasting skill in East Asia by employing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and by selecting its optimal set of physical parameterization schemes, especially in the planetary boundary layer (PBL) and land surface (LS) processes, via the micro-genetic algorithm (μGA). Among a bunch of available options (8 PBL schemes and 4 LS schemes), the selected optimal schemes are the Asymmetric Convective Model, version 2 (ACM2) for PBL and the Noah land surface model with multiple parameterization options (Noah-MP) for LS, respectively. For the given Asian dust storms cases, the WRF-Chem, using the optimized schemes, resulted in higher correlation coefficients with observations in all variables, including aerosol optical depth, PBL height, temperature and relative humidity at 2 m, and wind speed and direction at 10 m, than using the other sets of parameterization schemes.
