Effect of Cumulus Parameterization Schemes on the High-Resolution Numerical Simulation of Heavy Meiyu Front Rainfall Events

This study employed the Weather Research and Forecasting Model (WRF), Version 4.0.2, to simulate two Meiyu front precipitation processes in Zhejiang Province. Comparative analyses were performed using two microphysics schemes: the WRF Single-Moment 6-class (WSM6) and the Thompson schemes, two boundary layer schemes: the Yonsei University boundary layer scheme (YSU) and the Mellor-Yamada-Janjic (MYJ) schemes, and eleven cumulus convective parameterization schemes to investigate the influence of different schemes on the Meiyu front precipitation forecast. The results were as follows. (1) In the process of precipitation forecast evaluation for each experiment, both traditional methods (P2P and G2G) and the neighborhood method can objectively reveal the prediction level of each experiment. However, the neighborhood method conducts a more objective evaluation of the prediction level for small-scale heavy precipitation. (2) Three types of cumulus convection solutions, namely no cumulus, traditional cumulus, and scale-aware cumulus convections, can better simulate the light precipitation; however, with the intensification of precipitation due to rainstorms and heavy rainstorms, the scale-aware cumulus convection schemes significantly improve the forecast outcomes. (3) Under different microphysics and boundary layer combination schemes, the differences of simulation results of the scale-aware cumulus convection schemes were more significant than that of the traditional cumulus convection schemes. (4) Compared with the traditional cumulus convection scheme, the scale-aware cumulus convection scheme can considerably improve the prediction results of the model when the grid resolution is increased to 1 km in the gray zone range of 1–10 km. The results of this study can serve as a reference to some extent for the application of scale-aware convective parameterization schemes in high-precision operational forecasting.