Does a Scale-Aware Convective Parameterization Scheme Improve the Simulation of Heavy Rainfall Events?

Abstract

Precipitation predictability using the non-scale-aware and scale-aware convective parameterization schemes (CPSs) was investigated to assess the necessity of the CPSs within the gray-zone. This study evaluates the performance of the Weather Research and Forecasting (WRF) model's CPS for 135 heavy rainfall events (HREs) over the Korean Peninsula for 10 years (i.e., 2011–2020). We tested the Kain–Fritsch (KF) scheme (non-scale-aware) and Multi-scale Kain–Fritsch (MSKF) scheme (scale-aware) in the WRF model. The MSKF scheme shows an overall improved performance of precipitation simulation compared to the KF scheme, but the precipitation forecast performance of CPS depends on the characteristics of HREs. When the HREs are characterized by synoptic-scale atmospheric conditions with strong winds and large-scale water vapor transport, the forecast performance of both CPSs is similar because a cloud microphysics scheme can explicitly resolve most of the precipitation. However, in the case of HREs with weak synoptic forcing conditions (e.g., moisture transport and winds) related to the localized and meso-scale HREs, the MSKF scheme can improve overall simulated precipitation by increasing grid-scale precipitation and reducing the overestimation of subgrid-scale precipitation simulated in the KF scheme. Therefore, using the scale-aware CPS in the gray-zone can provide more accurate precipitation forecasts regardless of the environmental condition of the HREs.