A comparison of the rainfall forecasting skills of the WRF ensemble forecasting system using SPCPT and other cumulus parameterization error representation schemes

Abstract

The scientific community mainly uses ensemble systems to represent various sources of uncertainties and to produce better forecasts. The inability to choose accurate initial conditions leads to failed forecasts due to the well-known butterfly effect; however, in modern weather models, researchers have paid more attention to inadequacies in the sense of physical parameterization schemes and other dynamic processes. In this study, the uncertainties caused by cumulus parameterization are represented by the stochastic perturbed parameterization tendency (SPPT) scheme, and the results are compared with those of classic schemes, including the multi cumulus parameterization scheme, the parameter-perturbed scheme, and the Gaussian-noise-perturbed tendency scheme. The impacts of these various schemes on the precipitation predictions of the Weather Research and Forecasting (WRF) model are compared for Southeast Asia. The results using the stochastic perturbed cumulus parameterization tendency (SPCPT) perturbation scheme are contrasted with those of the total parameterization tendency perturbation scheme. Compared to the other schemes, the multi cumulus parameterization scheme has better Brier skill scores (BSS) and greater spreads. However, the SPCPT scheme shows significant improvements in the root mean square error (RMSE) and the Gerrity skill score (GSS) for rainfall prediction. This result also implies that the noise pattern is critical to the ensemble system; thus, using a single error representation scheme may be insufficient to estimate the error in the cumulus parameterization process.