Comparison between Multi-Physics and Stochastic Approaches for the 20 July 2021 Henan Heavy Rainfall Case

Abstract

In this study, three model perturbation schemes, the stochastically perturbed parameter scheme (SPP), stochastically perturbed physics tendency (SPPT), and multi-physics process parameterization (MP), were used to represent the model errors in the regional ensemble prediction systems (REPS). To study the effects of different model perturbation schemes on heavy rainfall forecasting, three sensitive experiments using three different combinations (EXP1: MP, EXP2: SPPT + SPP, and EXP3: MP + SPPT + SPP) of the model perturbation schemes were set up based on the Weather Research and Forecasting (WRF)-V4.2 model for a heavy rainfall case that occurred in Henan, China during 20–22 July 2021. The results show that the model perturbation schemes can provide forecast uncertainties for this heavy rainfall case. The stochastic physical perturbation method could improve the heavy rainfall forecast skill by approximately 5%, and EXP3 had better performance than EXP1 or EXP2. The spread-to-root mean square error ratios (spread/RMSE) of EXP3 were closer to 1 compared with those of the EXP1 and EXP2; particularly for the meridional wind above 10 m, the spread/RMSE was 0.94 for EXP3 and approximately 0.85 for EXP1 and EXP2. EXP3 exhibited better performance in Brier score verification. EXP3 had a 5% lower Brier score than EXP1 and EXP2, when the rainfall threshold was 25 mm. The growth of the initial ensemble variances of different model perturbation schemes were explored, and the results show that the perturbation energy of EXP3 developed faster, with a magnitude of 27.22 J/kg, whereas those of EXP1 and EXP2 were only 19.18 J/kg and 20.81 J/kg, respectively. The weak initial perturbation associated with the wind shear north of the heavy rainfall location can be easily developed by EXP3.