Predictability of the 7·20 extreme rainstorm in Zhengzhou in stochastic kinetic-energy backscatter ensembles

Abstract

The scale-dependent predictability of the devastating 7·20 extreme rainstorm in Zhengzhou, China in 2021 was investigated via ensemble experiments, which were perturbed on different scales using the stochastic kinetic-energy backscatter (SKEB) scheme in the WRF model, with the innermost domain having a 3-km grid spacing. The daily rainfall (RAIN24h) and the cloudburst during 1600–1700 LST (RAIN1h) were considered. Results demonstrated that with larger perturbation scales, the ensemble spread for the rainfall maximum widens and rainfall forecasts become closer to the observations. In ensembles with mesoscale or convective-scale perturbations, RAIN1h loses predictability at scales smaller than 20 km and RAIN24h is predictable for all scales. Whereas in ensembles with synoptic-scale perturbations, the largest scale of predictability loss extends to 60 km for both RAIN1h and RAIN24h. Moreover, the average positional error in forecasting the heaviest rainfall for RAIN24h (RAIN1h) was 400 km (50–60) km. The southerly low-level jet near Zhengzhou was assumed to be directly responsible for the forecast uncertainty of RAIN1h. The rapid intensification in low-level cyclonic vorticity, mid-level divergence, and upward motion concomitant with the jet dynamically facilitated the cloudburst. Further analysis of the divergent, rotational and vertical kinetic spectra and the corresponding error spectra showed that the error kinetic energy at smaller scales grows faster than that at larger scales and saturates more quickly in all experiments. Larger-scale perturbations not only boost larger-scale error growth but are also conducive to error growth at all scales through a downscale cascade, which indicates that improving the accuracy of larger-scale flow forecast may discernibly contributes to the forecast of cloudburst intensity and position.