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

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外文摘要：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 di-vergence,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.

外文关键词：

Stochastic kinetic-energy backscatter(SKEB)Extreme rainfallEnsemble forecastPredictability

作者：

Min YANG、Peilong YU、Lifeng ZHANG、Xiaobing PAN、Quanjia ZHONG、Yunying LI

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作者单位：

College of Meteorology and Oceanography,National University of Defense Technology,Changsha 410073,China

College of Meteorology and Oceanography,National University of Defense Technology Changsha 410073,China

Key Laboratory of High Impact Weather(Special),China Meteorological Administration,Changsha 410073,China

Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster,Ministry of Education/Joint International Research Laboratory of Climate and Environment Change,Nanjing University of Information Science and Technology,Nanjing 210044,China

Department of Ocean Science,Hong Kong University of Science and Technology,Hong Kong 999077,China

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基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaHunan Provincial Natural Science Foundation of China

项目编号：

421050664220504641975066 & U22422012021JC0009

出版年：

2024

DOI：

10.1007/s11430-023-1357-1

中国科学:地球科学(英文版)

中国科学院

中国科学:地球科学(英文版)

影响因子：1.002

ISSN：1674-7313

年,卷(期)：2024.67(7)