多次取样的网格化暴雨预报“配料法”研究

Research on“Batching Method”for Multi-sampling Grid Rainstorm Prediction

利用2008—2019年甘肃省陇东342个测站的逐日降水资料和ECMWF_ERA5再分析资料,分析了不同暴雨天气过程的环流特征,建立了精细至模式预报每个格点、综合考虑暴雨日每个预报时次的多次取样网格化暴雨预报“配料法”方案,并通过2020年5次暴雨过程进行验证。结果表明,陇东暴雨可分为西太平洋副热带高压(简称“副高”)边缘中尺度扰动型、冷槽切变型和偏北气流对流型;由于不同类型暴雨的环流特征差异明显,降水范围及持续时间不同,具有代表意义的关键物理量及阈值也不相同;网格化配料法的过程预报准确率相比ECMWF_THIN提高20%,站点预报准确率提高7.8%,性能优于ECMWF_THIN模式的暴雨预报;配料法对小区域预报效果优秀,预报范围越大预报准确率越低。

Based on daily precipitation data from 342 stations in Gansu province and ECMWF_ERA5reanalysis data from 2008 to 2019,the circulation characteristics of different types of rainstorm weather events were summarized.A “batching method” for rainstorm prediction was established that considers multiple sampling at each forecast time for rainstorm days and was precise to model prediction at each grid point.The method was validated using 5 rainstorm events in 2020.The results showed that heavy rain in eastern Gansu can be classified into three types:mesoscale disturbance at the edge of the subtropical high,cold trough shear,and northward convection.Due to the differences in the circulation characteristics of the different types of heavy rain,the precipitation range and duration as well as the key physical quantities and thresholds that represent them also vary.The grid-based batching method improves the process prediction accuracy by 20% and the site prediction accuracy by 7.8%,compared to ECMWF_THIN.The performance of the batching method is better than that of the ECMWF_THIN model for heavy rain forecasting.The batching method is especially effective for small-area prediction,and the accuracy of the forecast decreases as the range of the forecast increases.