Application of an Improved Analog-Based Heavy Precipitation Forecast Model to the Yangtze–Huai River Valley and Its Performance in June–July 2020

作     者：Baiquan ZHOU Panmao ZHAI Ruoyun NIU Baiquan ZHOU;Panmao ZHAI;Ruoyun NIU

作者机构：State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 National Meteorological CenterChina Meteorological AdministrationBeijing 100081 

出 版 物：《Journal of Meteorological Research》 (气象学报（英文版）)

年 卷 期：2021年第35卷第6期

页      面：987-997页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：Supported by the National Key Research and Development Program of China(2018YFC1507700) National Natural Science Foundation of China(41905082) Basic Research to Operation Fund of Chinese Academy of Meteorological Sciences(2019Y009) 

主　　题：analog-based forecast heavy precipitation event forecast verification latitudinal rainband 

摘      要：Precipitation extremes,such as the record-breaking Meiyu characterized by frequent occurrences of rainstorms that resulted in severe flooding over the Yangtze-Huai River valley(YHRV)in June-July 2020,are always attracting considerable interest,highlighting the importance of improving the forecast accuracy at the medium-to-long *** elevate the skill in forecasting heavy precipitation events(HPEs)with both long and short durations,the Key Influential Systems Based Analog Model(KISAM)was further improved and brought into operational application in *** and comparison of this newly adapted analog model and ensemble mean forecasts from the ECMWF at lead times of up to 15 days were carried out for the identified 16 HPEs over the YHRV in June-July *** results demonstrate that KISAM is advantageous over ECMWF ensemble mean for forecasts of heavy precipitation≥25 mm day-1 at the medium-to-long(6-15-day)lead times,based on the traditional dichotomous *** short lead times,ECMWF ensemble mean outperforms KISAM due largely to the low false alarm rates(FARs)benefited from an underestimation of the frequency of heavy ***,at the medium-to-long forecast range,the large fraction of misses induced by the high degree of underforecasting overwhelms the fairly good FARs in the ECMWF ensemble mean,which partly explains its inferiority to KISAM in terms of the threat *** assessment on forecasts of the latitudinal location of accumulated heavy precipitation indicates that smaller displacement errors also account for a part of the better performance of KISAM at lead times of 8-12 days.