暴雨灾害TORRENTIALRAINANDDISASTERSVol.42No.3Jun.2023第42卷第3期2023年6月ComparativeanalysisofsimulationofaheavyraininSichuanProvincewithdifferentdataassimilationWENYing1,2,FENGCaiyun1,4,YULian3(1.ChengduUniversityofInformationTechnology,PlateauAtmosphereandEnvironmentKeyLaboratoryofSichuanProvince,Chengdu610225;2.OceanUniversityofChina,Qingdao266100;3.InstituteofPlateauMeteorology,CMA,HeavyRainandDrought-FloodDisastersinPlateauandBasinKeyLaboratoryofSichuanProvince,Chengdu610072;4.KeyLaboratoryforCloudPhysicsofChinaMeteorologicalAdministration,Beijing100081)Abstract:Inordertoevaluatetheinfluenceoftheassimilationofdifferentobservationaldatasuchasconventionalgroundobservations,ra⁃diosondeandradarradialwindonthemeso-scalemodelofheavyrainforecastinSichuanProvince,aheavyrainstormprocessinSichuanfrom14to18June,2020isusedasanexample.UsingWeatherResearchAndForecasting(WRF)modelandGridPointStatisticalInterpola⁃tion(GSI)assimilationsystem,weassimilatedtheconventionalandradardatarespectivelyandsimultaneously，andcomparedtheresultsofthreeassimilationexperimentsqualitativelyandquantitatively.TheresultsshowthattheWRFmodelcombinedwiththeGSIassimilationsys⁃temcansimulatetherainstormwell.Forthe21-hcumulativeprecipitationforecast,assimilatingconventionalobservationdatacanbetterim⁃provethetrendofrainbeltandthefallareaoftherainstorm.Theassimilatedradardatashowedbetterperformanceinprecipitationintensity,rainstormrangeandthelighttomoderaterainforecast,TheaverageETSscoreofthelighttomoderaterainwasincreasedby0.05.Assimila⁃tionofboththeconventionalobservationandradardataimprovedETS,POD,FARandBIASscoresforheavyrain.Forthe12-hcumulativeprecipitationforecast,thesimulationperformanceoftheprecipitationtrendisthebestwiththeassimilationofradardata,andtheexperiment文影,封彩云,余莲.2023.不同资料同化对四川一次暴雨过程数值模拟的对比分析[J].暴雨灾害,42(3):260-272.WENYing,FENGCai-yun,YULian.2023.ComparativeanalysisofsimulationofaheavyraininSichuanProvincewithdifferentdataassimilation[J].Torrenti...
