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基于GWR模型的长江流域TRMM数据降尺度 Title:DownscalingofTRMMDataintheYangtzeRiverBasinbasedontheGWRModel Abstract: AccurateassessmentofprecipitationpatternsandspatialdistributioniscrucialforwaterresourcesmanagementintheYangtzeRiverBasin.However,thespatialresolutionofsatellite-basedprecipitationdatasetssuchastheTropicalRainfallMeasuringMission(TRMM)isoftencoarse,makingitdifficulttocapturelocalizedvariations.ThisstudyaimstodownscaleTRMMdatausingtheGeographicallyWeightedRegression(GWR)modeltoimprovethespatialresolutionandprovidemoredetailedinformationonprecipitationpatternsintheYangtzeRiverBasin. 1.Introduction: TheYangtzeRiverBasinisoneofthemostimportantwaterresourcesregionsinChina,withhighspatialvariabilityinprecipitationduetoitsdiversetopographyandclimate.However,theavailabilityofhigh-resolutionprecipitationdataislimited,hinderingwaterresourcemanagementandhydrologicalmodeling.Therefore,downscalingcoarse-resolutionsatelliteprecipitationdata,suchasTRMM,isessentialtoimprovetheaccuracyofthespatialdistributionofprecipitation. 2.Methodology: TheGeographicallyWeightedRegression(GWR)modelisaspatialstatisticaltechniquethatallowsfortheexplorationoflocalrelationshipsbetweenvariables.Inthisstudy,theGWRmodelisappliedtodownscaleTRMMprecipitationdataintheYangtzeRiverBasin.ThemodelistrainedusingobservedprecipitationvaluesfromanetworkofraingaugesasthedependentvariableandTRMMprecipitationastheindependentvariable.TheGWRmodelestimatesthelocalrelationshipbetweenthesevariables,capturingspatiallyvaryingrelationshipsandprovidingdownscaledprecipitationestimatesatahigherresolution. 3.Data: ThestudyutilizesTRMM3B43dataset,whichprovidesprecipitationestimatesataspatialresolutionof0.25degrees(~25km).RaingaugedatafromtheChinaMeteorologicalAdministration(CMA)areusedasthegroundtruthformodeltrainingandvalidation.Otherauxiliarydatasets,suchaselevationandlandcover,arealsoconsideredtoaccountfortheirpotentialinfluenceonprecipitationpatterns. 4.ResultsandDiscussion: TheGWRmodelisappliedtodownscaleTRMMprecipitationdataf