简介：这研究检验与一个整体Kalman过滤器(EnKF)联合确定的四维的变化吸收系统(4DVAR)为数据吸收生产一条优异混合途径的性能。当在阻止过滤器分叉利用4DVAR时，从使用州依赖者的不确定性的联合吸收计划(E4DVAR)好处由EnKF提供了：4DVAR分析通过费用的最小化生产以后的最大的可能性答案整体不安关于被转变的功能，和产生整体分析能为下一个吸收周期并且作为整体预报的一个基础向前被宣传。这条联合途径的可行性和有效性与模仿的观察在一个理想化的模型被表明。E4DVAR能够在完美模型、有瑕疵模型的情形下面超过4DVAR和EnKF，这被发现。联合计划的性能比为标准EnKF或4DVAR实现的那些对整体尺寸或吸收窗口长度也不太敏感。

简介：IntheEnsembleKalmanFilter(EnKF)dataassimilation-predictionsystem,mostofthecomputationtimeisspentonthepredictionrunsofensemblemembers.Alimitedorsmallensemblesizedoesreducethecomputationalcost,butanexcessivelysmallensemblesizeusuallyleadstofilterdivergence,especiallywhentherearemodelerrors.InordertoimprovetheefficiencyoftheEnKFdataassimilation-predictionsystemandpreventitagainstfilterdivergence,atime-expandedsamplingapproachforEnKFbasedontheWRF(WeatherResearchandForecasting)modelisusedtoassimilatesimulatedsoundingdata.TheapproachsamplesaseriesofperturbedstatevectorsfromNbmemberpredictionrunsnotonlyattheanalysistime(astheconventionalapproachdoes)butalsoatequallyseparatedtimelevels(timeintervalis△t)beforeandaftertheanalysistimewithMtimes.Alltheabovesampledstatevectorsareusedtoconstructtheensembleandcomputethebackgroundcovariancefortheanalysis,sotheensemblesizeisincreasedfromNbtoNb+2M×Nb=(1+2M)×Nb)withoutincreasingthenumberofpredictionruns(itisstillNb).Thisreducesthecomputationalcost.Aseriesofexperimentsareconductedtoinvestigatetheimpactof△t(thetimeintervaloftime-expandedsampling)andM(themaximumsamplingtimes)ontheanalysis.TheresultsshowthatiftandMareproperlyselected,thetime-expandedsamplingapproachachievesthesimilareffecttothatfromtheconventionalapproachwithanensemblesizeof(1+2M)×Nb,butthenumberofpredictionrunsisgreatlyreduced.

简介：InthispaperweinvestigatetheimpactoftheAtmosphericInfra-RedSounder(AIRS)temperatureretrievalsondataassimilationandtheresultingforecastsusingthefour-dimensionalLocalEnsembleTransformKalmanFilter(LETKF)dataassimilationschemeandareducedresolutionversionoftheNCEPGlobalForecastSystem(GFS).OurresultsindicatethattheAIRStemperatureretrievalshaveasignificantandconsistentpositiveimpactintheSouthernHemisphericextratropicsonbothanalysesandforecasts,whichisfoundnotonlyinthetemperaturefieldbutalsoinothervariables.IntropicsandtheNorthernHemisphericextratropicstheseimpactsaresmaller,butarestillgenerallypositiveorneutral.

简介：Thepaperinvestigatestheabilitytoretrievethetruesoilmoistureprofilebyassimilatingnear-surfacesoilmoistureintoasoilmoisturemodelwithanensembleKalmanfilter(EnKF)assimilationscheme,includingtheeffectofensemblesize,updateintervalandnonlinearitiesintheprofileretrieval,therequiredtimeforfullretrievalofthesoilmoistureprofiles,andthepossibleinfluenceofthedepthofthesoilmoistureobservation.Thesequestionsareaddressedbyadesktopstudyusingsyntheticdata.The'true'soilmoistureprofilesaregeneratedfromthesoilmoisturemodelundertheboundaryconditionof0.5cmd-1evaporation.Totesttheassimilationschemes,themodelisinitializedwithapoorinitialguessofthesoilmoistureprofile,anddifferentensemblesizesaretestedshowingthatanensembleof40membersisenoughtorepresentthecovarianceofthemodelforecasts.Alsocomparedaretheresultswiththosefromthedirectinsertionassimilationscheme,showingthattheEnKFissuperiortothedirectinsertionassimilationscheme,forhourlyobservations,withretrievalofthesoilmoistureprofilebeingachievedin16hascomparedto12daysormore.Fordailyobservations,thetruesoilmoistureprofileisachievedinabout15dayswiththeEnKF,butitisimpossibletoapproximatethetruemoisturewithin18daysbyusingdirectinsertion.ItisalsofoundthatobservationdepthdoesnothaveasignificanteffectonprofileretrievaltimefortheEnKF.Thenonlinearitieshavesomenegativeinfluenceontheoptimalestimatesofsoilmoistureprofilebutnotveryseriously.