Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter （EnKF）-中国Betway体育网页登陆

摘要 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.

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Estimating the Soil Moisture Profile by Assimilating Near-Surface Observations with the Ensemble Kalman Filter （EnKF）

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