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While a model parameter estimation, which allows parameters to change spatio‐temporally, shows promise in quantifying and mitigating systematic model errors, the estimation of the spatio‐temporally distributed model parameters has been practically challenging. Here we present an efficient and practical method to estimate time‐varying parameters in high‐dimensional spaces. In our proposed method, Hybrid Offline and Online Parameter Estimation with ensemble Kalman filtering (HOOPE‐EnKF), model parameters estimated by EnKF are constrained by results of offline batch optimization, in which the posterior distribution of model parameters is obtained by comparing simulated and observed climatological variables. HOOPE‐EnKF outperforms the original EnKF in synthetic experiments using a two‐scale Lorenz96 model and a simple global general circulation model. One advantage of HOOPE‐EnKF over traditional EnKFs is that its performance is not greatly affected by inflation factors for model parameters, thus eliminating the need for extensive tuning of inflation factors. We thoroughly discuss the potential of HOOPE‐EnKF as a practical method for improving parameterizations of process‐based models and prediction in real‐world applications such as numerical weather prediction.</jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1420845751138972160","@type":"Researcher","personIdentifier":[{"@type":"KAKEN_RESEARCHERS","@value":"30784475"},{"@type":"NRID","@value":"1000030784475"},{"@type":"NRID","@value":"9000415323861"},{"@type":"NRID","@value":"9000250466653"},{"@type":"NRID","@value":"9000405666379"},{"@type":"NRID","@value":"9000399330348"},{"@type":"NRID","@value":"9000405693837"},{"@type":"NRID","@value":"9000410756753"},{"@type":"RESEARCHMAP","@value":"https://researchmap.jp/ysawada"}],"foaf:name":[{"@value":"Yohei Sawada"}],"jpcoar:affiliationName":[{"@value":"Institute of Engineering Innovation Graduate School of Engineering the University of Tokyo  Tokyo Japan"},{"@value":"Meteorological Research Institute Japan Meteorological Agency  Tsukuba Japan"}]},{"@id":"https://cir.nii.ac.jp/crid/1380021390765303937","@type":"Researcher","foaf:name":[{"@value":"Le Duc"}],"jpcoar:affiliationName":[{"@value":"Institute of Engineering Innovation Graduate School of Engineering the University of Tokyo  Tokyo Japan"},{"@value":"Meteorological Research Institute Japan Meteorological Agency  Tsukuba Japan"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"19422466"},{"@type":"EISSN","@value":"19422466"}],"prism:publicationName":[{"@value":"Journal of Advances in Modeling Earth Systems"}],"dc:publisher":[{"@value":"American Geophysical Union 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sciences"},{"@id":"https://cir.nii.ac.jp/all?q=GC1-1581","dc:title":"GC1-1581"},{"@id":"https://cir.nii.ac.jp/all?q=parameter%20estimation","dc:title":"parameter estimation"},{"@id":"https://cir.nii.ac.jp/all?q=Oceanography","dc:title":"Oceanography"},{"@id":"https://cir.nii.ac.jp/all?q=data%20assimilation","dc:title":"data assimilation"},{"@id":"https://cir.nii.ac.jp/all?q=ensemble%20Kalman%20filter","dc:title":"ensemble Kalman filter"},{"@id":"https://cir.nii.ac.jp/all?q=Statistics%20-%20Methodology","dc:title":"Statistics - Methodology"},{"@id":"https://cir.nii.ac.jp/all?q=GB3-5030","dc:title":"GB3-5030"},{"@id":"https://cir.nii.ac.jp/all?q=Geophysics%20(physics.geo-ph)","dc:title":"Geophysics 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