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dc.contributor.authorLuo, X.
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2014-11-11T14:33:18Z
dc.date.available2014-11-11T14:33:18Z
dc.date.issued2012-10-03
dc.identifier.citationLuo X, Hoteit I (2012) Ensemble Kalman filtering with residual nudging. Tellus A 64. doi:10.3402/tellusa.v64i0.17130.
dc.identifier.issn02806495
dc.identifier.doi10.3402/tellusa.v64i0.17130
dc.identifier.urihttp://hdl.handle.net/10754/334641
dc.description.abstractCovariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.
dc.language.isoen
dc.publisherInforma UK Limited
dc.relation.urlhttp://arxiv.org/abs/1210.1318
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rightsArchived with thanks to Tellus, Series A: Dynamic Meteorology and Oceanography
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/
dc.subjectData assimilation
dc.subjectEnsemble kalman filter
dc.subjectResidual nudging
dc.subjectclimate prediction
dc.subjectcovariance analysis
dc.subjectdata assimilation
dc.subjectensemble forecasting
dc.subjectKalman filter
dc.subjectnumerical model
dc.titleEnsemble Kalman filtering with residual nudging
dc.typeArticle
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalTellus A
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionInternational Research Institute of Stavanger, Bergen, Norway
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
dc.identifier.arxivid1210.1318
kaust.personHoteit, Ibrahim
dc.versionv1
refterms.dateFOA2018-06-13T14:23:46Z
dc.date.published-online2012-10-03
dc.date.published-print2012-12
dc.date.posted2012-10-04


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This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.