Ensemble Kalman filtering with residual nudging

Handle URI:
http://hdl.handle.net/10754/334641
Title:
Ensemble Kalman filtering with residual nudging
Authors:
Luo, X.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Covariance 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.
KAUST Department:
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Citation:
Luo X, Hoteit I (2012) Ensemble Kalman filtering with residual nudging. Tellus A 64. doi:10.3402/tellusa.v64i0.17130.
Publisher:
Co-Action Publishing
Journal:
Tellus A
Issue Date:
3-Oct-2012
DOI:
10.3402/tellusa.v64i0.17130
ARXIV:
arXiv:1210.1318
Type:
Article
ISSN:
02806495
Additional Links:
http://arxiv.org/abs/1210.1318
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorLuo, X.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2014-11-11T14:33:18Z-
dc.date.available2014-11-11T14:33:18Z-
dc.date.issued2012-10-03en
dc.identifier.citationLuo X, Hoteit I (2012) Ensemble Kalman filtering with residual nudging. Tellus A 64. doi:10.3402/tellusa.v64i0.17130.en
dc.identifier.issn02806495en
dc.identifier.doi10.3402/tellusa.v64i0.17130en
dc.identifier.urihttp://hdl.handle.net/10754/334641en
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.en
dc.language.isoenen
dc.publisherCo-Action Publishingen
dc.relation.urlhttp://arxiv.org/abs/1210.1318en
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.en
dc.rightsArchived with thanks to Tellus, Series A: Dynamic Meteorology and Oceanographyen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/en
dc.subjectData assimilationen
dc.subjectEnsemble kalman filteren
dc.subjectResidual nudgingen
dc.subjectclimate predictionen
dc.subjectcovariance analysisen
dc.subjectdata assimilationen
dc.subjectensemble forecastingen
dc.subjectKalman filteren
dc.subjectnumerical modelen
dc.titleEnsemble Kalman filtering with residual nudgingen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Science and Technology, Thuwal, Saudi Arabiaen
dc.identifier.journalTellus Aen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionInternational Research Institute of Stavanger, Bergen, Norwayen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
dc.identifier.arxividarXiv:1210.1318en
kaust.authorHoteit, Ibrahimen
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