Multivariate localization methods for ensemble Kalman filtering

Handle URI:
http://hdl.handle.net/10754/584235
Title:
Multivariate localization methods for ensemble Kalman filtering
Authors:
Roh, S.; Jun, M.; Szunyogh, I.; Genton, Marc G. ( 0000-0001-6467-2998 )
Abstract:
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Multivariate localization methods for ensemble Kalman filtering 2015, 22 (6):723 Nonlinear Processes in Geophysics
Publisher:
Copernicus GmbH
Journal:
Nonlinear Processes in Geophysics
Issue Date:
3-Dec-2015
DOI:
10.5194/npg-22-723-2015
Type:
Article
ISSN:
1607-7946
Additional Links:
http://www.nonlin-processes-geophys.net/22/723/2015/
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorRoh, S.en
dc.contributor.authorJun, M.en
dc.contributor.authorSzunyogh, I.en
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2015-12-20T10:59:10Zen
dc.date.available2015-12-20T10:59:10Zen
dc.date.issued2015-12-03en
dc.identifier.citationMultivariate localization methods for ensemble Kalman filtering 2015, 22 (6):723 Nonlinear Processes in Geophysicsen
dc.identifier.issn1607-7946en
dc.identifier.doi10.5194/npg-22-723-2015en
dc.identifier.urihttp://hdl.handle.net/10754/584235en
dc.description.abstractIn ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.en
dc.language.isoenen
dc.publisherCopernicus GmbHen
dc.relation.urlhttp://www.nonlin-processes-geophys.net/22/723/2015/en
dc.rightsArchived with thanks to Nonlinear Processes in Geophysics. This work is distributed under the Creative Commons Attribution 3.0 License.en
dc.titleMultivariate localization methods for ensemble Kalman filteringen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalNonlinear Processes in Geophysicsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, TX 77843-3143, USAen
dc.contributor.institutionDepartment of Atmospheric Sciences, Texas A&M University, College Station, TX 77843-3148, USAen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorGenton, Marc G.en
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