Multivariate localization methods for ensemble Kalman filtering

Handle URI:
http://hdl.handle.net/10754/561066
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 (entry-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 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, 2 (3):833 Nonlinear Processes in Geophysics Discussions
Journal:
Nonlinear Processes in Geophysics Discussions
Issue Date:
8-May-2015
DOI:
10.5194/npgd-2-833-2015
Type:
Article
ISSN:
2198-5634
Additional Links:
http://www.nonlin-processes-geophys-discuss.net/2/833/2015/
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.accessioned2015-07-27T12:21:03Zen
dc.date.available2015-07-27T12:21:03Zen
dc.date.issued2015-05-08en
dc.identifier.citationMultivariate localization methods for ensemble Kalman filtering 2015, 2 (3):833 Nonlinear Processes in Geophysics Discussionsen
dc.identifier.issn2198-5634en
dc.identifier.doi10.5194/npgd-2-833-2015en
dc.identifier.urihttp://hdl.handle.net/10754/561066en
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 (entry-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 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.relation.urlhttp://www.nonlin-processes-geophys-discuss.net/2/833/2015/en
dc.rightsArchived with thanks to Nonlinear Processes in Geophysics Discussions. 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 Geophysics Discussionsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, TX, USAen
dc.contributor.institutionDepartment of Atmospheric Sciences, Texas A&M University, College Station, TX, USAen
kaust.authorGenton, Marc G.en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.