Ensemble Kalman Filtering with a Divided State-Space Strategy for Coupled Data Assimilation Problems

Handle URI:
http://hdl.handle.net/10754/347009
Title:
Ensemble Kalman Filtering with a Divided State-Space Strategy for Coupled Data Assimilation Problems
Authors:
Luo, Xiaodong; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
This study considers the data assimilation problem in coupled systems, which consists of two components (subsystems) interacting with each other through certain coupling terms. A straightforward way to tackle the assimilation problem in such systems is to concatenate the states of the subsystems into one augmented state vector, so that a standard ensemble Kalman filter (EnKF) can be directly applied. This work presents a divided state-space estimation strategy, in which data assimilation is carried out with respect to each individual subsystem, involving quantities from the subsystem itself and correlated quantities from other coupled subsystems. On top of the divided state-space estimation strategy, the authors also consider the possibility of running the subsystems separately. Combining these two ideas, a few variants of the EnKF are derived. The introduction of these variants is mainly inspired by the current status and challenges in coupled data assimilation problems and thus might be of interest from a practical point of view. Numerical experiments with a multiscale Lorenz 96 model are conducted to evaluate the performance of these variants against that of the conventional EnKF. In addition, specific for coupled data assimilation problems, two prototypes of extensions of the presented methods are also developed in order to achieve a trade-offbetween efficiency and accuracy.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
Ensemble Kalman Filtering with a Divided State-Space Strategy for Coupled Data Assimilation Problems* 2014, 142 (12):4542 Monthly Weather Review
Publisher:
American Meteorological Society
Journal:
Monthly Weather Review
Issue Date:
Dec-2014
DOI:
10.1175/MWR-D-13-00402.1
ARXIV:
arXiv:1408.4236
Type:
Article
ISSN:
0027-0644; 1520-0493
Additional Links:
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-13-00402.1; http://arxiv.org/abs/1408.4236
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLuo, Xiaodongen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-03-23T11:44:39Zen
dc.date.available2015-03-23T11:44:39Zen
dc.date.issued2014-12en
dc.identifier.citationEnsemble Kalman Filtering with a Divided State-Space Strategy for Coupled Data Assimilation Problems* 2014, 142 (12):4542 Monthly Weather Reviewen
dc.identifier.issn0027-0644en
dc.identifier.issn1520-0493en
dc.identifier.doi10.1175/MWR-D-13-00402.1en
dc.identifier.urihttp://hdl.handle.net/10754/347009en
dc.description.abstractThis study considers the data assimilation problem in coupled systems, which consists of two components (subsystems) interacting with each other through certain coupling terms. A straightforward way to tackle the assimilation problem in such systems is to concatenate the states of the subsystems into one augmented state vector, so that a standard ensemble Kalman filter (EnKF) can be directly applied. This work presents a divided state-space estimation strategy, in which data assimilation is carried out with respect to each individual subsystem, involving quantities from the subsystem itself and correlated quantities from other coupled subsystems. On top of the divided state-space estimation strategy, the authors also consider the possibility of running the subsystems separately. Combining these two ideas, a few variants of the EnKF are derived. The introduction of these variants is mainly inspired by the current status and challenges in coupled data assimilation problems and thus might be of interest from a practical point of view. Numerical experiments with a multiscale Lorenz 96 model are conducted to evaluate the performance of these variants against that of the conventional EnKF. In addition, specific for coupled data assimilation problems, two prototypes of extensions of the presented methods are also developed in order to achieve a trade-offbetween efficiency and accuracy.en
dc.publisherAmerican Meteorological Societyen
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-13-00402.1en
dc.relation.urlhttp://arxiv.org/abs/1408.4236en
dc.rights© 2015 American Meteorological Society Privacy Policy and Disclaimer Headquarters: 45 Beacon Street Boston, MA 02108-3693 DC Office: 1120 G Street, NW, Suite 800 Washington DC, 20005-3826 amsinfo@ametsoc.org Phone: 617-227-2425 Fax: 617-742-8718 Allen Press, Inc. assists in the online publication of AMS journalsen
dc.titleEnsemble Kalman Filtering with a Divided State-Space Strategy for Coupled Data Assimilation Problemsen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalMonthly Weather Reviewen
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:1408.4236en
kaust.authorHoteit, Ibrahimen
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