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dc.contributor.authorLuo, Xiaodong
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-03-23T11:44:39Z
dc.date.available2015-03-23T11:44:39Z
dc.date.issued2014-12
dc.identifier.citationEnsemble Kalman Filtering with a Divided State-Space Strategy for Coupled Data Assimilation Problems* 2014, 142 (12):4542 Monthly Weather Review
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493
dc.identifier.doi10.1175/MWR-D-13-00402.1
dc.identifier.urihttp://hdl.handle.net/10754/347009
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.
dc.publisherAmerican Meteorological Society
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-13-00402.1
dc.relation.urlhttp://arxiv.org/abs/1408.4236
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 journals
dc.titleEnsemble Kalman Filtering with a Divided State-Space Strategy for Coupled Data Assimilation Problems
dc.typeArticle
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.identifier.journalMonthly Weather Review
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.arxividarXiv:1408.4236
kaust.personHoteit, Ibrahim
refterms.dateFOA2014-12-28T00:00:00Z


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