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dc.contributor.authorChen, Changsheng
dc.contributor.authorMalanotte-Rizzoli, Paola
dc.contributor.authorWei, Jun
dc.contributor.authorBeardsley, Robert C.
dc.contributor.authorLai, Zhigang
dc.contributor.authorXue, Pengfei
dc.contributor.authorLyu, Sangjun
dc.contributor.authorXu, Qichun
dc.contributor.authorQi, Jianhua
dc.contributor.authorCowles, Geoffrey W.
dc.date.accessioned2010-05-26T16:03:08Z
dc.date.available2010-05-26T16:03:08Z
dc.date.issued2009-05-13
dc.identifier.citationJournal of Geophysical Research 14 (2009): C05011en_US
dc.identifier.urihttp://hdl.handle.net/1912/3531
dc.descriptionAuthor Posting. © American Geophysical Union, 2009. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 114 (2009): C05011, doi:10.1029/2007JC004548.en_US
dc.description.abstractTwin experiments were made to compare the reduced rank Kalman filter (RRKF), ensemble Kalman filter (EnKF), and ensemble square-root Kalman filter (EnSKF) for coastal ocean problems in three idealized regimes: a flat bottom circular shelf driven by tidal forcing at the open boundary; an linear slope continental shelf with river discharge; and a rectangular estuary with tidal flushing intertidal zones and freshwater discharge. The hydrodynamics model used in this study is the unstructured grid Finite-Volume Coastal Ocean Model (FVCOM). Comparison results show that the success of the data assimilation method depends on sampling location, assimilation methods (univariate or multivariate covariance approaches), and the nature of the dynamical system. In general, for these applications, EnKF and EnSKF work better than RRKF, especially for time-dependent cases with large perturbations. In EnKF and EnSKF, multivariate covariance approaches should be used in assimilation to avoid the appearance of unrealistic numerical oscillations. Because the coastal ocean features multiscale dynamics in time and space, a case-by-case approach should be used to determine the most effective and most reliable data assimilation method for different dynamical systems.en_US
dc.description.sponsorshipP. Malanotte-Rizzoli and J. Wei were supported by the Office of Naval Research (ONR grant N00014-06-1- 0290); C. Chen and Q. Xu were supported by the U.S. GLOBEC/Georges Bank program (through NSF grants OCE-0234545, OCE-0227679, OCE- 0606928, OCE-0712903, OCE-0726851, and OCE-0814505 and NOAA grant NA-16OP2323), the NSF Arctic research grants ARC0712903, ARC0732084, and ARC0804029, and URI Sea Grant R/P-061; P. Xue was supported through the MIT Sea Grant 2006-RC-103; Z. Lai, J. Qi, and G. Cowles were supported through the Massachusetts Marine Fisheries Institute (NOAA grants NA04NMF4720332 and NA05NMF4721131); and R. Beardsley was supported through U.S. GLOBEC/Georges Bank NSF grant OCE-02227679, MIT Sea Grant NA06OAR1700019, and the WHOI Smith Chair in Coastal Oceanography.en_US
dc.format.mimetypeapplication/x-tex
dc.format.mimetypeapplication/pdf
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dc.language.isoen_USen_US
dc.publisherAmerican Geophysical Unionen_US
dc.relation.urihttp://dx.doi.org/10.1029/2007JC004548
dc.subjectKalman filtersen_US
dc.subjectData assimilationen_US
dc.subjectOcean modelingen_US
dc.titleApplication and comparison of Kalman filters for coastal ocean problems : an experiment with FVCOMen_US
dc.typeArticleen_US
dc.identifier.doi10.1029/2007JC004548


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