Application and comparison of Kalman filters for coastal ocean problems : an experiment with FVCOM


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dc.contributor.author Chen, Changsheng
dc.contributor.author Malanotte-Rizzoli, Paola
dc.contributor.author Wei, Jun
dc.contributor.author Beardsley, Robert C.
dc.contributor.author Lai, Zhigang
dc.contributor.author Xue, Pengfei
dc.contributor.author Lyu, Sangjun
dc.contributor.author Xu, Qichun
dc.contributor.author Qi, Jianhua
dc.contributor.author Cowles, Geoffrey W.
dc.date.accessioned 2010-05-26T16:03:08Z
dc.date.available 2010-05-26T16:03:08Z
dc.date.issued 2009-05-13
dc.identifier.citation Journal of Geophysical Research 14 (2009): C05011 en_US
dc.identifier.uri http://hdl.handle.net/1912/3531
dc.description Author 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.abstract Twin 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.sponsorship P. 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.mimetype application/x-tex
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
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dc.language.iso en_US en_US
dc.publisher American Geophysical Union en_US
dc.relation.uri http://dx.doi.org/10.1029/2007JC004548
dc.subject Kalman filters en_US
dc.subject Data assimilation en_US
dc.subject Ocean modeling en_US
dc.title Application and comparison of Kalman filters for coastal ocean problems : an experiment with FVCOM en_US
dc.type Article en_US
dc.identifier.doi 10.1029/2007JC004548

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