Publication Date:
2022-05-26
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.
Description:
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.
Description:
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.
Keywords:
Kalman filters
;
Data assimilation
;
Ocean modeling
Repository Name:
Woods Hole Open Access Server
Type:
Article
Format:
application/x-tex
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application/pdf
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