Application and comparison of Kalman filters for coastal ocean problems: An experiment with FVCOM
Name
2007JC004548.pdf
Size
2.68 MB
Format
Adobe PDF
Checksum (MD5)
390999fb16d584e416db4513d5b2af7b
Author(s)
Chen, Changsheng
Wei, Jun
Beardsley, Robert C.
Lai, Zhigang
Xue, Pengfei
Lyu, Sangjun
Xu, Qichun
Qi, Jianhua
Cowles, Geoffrey W.
Rizzoli, Paola M
Date Issued
May 2009
Journal
Journal of Geophysical Research
Publisher
American Geophysical Union
Citation
Chen, C., P. Malanotte-Rizzoli, J. Wei, R. C. Beardsley, Z. Lai, P. Xue, S. Lyu, Q. Xu, J. Qi, and G. W. Cowles (2009), Application and comparison of Kalman filters for coastal ocean problems: An experiment with FVCOM, J. Geophys. Res., 114, C05011, doi:10.1029/2007JC004548. ©2009 American Geophysical Union
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2007JC004548
