Spatial regression and multiscale approximations for sequential data assimilation in ocean models

Toshio M. Chin, Arthur J. Mariano, Eric P. Chassignet

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Effects of spatial regularity and locality assumptions in the extended Kalman filter are examined for oceanic data assimilation problems. Biorthogonal wavelet bases are used to implement spatial regularity through multiscale approximations, while a Markov random field (MRF) is used to impose locality through spatial regression. Both methods are shown to approximate the optimal Kalman filter estimates closely, although the stability of the estimates can be dependent on the choice of basis functions in the wavelet case. The observed filter performance is nearly constant over a wide range of values for the scalar weights (uncertainty variances) given to the model and data examined here. The MRF-based method, with its inhomogeneous and anisotropic covariance parameterization, has been shown to be particularly effective and stable in assimilation of simulated TOPEX/POSEIDON altimetry data into a reduced-gravity, shallow-water equation model.

Original languageEnglish (US)
Article number1998JC900075
Pages (from-to)7991-8014
Number of pages24
JournalJournal of Geophysical Research: Oceans
Issue numberC4
StatePublished - Apr 15 1999

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Atmospheric Science
  • Astronomy and Astrophysics
  • Oceanography


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