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 journalArticle

23 Citations (Scopus)

Abstract

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 C: Oceans
Volume104
Issue numberC4
StatePublished - Apr 15 1999

Fingerprint

ocean models
assimilation
Kalman filters
Kalman filter
regularity
data assimilation
wavelet
regression analysis
TOPEX
altimetry
shallow-water equation
Extended Kalman filters
ocean
estimates
shallow water
Parameterization
microgravity
approximation
parameterization
Gravitation

ASJC Scopus subject areas

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

Cite this

Spatial regression and multiscale approximations for sequential data assimilation in ocean models. / Chin, Toshio M.; Mariano, Arthur J; Chassignet, Eric P.

In: Journal of Geophysical Research C: Oceans, Vol. 104, No. C4, 1998JC900075, 15.04.1999, p. 7991-8014.

Research output: Contribution to journalArticle

@article{2c2c326bb78f4ea89fa59d3f8e20baf1,
title = "Spatial regression and multiscale approximations for sequential data assimilation in ocean models",
abstract = "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.",
author = "Chin, {Toshio M.} and Mariano, {Arthur J} and Chassignet, {Eric P.}",
year = "1999",
month = "4",
day = "15",
language = "English (US)",
volume = "104",
pages = "7991--8014",
journal = "Journal of Geophysical Research: Oceans",
issn = "2169-9275",
publisher = "Wiley-Blackwell",
number = "C4",

}

TY - JOUR

T1 - Spatial regression and multiscale approximations for sequential data assimilation in ocean models

AU - Chin, Toshio M.

AU - Mariano, Arthur J

AU - Chassignet, Eric P.

PY - 1999/4/15

Y1 - 1999/4/15

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0033560571&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033560571&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0033560571

VL - 104

SP - 7991

EP - 8014

JO - Journal of Geophysical Research: Oceans

JF - Journal of Geophysical Research: Oceans

SN - 2169-9275

IS - C4

M1 - 1998JC900075

ER -