August 2003 Sequential Data Assimilation Techniques in Oceanography
Laurent Bertino,, Geir Evensen, Hans Wackernagel
Author Affiliations +
Internat. Statist. Rev. 71(2): 223-241 (August 2003).


We review recent developments of sequential data assimilation techniques used in oceanography to integrate spatio-temporal observations into numerical models describing physical and ecological dynamics. Theoretical aspects from the simple case of linear dynamics to the general case of nonlinear dynamics are described from a geostatistical point-of-view. Current methods derived from the Kalman filter are presented from the least complex to the most general and perspectives for nonlinear estimation by sequential importance resampling filters are discussed. Furthermore an extension of the ensemble Kalman filter to transformed Gaussian variables is presented and illustrated using a simplified ecological model. The described methods are designed for predicting over geographical regions using a high spatial resolution under the practical constraint of keeping computing time sufficiently low to obtain the prediction before the fact. Therefore the paper focuses on widely used and computationally efficient methods.


Download Citation

Laurent Bertino,. Geir Evensen. Hans Wackernagel. "Sequential Data Assimilation Techniques in Oceanography." Internat. Statist. Rev. 71 (2) 223 - 241, August 2003.


Published: August 2003
First available in Project Euclid: 18 November 2003

zbMATH: 1114.62364

Keywords: data assimilation , Ecological model , Geostatistics , Kalman filter , Non-linear dynamical systems , state-space models

Rights: Copyright © 2003 International Statistical Institute


This article is only available to subscribers.
It is not available for individual sale.

Vol.71 • No. 2 • August 2003
Back to Top