International Statistical Review

Sequential Data Assimilation Techniques in Oceanography

Laurent Bertino,, Geir Evensen, and Hans Wackernagel

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Abstract

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.

Article information

Source
Internat. Statist. Rev., Volume 71, Number 2 (2003), 223-241.

Dates
First available in Project Euclid: 18 November 2003

Permanent link to this document
https://projecteuclid.org/euclid.isr/1069172299

Zentralblatt MATH identifier
1114.62364

Keywords
Data assimilation Geostatistics Kalman filter Non-linear dynamical systems State-space models Ecological model

Citation

Bertino,, Laurent; Evensen, Geir; Wackernagel, Hans. Sequential Data Assimilation Techniques in Oceanography. Internat. Statist. Rev. 71 (2003), no. 2, 223--241. https://projecteuclid.org/euclid.isr/1069172299


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