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Sequential Data Assimilation Techniques in Oceanography

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  • Laurent Bertino
  • Geir Evensen
  • Hans Wackernagel

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. Nous recensons quelques développements récents de techniques d'assimilation séquentielle utilisées en océanographie, qui intègrent des observations spatio‐temporelles dans des modèles numériques décrivant des dynamiques physiques et écologiques. Les aspects théoriques allant du cas simple d'une dynamique linéaire au cas général d'une dynamique nonlinéaire sont examinées du point de vue géostatistique. Des méthodes usuelles dérivées du filtre de Kalman sont présentées en partant du cas le moins complexe au cas le plus général et des perspectives pour une estimation non‐linéaire sont discutées. Nous présentons en outre une extension du filtre de Kalman d'ensemble au cas de variables ayant subi une transformation gaussienne et nous l'illustrons en utilisant un modèle écologique simplifié. Les méthodes exposées sont conçues pour prédire dans une région géographique avec une haute résolution spatiale sous la contrainte pratique que les temps de calcul soient suffisamment courts pour obtenir une prédiction avant l'heure. Ainsi l'article se concentre sur des méthodes couramment utilisées et de grande efficacité calculatoire.

Suggested Citation

  • Laurent Bertino & Geir Evensen & Hans Wackernagel, 2003. "Sequential Data Assimilation Techniques in Oceanography," International Statistical Review, International Statistical Institute, vol. 71(2), pages 223-241, August.
  • Handle: RePEc:bla:istatr:v:71:y:2003:i:2:p:223-241
    DOI: 10.1111/j.1751-5823.2003.tb00194.x
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    References listed on IDEAS

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    1. Kanti Mardia & Colin Goodall & Edwin Redfern & Francisco Alonso, 1998. "The Kriged Kalman filter," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 7(2), pages 217-282, December.
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    1. Peter Guttorp, 2003. "Environmental Statistics—A Personal View," International Statistical Review, International Statistical Institute, vol. 71(2), pages 169-179, August.
    2. Louca, Stilianos & Doebeli, Michael, 2016. "Reaction-centric modeling of microbial ecosystems," Ecological Modelling, Elsevier, vol. 335(C), pages 74-86.

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