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Original Research Papers

A local ensemble Kalman filter for atmospheric data assimilation

Authors:

Edward Ott,

Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, MD, US
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Brian R. Hunt,

Institute for Physical Science and Technology and Department of Mathematics, University of Maryland, College Park, MD, US
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Istvan Szunyogh ,

Institute for Physical Science and Technology and Department of Meteorology, University of Maryland, College Park, MD, US
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Aleksey V. Zimin,

Institute for Physical Science and Technology, University of Maryland, College Park, MD, US
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Eric J. Kostelich,

Department of Mathematics and Statistics, Arizona State University, AZ, US
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Matteo Corazza,

Department of Meteorology, University of Maryland, College Park, MD, US
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Eugenia Kalnay,

Department of Meteorology, University of Maryland, College Park, MD, US
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D. J. Patil,

Institute for Physical Science and Technology and Department of Mathematics, University of Maryland, College Park, MD, US
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James A. Yorke

Institute for Physical Science and Technology and Department of Mathematics, University of Maryland, College Park, MD, US
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Abstract

In this paper, we introduce a new, local formulation of the ensemble Kalman filter approach for atmospheric data assimilation. Our scheme is based on the hypothesis that, when the Earth’s surface is divided up into local regions of moderate size, vectors of the forecast uncertainties in such regions tend to lie in a subspace of much lower dimension than that of the full atmospheric state vector of such a region. Ensemble Kalman filters, in general, take the analysis resulting from the data assimilation to lie in the same subspace as the expected forecast error. Under our hypothesis the dimension of the subspace corresponding to local regions is low. This is used in our scheme to allow operations only on relatively low-dimensional matrices. The data assimilation analysis is performed locally in a manner allowing massively parallel computation to be exploited. The local analyses are then used to construct global states for advancement to the next forecast time. One advantage, which may take on more importance as ever-increasing amounts of remotely-sensed satellite data become available, is the favorable scaling of the computational cost of our method with increasing data size, as compared to other methods that assimilate data sequentially. The method, its potential advantages, properties, and implementation requirements are illustrated by numerical experiments on the Lorenz-96 model. It is found that accurate analysis can be achieved at a cost which is very modest compared to that of a full global ensemble Kalman filter.

How to Cite: Ott, E., Hunt, B.R., Szunyogh, I., Zimin, A.V., Kostelich, E.J., Corazza, M., Kalnay, E., Patil, D.J. and Yorke, J.A., 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus A: Dynamic Meteorology and Oceanography, 56(5), pp.415–428. DOI: http://doi.org/10.3402/tellusa.v56i5.14462
  Published on 01 Jan 2004
 Accepted on 13 Apr 2004            Submitted on 10 Dec 2003

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