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Original Research Papers

Assimilating non-local observations with a local ensemble Kalman filter

Authors:

Elana J. Fertig ,

Institute for Physical Science and Technology and Department of Mathematics, 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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Edward Ott,

Institute for Research in Electronics and Applied Physics, Department of Electrical and Computer Engineering and Department of Physics, University of Maryland, College Park, MD, US
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Istvan Szunyogh

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

Many ensemble data assimilation schemes utilize spatial localization so that a small ensemble can capture the unstable degrees of freedom in the model state. These local ensemble-based schemes typically allow the analysis at a given location to depend only on observations near that location. Meanwhile, the location of satellite observations cannot be pinpointed in the same manner as conventional observations. We propose a technique to update the state at a given location by assimilating satellite radiance observations that are strongly correlated to the model state there. For satellite retrievals, we propose incorporating the observation error covariance matrix and selecting the retrievals that have errors correlated to observations near the location to be updated. Our selection techniques improve the analysis obtained when assimilating simulated satellite observations with a seven-layer primitive equation model, the SPEEDY model.

How to Cite: Fertig, E.J., Hunt, B.R., Ott, E. and Szunyogh, I., 2007. Assimilating non-local observations with a local ensemble Kalman filter. Tellus A: Dynamic Meteorology and Oceanography, 59(5), pp.719–730. DOI: http://doi.org/10.1111/j.1600-0870.2007.00260.x
  Published on 01 Jan 2007
 Accepted on 18 May 2007            Submitted on 20 Oct 2006

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