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Original Research Papers

Local ensemble Kalman filtering in the presence of model bias

Authors:

Seung-Jong Baek ,

Institute for Research in Electronics and Applied Physics, and Department of Electrical and Computer Engineering, 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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Eugenia Kalnay,

Institute for Physical Science and Technology, and Department of Atmospheric and Oceanic Science, University of Maryland,College Park, MD, US
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Edward Ott,

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

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

We modify the local ensemble Kalman filter (LEKF) to incorporate the effect of forecast model bias. The method is based on augmentation of the atmospheric state by estimates of the model bias, and we consider different ways of modeling (i.e. parameterizing) the model bias.We evaluate the effectiveness of the proposed augmented state ensemble Kalman filter through numerical experiments incorporating various model biases into the model of Lorenz and Emanuel. Our results highlight the critical role played by the selection of a good parameterization model for representing the form of the possible bias in the forecast model. In particular, we find that forecasts can be greatly improved provided that a good model parameterizing the model bias is used to augment the state in the Kalman filter.

How to Cite: Baek, S.-J., Hunt, B.R., Kalnay, E., Ott, E. and Szunyogh, I., 2006. Local ensemble Kalman filtering in the presence of model bias. Tellus A: Dynamic Meteorology and Oceanography, 58(3), pp.293–306. DOI: http://doi.org/10.1111/j.1600-0870.2006.00178.x
  Published on 01 Jan 2006
 Accepted on 5 Dec 2005            Submitted on 6 Apr 2005

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