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Original Research Papers

Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model

Authors:

Istvan Szunyogh ,

Department of Meteorology and 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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G. Gyarmati,

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

Institute for Physical Science and Technology, 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,

Department of Meteorology and Institute for Physical Science and Technology, 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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James A. Yorke

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

The accuracy and computational efficiency of the recently proposed local ensemble Kalman filter (LEKF) data assimilation scheme is investigated on a state-of-the-art operational numerical weather prediction model using simulated observations. The model selected for this purpose is the T62 horizontal- and 28-level vertical-resolution version of the Global Forecast System (GFS) of the National Center for Environmental Prediction. The performance of the data assimilation system is assessed for different configurations of the LEKF scheme. It is shown that a modest size (40-member) ensemble is sufficient to track the evolution of the atmospheric state with high accuracy. For this ensemble size, the computational time per analysis is less than 9 min on a cluster of PCs. The analyses are extremely accurate in the mid-latitude storm track regions. The largest analysis errors, which are typically much smaller than the observational errors, occur where parametrized physical processes play important roles. Because these are also the regions where model errors are expected to be the largest, limitations of a real-data implementation of the ensemble-based Kalman filter may be easily mistaken for model errors. In light of these results, the importance of testing the ensemble-based Kalman filter data assimilation systems on simulated observations is stressed.

How to Cite: Szunyogh, I., Kostelich, E.J., Gyarmati, G., Patil, D.J., Hunt, B.R., Kalnay, E., Ott, E. and Yorke, J.A., 2005. Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model. Tellus A: Dynamic Meteorology and Oceanography, 57(4), pp.528–545. DOI: http://doi.org/10.3402/tellusa.v57i4.14721
  Published on 01 Jan 2005
 Accepted on 10 Jan 2005            Submitted on 29 Jun 2004

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