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Original Research Papers

Observation bias correction with an ensemble Kalman filter

Authors:

Elana J. Fertig ,

Oncology Biostatistics, Johns Hopkins University, Baltimore, MD 21205, US
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Seung-Jong Baek,

Institute for Research in Electronics and Applied Physics and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, US
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Brian R. Hunt,

Institute for Physical Science and Technology and Department of Mathematics, University of Maryland, College Park, MD 20742, 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 20742, US
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Istvan Szunyogh,

Department of Atmospheric and Ocean Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, US
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José A. Aravéquia,

Department of Atmospheric and Ocean Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, US; Center for Weather Forecast and Climatic Studies, Brazilian Institute of Space Research, Cahoeira Paulista, San Paulo 12630, BR
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Eugenia Kalnay,

Department of Atmospheric and Ocean Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, US
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Hong Li,

Shanghai Typhoon Institute, Shanghai, CN
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Junjie Liu

Earth and Planetary Science Department, University of California, Berkeley, CA 94720, US
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Abstract

This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases. Our approach is to use state-space augmentation to estimate satellite biases as part of the ensemble data assimilation procedure.We illustrate our approach by applying it to a particular ensemble scheme—the local ensemble transform Kalman filter (LETKF)—to assimilate simulated biased atmospheric infrared sounder brightness temperature observations from 15 channels on the simplified parameterizations, primitive-equation dynamics (SPEEDY) model. The scheme we present successfully reduces both the observation bias and analysis error in perfect-model simulations.

How to Cite: Fertig, E.J., Baek, S.-J., Hunt, B.R., Ott, E., Szunyogh, I., Aravéquia, J.A., Kalnay, E., Li, H. and Liu, J., 2009. Observation bias correction with an ensemble Kalman filter. Tellus A: Dynamic Meteorology and Oceanography, 61(2), pp.210–226. DOI: http://doi.org/10.1111/j.1600-0870.2008.00378.x
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Published on 01 Jan 2009.
Peer Reviewed

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