Observation bias correction with an ensemble Kalman filter |
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Authors: | ELANA J. FERTIG ,SEUNG-JONG BAEK,BRIAN R. HUNT,EDWARD OTT,ISTVAN SZUNYOGH,JOSÉ A. ARAVÉ QUIA,EUGENIA KALNAY,HONG LI, JUNJIE LIU |
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Affiliation: | Oncology Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA;;Institute for Research in Electronics and Applied Physics and Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA;;Institute for Physical Science and Technology and Department of Mathematics, University of Maryland, College Park, MD 20742, USA;;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, USA;;Department of Atmospheric and Ocean Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA;;Department of Atmospheric and Ocean Science and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA and Center for Weather Forecast and Climatic Studies, Brazilian Institute of Space Research, Cahoeira Paulista, San Paulo 12630, Brazil;;Shanghai Typhoon Institute, Shanghai, China;;Earth and Planetary Science Department, University of California, Berkeley, CA 94720, USA |
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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. |
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