Advances in Atmospheric Sciences

, Volume 26, Issue 1, pp 154–160 | Cite as

An adaptive estimation of forecast error covariance parameters for Kalman filtering data assimilation



An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing −2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers’ equation model) is used to demonstrate the efficacy of the proposed approach.

Key words

data assimilation Kalman filter ensemble prediction estimation 


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Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag GmbH 2009

Authors and Affiliations

  1. 1.National Institute of Water and Atmospheric ResearchWellingtonNew Zealand

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