Abstract
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.
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Zheng, X. An adaptive estimation of forecast error covariance parameters for Kalman filtering data assimilation. Adv. Atmos. Sci. 26, 154–160 (2009). https://doi.org/10.1007/s00376-009-0154-5
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DOI: https://doi.org/10.1007/s00376-009-0154-5