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An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation


doi: 10.1007/s00376-009-0154-5

  • 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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Manuscript History

Manuscript received: 10 January 2009
Manuscript revised: 10 January 2009
通讯作者: 陈斌, bchen63@163.com
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An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation

  • 1. National Institute of Water and Atmospheric Research, Wellington, New Zealand

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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