An Adaptive Estimation of Forecast Error Covariance
Parameters for Kalman Filtering Data Assimilation |
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| Authors: | Xiaogu ZHENG |
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| Institution: | National Institute of Water and Atmospheric Research, Wellington, New Zealand |
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| 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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| Keywords: | data assimilation Kalman filter ensemble prediction estimation |
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