An adaptive estimation of forecast error covariance parameters for Kalman filtering data assimilation
An adaptive estimation of forecast error covariance parameters for Kalman filtering data assimilation
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DOI：
10.1007/s00376-009-0154-5
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发表时间：
2009-01
影响因子：
5.8
通讯作者：
Xiaogu Zheng
中科院分区：
文献类型：
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作者：
Xiaogu Zheng
提出了一种用于卡尔曼滤波数据同化的预报误差协方差矩阵的自适应估计方法。最初使用扰动预报集合来估计预报误差协方差矩阵。然后用尺度参数调整该初始估计矩阵，该尺度参数通过最小化观测-负-预测残差的−2对数似然自适应地估计。在模型误差统计未知的情况下，该方法可用于不完全模式的卡尔曼滤波同化。用一个简单的非线性模型(Burgers型方程模型)验证了该方法的有效性。
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.