The back error covariance controls the magnitude and structure of adjustment to the observations. Therefore,it plays a very important role in the quality of the assimilation.
背景误差协方差控制了分析向观测调整的幅度以及调整的结构,所以其对同化分析的质量起着至关重要的作用。
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The computation formulas for the prediction error covariance matrix between any two subsystems are given.
同时给出了任两个传感器之间的预报误差协方差阵的计算公式。
Different correlation functions fitting background error covariance are investigated and the results are given.
对于不同的相关函数模型对背景误差协方差的拟合也做了相关分析对比。
There are two forms of extended sequential estimation algorithm: 1. integrating error covariance matrix; 2. integrating state transition matrix.
推广的序贯估算法有两种形式：1。积分误差协方差矩阵，2。积分状态转移矩阵。
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