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Using Singular Value Decomposition in Conjunction with Data Assimilation Procedures

机译:与数据同化程序结合使用奇异值分解

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In this study we apply the singular value decomposition (SVD) technique of the so-called 'observability' matrix to analyse the information content of observations in 4D-Var assimilation procedures. Using a simple one-dimensional transport equation, the relationship between the optimal state estimate and the right singular vectors of the observability matrix is examined. It is shown the importance of the value of the variance ratio, between the variances of the background and the observational errors, in maximizing the information that can be extracted from the observations by using Tikhonov regularization theory. Numerical results are presented.
机译:在这项研究中,我们应用所谓的“可观察性”矩阵的奇异值分解(SVD)技术来分析4D-Var同化过程中观测的信息内容。使用简单的一维输运方程,研究了最佳状态估计与可观察性矩阵的右奇异向量之间的关系。它显示了背景变化和观测误差之间的方差比值的重要性,这对于最大化使用Tikhonov正则化理论可以从观测中提取的信息非常重要。给出了数值结果。

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