Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization
Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization
复制标题
基于集成的数据同化的样本协方差的线性过滤。
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
L. Berre
中科院分区：
文献类型：
--
作者：
B. Ménétrier;T. Montmerle;Y. Michel;L. Berre
摘要在数值天气预报(NWP)系统的数据同化(DA)方案中，预报误差协方差的估计是获得某些气流相关性的关键。以前的研究表明，集合数据同化方法对于这项任务是最准确的。然而，它们巨大的计算成本增加了对集合大小的强烈限制。因此，用小集合估计的协方差受随机抽样误差的影响。本文的目的是发展一种协方差滤波理论，以便在保留感兴趣信号的同时去除大部分采样噪声，并将其应用于实际数值预报系统的DA方案中。这是由两部分组成的研究的第一部分，介绍了基于最优线性滤波和样本中心矩估计理论的最优滤波准则的理论方面。其强度仅取决于样本估计量和滤波输出的使用。这些标准为新的算法铺平了道路。
AbstractIn data assimilation (DA) schemes for numerical weather prediction (NWP) systems, the estimation of forecast error covariances is a key point to get some flow dependency. As shown in previous studies, ensemble data assimilation methods are the most accurate for this task. However, their huge computational cost raises a strong limitation to the ensemble size. Consequently, covariances estimated with small ensembles are affected by random sampling errors. The aim of this study is to develop a theory of covariance filtering in order to remove most of the sampling noise while keeping the signal of interest and then to use it in the DA scheme of a real NWP system. This first part of a two-part study presents the theoretical aspects of such criteria for optimal filtering based on the merging of the theories of optimal linear filtering and of sample centered moments estimation. Its strength relies on the use of sample estimated quantities and filter output only. These criteria pave the way for new algori...