Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part II: Application to a Convective-Scale NWP Model
Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part II: Application to a Convective-Scale NWP Model
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：
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发表时间：
2015
期刊：
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通讯作者：
L. Berre
中科院分区：
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作者：
B. Ménétrier;T. Montmerle;Y. Michel;L. Berre
摘要在这项由两部分组成的研究的第一部分中，详细介绍了从预测集合中采样的协方差最佳线性过滤的新理论。这种方法，特别是设计用于数值天气预报（NWP）系统中的数据同化（DA）方案，具有使用仅涉及样本估计量和滤波器输出的最优性准则的优点。在这第二部分中，理论进行了测试与真实的背景误差协方差计算使用大集合数据同化（EDA）在对流尺度耦合在全球尺度上的大EDA，分别基于应用研究业务中尺度（AROME）和ARPEGE业务数值预报系统。背景误差方差估计与该合奏的一个子集进行过滤，并与其余成员，这被认为是一个独立的参考获得的值进行评估。在第一部分中给出的算法给出了相关的结果，其中齐次滤波是准最优的。异基因
AbstractIn Part I of this two-part study, a new theory for optimal linear filtering of covariances sampled from an ensemble of forecasts was detailed. This method, especially designed for data assimilation (DA) schemes in numerical weather prediction (NWP) systems, has the advantage of using optimality criteria that involve sample estimated quantities and filter output only. In this second part, the theory is tested with real background error covariances computed using a large ensemble data assimilation (EDA) at the convective scale coupled with a large EDA at the global scale, based respectively on the Applications of Research to Operations at Mesoscale (AROME) and ARPEGE operational NWP systems. Background error variances estimated with a subset of this ensemble are filtered and evaluated against values obtained with the remaining members, which are considered as an independent reference. Algorithms presented in Part I show relevant results, with the homogeneous filtering being quasi optimal. Heterogene...