A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering
A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering
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DOI：
10.16993/tellusa.214
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
A. Popov;Adrian Sandu;E. Niño;G. Evensen
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文献类型：
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作者：
A. Popov;Adrian Sandu;E. Niño;G. Evensen
操作集成卡尔曼滤波 (EnKF) 方法依赖于特定于模型的启发式方法，例如本地化，这通常是基于模型变量的空间局部性来实现的。相反，我们通过查看局部平均时间行为，提出更密切依赖于模型动态的方法。这种行为通常用动力系统的气候协方差来描述。我们将利用这个协方差作为协方差收缩方法中的目标矩阵，从中我们将绘制一个合成系综，以丰富系综变换和系综的子空间。
Operational Ensemble Kalman Filtering (EnKF) methods rely on model-specific heuristics such as localization, which are typically implemented based on the spacial locality of the model variables. We instead propose methods that more closely depend on the dynamics of the model, by looking at locally averaged-in-time behavior. Such behavior is typically described in terms of a climatological covariance of the dynamical system. We will be utilizing this covariance as the target matrix in covariance shrinkage methods, from which we will be drawing a synthetic ensemble in order to enrich both the ensemble transformation and the subspace of the ensemble.