Implications of the Form of the Ensemble Transformation in the Ensemble Square Root Filters
Implications of the Form of the Ensemble Transformation in the Ensemble Square Root Filters
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DOI：
10.1175/2007mwr2021.1
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发表时间：
2008-03
影响因子：
3.2
通讯作者：
P. Sakov;P. Oke
中科院分区：
文献类型：
--
作者：
P. Sakov;P. Oke
摘要本文考虑了集合平方根滤波器（ESRF）中不同形式的集合变换对基于ESRF的数据同化系统性能的影响。它强调了使用平均保持解决方案的合奏变换矩阵（ETM）的重要性。本文证明了任意保均值ETM都可以表示为对称解与保均值正交矩阵的乘积。本文还介绍了一种新的风味ESRF，称为ESRF与均值保持随机旋转。为了研究ESRF中ETM的不同解决方案的性能，进行了两个小模型的实验。在这些实验中，两个均值保持的解决方案，两个非均值保持的解决方案，和一个传统的集合卡尔曼滤波器与扰动观测的性能进行了比较。实验结果表明，在ESRF的ETM的均值保持解决方案的性能显着更好的相比，非均值保持…
Abstract This paper considers implications of different forms of the ensemble transformation in the ensemble square root filters (ESRFs) for the performance of ESRF-based data assimilation systems. It highlights the importance of using mean-preserving solutions for the ensemble transform matrix (ETM). The paper shows that an arbitrary mean-preserving ETM can be represented as a product of the symmetric solution and an orthonormal mean-preserving matrix. The paper also introduces a new flavor of ESRF, referred to as ESRF with mean-preserving random rotations. To investigate the performance of different solutions for the ETM in ESRFs, experiments with two small models are conducted. In these experiments, the performances of two mean-preserving solutions, two non-mean-preserving solutions, and a traditional ensemble Kalman filter with perturbed observations are compared. The experiments show a significantly better performance of the mean-preserving solutions for the ETM in ESRFs compared to non-mean-preservi...