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首页> 外文期刊>Monthly Weather Review >A Comparison of Hybrid Ensemble Transform Kalman Filter-Optimum Interpolation and Ensemble Square Root Filter Analysis Schemes
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A Comparison of Hybrid Ensemble Transform Kalman Filter-Optimum Interpolation and Ensemble Square Root Filter Analysis Schemes

机译：混合集成变换卡尔曼滤波器-最佳插值与集成平方根滤波器分析方案的比较

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摘要

A hybrid ensemble transform Kalman filter (ETKF)-optimum interpolation (OI) analysis scheme is described and compared with an ensemble square root filter (EnSRF) analysis scheme. A two-layer primitive equation model was used under perfect-model assumptions. A simplified observation network was used, and the OI method utilized a static background error covariance constructed from a large inventory of historical forecast errors. The hybrid scheme updated the ensemble mean using a hybridized ensemble and static background-error covariance. The ensemble perturbations in the hybrid scheme were updated by the ETKF scheme. The EnSRF ran parallel data assimilation cycles for each member and serially assimilated the observations. The EnSRF background-error covariance was estimated fully from the ensemble. For 50-member ensembles, the analyses from the hybrid scheme were as accurate or nearly as accurate as those from the EnSRF, depending on the norm. For 20-member ensembles, the analyses from the hybrid scheme were more accurate than analyses from the EnSRF under certain norms. Both hybrid and EnSRF analyses were more accurate than the analyses from the OI. Further reducing the ensemble size to five members, the EnSRF exhibited filter divergence, whereas the analyses from the hybrid scheme were still better than those updated by the OI. Additionally, the hybrid scheme was less prone to spurious gravity wave activity than the EnSRF, especially when the ensemble size was small. Maximal growth in the ETKF ensemble perturbation space exceeded that in the EnSRF ensemble perturbation space. The relationship of the ETKF ensemble variance to the analysis error variance, a measure of a spread-skill relationship, was similar to that of the EnSRF ensemble. The hybrid scheme can be implemented in a reasonably straightforward manner in the operational variational frameworks, and the computational cost of the hybrid is expected to be much less than the EnSRF in the operational settings.
机译：描述了混合集成变换卡尔曼滤波器（ETKF）-最佳插值（OI）分析方案，并将其与集成平方根滤波器（EnSRF）分析方案进行了比较。在完美模型假设下使用了两层原始方程模型。使用了简化的观测网络，OI方法利用了从大量历史预测误差清单中构建的静态背景误差协方差。混合方案使用混合集成和静态背景误差协方差更新了集成平均值。混合方案中的整体扰动由ETKF方案更新。 EnSRF为每个成员运行并行的数据同化周期，并串行化观测结果。 EnSRF背景误差协方差是从集合中完全估算出来的。对于50个成员的合奏，根据规范，混合方案的分析与EnSRF的分析一样准确或几乎准确。对于20个成员的合奏，在某些规范下，混合方案的分析比EnSRF的分析更准确。混合分析和EnSRF分析都比OI分析更准确。 EnSRF进一步将集合大小减小到五个成员，表现出滤光器发散性，而混合方案的分析仍然优于OI更新的分析。此外，与EnSRF相比，混合方案更不容易产生伪重力波活动，尤其是在合奏尺寸较小时。 ETKF集合扰动空间的最大增长超过了EnSRF集合扰动空间的最大增长。 ETKF集成方差与分析误差方差（一种扩展技能关系的度量）之间的关系与EnSRF集成相类似。可以在操作变型框架中以合理直接的方式实现混合方案，并且在操作环境中，混合方案的计算成本预计将比EnSRF小得多。

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