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首页> 外文期刊>Ocean modelling >An ensemble recentering Kalman filter with an application to Argo temperature data assimilation into the NASA GEOS-5 coupled model
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An ensemble recentering Kalman filter with an application to Argo temperature data assimilation into the NASA GEOS-5 coupled model

机译：整体调心卡尔曼滤波器及其在Argo温度数据同化到NASA GEOS-5耦合模型中的应用

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摘要

A two-step ensemble recentering Kalman filter (ERKF) analysis scheme is introduced. The algorithm consists of a recentering step followed by an ensemble Kalman filter (EnKF) analysis step. The recentering step is formulated such as to adjust the prior distribution of an ensemble of model states so that the deviations of individual samples from the sample mean are unchanged but the original sample mean is shifted to the prior position of the most likely particle, where the likelihood of each particle is measured in terms of closeness to the assimilated observations. The computational cost of the ERKF is essentially the same as that of a same size EnKF. The ERKF is applied to the assimilation of Argo temperature profiles into the OGCM component of an ensemble of NASA GEOS-5 coupled models. Unassimilated Argo salt data are used for validation. These data serve as a proxy to assess the potential of the ERKF to improve estimates of unobserved model variables. A surprisingly small number (16) of model trajectories is sufficient to significantly improve model estimates of salinity over estimates from an ensemble run without assimilation. The two-step algorithm also performs better than the EnKF although its performance is degraded in poorly observed regions. The efficacy of the recentering is attributed to its ability to preserve balance relationships between observed and unobserved variables, even when the ensemble size is too small for the EnKF to accurately estimate cross-field error covariances.
机译：提出了一种两步集成整体卡尔曼滤波器（ERKF）分析方案。该算法包括重新定调步骤，然后是集成卡尔曼滤波器（EnKF）分析步骤。重新调整步骤的公式如下：调整模型状态集合的先验分布，以使各个样本与样本均值的偏差保持不变，但原始样本均值会移动到最可能的粒子的先前位置，在此位置每个粒子的似然度是根据与观察结果的接近程度来衡量的。 ERKF的计算成本与相同大小的EnKF基本上相同。 ERKF用于将Argo温度曲线吸收到NASA GEOS-5耦合模型集合的OGCM组件中。未经同化的Argo盐数据用于验证。这些数据可作为评估ERKF改进未观察到的模型变量估计的潜力的代理。极少的模型轨迹数（16）足以显着改善盐度的模型估算，而不是来自集合运行的估算（无需同化）。两步算法的性能也比EnKF更好，尽管在观察较差的区域其性能会降低。即使当集合大小对于EnKF而言太小而无法准确估计跨场误差协方差时，重排的效果也归因于其保持观察到的变量和未观察到的变量之间的平衡关系的能力。

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