Serial ensemble Kalman filter (EnKF) is a kind of EnKF which treats observations serially during every assimilation step. The assimilation order can be generated by different rules and has significant impacts on the performance of serial EnKF when localization algorithm is applied. In this study, we seek to examine and better understand the characteristics of various ordering methods when they are applied in the serial EnKF. The results show that different ordering methods demonstrate almost the same changes in analysis as the localization radius changing. Moreover, the optimal parameters of localization radius and forgetting factor of serial EnKF are found varying among different ordering rules. In addition, a novel rule for confirming the assimilation order is proposed to further improve the performance of serial EnKF. The observations are sorted from “better” to “worse” (OBS-BtoW), which are evaluated by estimating the distance of analysis between the prior and observations. Compared with the existing ordering methods, the proposed method can improve the performance at a very small computation cost without needing future forecasts and the truth.

中文翻译：

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利用串行集成卡尔曼滤波器探索同化顺序以改进同化

串行集成卡尔曼滤波器 (EnKF) 是一种 EnKF，它在每个同化步骤中串行处理观测值。同化顺序可以由不同的规则生成，并且在应用定位算法时对串行 EnKF 的性能有显着影响。在这项研究中，我们试图检查和更好地理解各种排序方法在系列 EnKF 中应用时的特征。结果表明，随着定位半径的变化，不同的排序方法在分析中表现出几乎相同的变化。此外，发现序列EnKF的定位半径和遗忘因子的最佳参数在不同的排序规则中有所不同。此外，提出了一种确认同化顺序的新规则，以进一步提高串行 EnKF 的性能。观察从“更好”到“更差”（OBS-BtoW）排序，通过估计先验和观察之间的分析距离来评估。与现有的排序方法相比，所提出的方法可以以非常小的计算成本提高性能，而不需要未来的预测和真相。