In this study, we developed a flow‐dependent sequential assimilation‐based targeted observation method by minimizing the analysis error variance under the framework of the ensemble Kalman filter (EnKF). This approach considers the flow‐dependent variation in background error statistics when identifying optimal observational sites through the sequential assimilation method. Covariance localization is also introduced in this method, enabling computational efficiency and eliminating impacts from spurious observations. By quantifying the reduction in analysis error variances, the proposed method could estimate the potential improvements by each optimal observation while assimilated. With this method, we design an optimal observational array for sea level anomaly (SLA) prediction in the tropical Indian Ocean (TIO), which is implemented using a fully coupled climate model, the Community Earth System Model (CESM), in conjunction with a coupled assimilation system. The optimal observational array detected from this method was found to theoretically reduce the initial uncertainty by up to approximately 60% of the error variance. An observing system simulation experiment (OSSE) using the CESM and the coupled assimilation system, which was designed for validation purposes, confirms the theoretical reduction in the analysis error variance by the optimal observation array.

中文翻译：

---

集合卡尔曼滤波同化系统的流依赖目标观测方法

在这项研究中，我们通过最小化集合卡尔曼滤波器（EnKF）框架下的分析误差方差，开发了一种基于流量的基于顺序同化的目标观测方法。通过顺序同化方法确定最佳观测点时，这种方法考虑了背景误差统计中流量相关的变化。此方法中还引入了协方差局部化，从而提高了计算效率，并消除了来自假观测的影响。通过量化分析误差方差的减少，所提出的方法可以在同化时通过每个最佳观测值来估计潜在的改进。通过这种方法，我们为热带印度洋（TIO）中的海平面异常（SLA）预测设计了最佳观测阵列，它是通过完全耦合的气候模型，社区地球系统模型（CESM）以及耦合的同化系统实现的。从理论上发现，从该方法检测到的最佳观测阵列可以将初始不确定性减少大约60％的误差方差。使用CESM和耦合同化系统进行的观测系统仿真实验（OSSE），其设计用于验证目的，通过最佳观测阵列确认了理论上的误差减小。