摘要：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.
