Ensemble optimal interpolation (EnOI) is a variant of the ensemble Kalman filter (EnKF) that operates with a static ensemble to drastically reduce its computational cost. The idea is to use a pre-selected ensemble to parameterize the background covariance matrix, which avoids the costly integration of the ensemble members with the dynamical model during the forecast step of the filtering process. To better represent the pronounced time-varying circulation of the Red Sea, we propose a new adaptive EnOI approach in which the ensemble members are adaptively selected at every assimilation cycle from a large dictionary of ocean states describing the Red Sea variability. We implement and test different schemes to select the ensemble members (i) based on the similarity to the forecast state according to some criteria, or (ii) in term of best representation of the forecast in an ensemble subspace using an Orthogonal Matching Pursuit (OMP) algorithm. The relevance of the schemes is first demonstrated with the Lorenz 63 and Lorenz 96 models. Then results of numerical experiments assimilating real remote sensing data into a high resolution MIT general circulation model (MITgcm) of the Red Sea using the Data Assimilation Research Testbed (DART) system are presented and discussed.

集成最优插值（EnOI）是集成卡尔曼滤波器（EnKF）的变体，它与静态集成一起工作以大大降低其计算成本。想法是使用预选的集合对背景协方差矩阵进行参数化，从而避免了在滤波过程的预测步骤中将集合成员与动力学模型进行昂贵的集成。为了更好地表示红海的明显时变环流，我们提出了一种新的自适应EnOI方法，其中在每个同化周期从描述红海可变性的大型海洋状态字典中自适应地选择集合成员。我们根据某些条件，根据与预测状态的相似性，实施并测试了不同的方案来选择集合成员（i），或（ii）使用正交匹配追踪（OMP）算法在集合子空间中最好地表示预测。该方案的相关性首先在Lorenz 63和Lorenz 96模型中得到了证明。然后提出并讨论了使用数据同化研究试验台（DART）系统将真实的遥感数据同化为红海的高分辨率MIT常规循环模型（MITgcm）的数值实验结果。