A limited-area ocean prediction system is developed to acquire forecast error covariances related to uncertainty in atmospheric forcing and turbulent mixing using perturbed model parameters within an Ensemble Kalman Filter (EnKF). The system performs sequential data assimilation delivering realistic ocean state estimation and forecasts. It is initialised to observations using the EnKF, the hybrid-EnKF and Ensemble Optimal Interpolation (EnOI). It has higher resolution than the parent global model, includes tides and assimilates altimetric sea level anomaly to constrain the offshore mesoscale circulation. Dynamic ensemble spread introduced by parameter uncertainty shows agreement with error estimates obtained from forecast innovation statistics. Hybrid EnKF offers improvements to both EnKF and EnOI, having smaller analysis increments and forecast errors. The ensemble mean of the hybrid-EnKF is more accurate than any member due to the introduction of parameterised error which behaves as additive inflation in the hybrid EnKF system. This indicates the parameterisation acts as a non-linear filter for model forecast error growth. To demonstrate consistency, experiments are carried out for various regions that differ in their oceanographic situations. The hybrid EnKF data assimilation system enables practical use of dynamic ensembles that capture the flow dependent errors which are mixed with static or climatological covariances for situations where model error is systematically under-estimated.

中文翻译：

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代表同化海洋模型的有限区域数据的不确定性

开发了有限区域海洋预测系统，使用集合卡尔曼滤波器（EnKF）内的扰动模型参数来获取与大气强迫和湍流混合的不确定性相关的预测误差协方差。该系统执行顺序数据同化，提供现实的海洋状态估计和预测。它使用 EnKF、混合 EnKF 和集成最优插值 (EnOI) 初始化为观测值。它比母全球模型具有更高的分辨率，包括潮汐并同化高度海平面异常以约束近海中尺度环流。由参数不确定性引入的动态集合分布表明与从预测创新统计中获得的误差估计一致。混合 EnKF 对 EnKF 和 EnOI 进行了改进，具有更小的分析增量和预测误差。由于引入了参数化误差，混合 EnKF 的整体平均值比任何成员都更准确，该参数化误差在混合 EnKF 系统中表现为加性膨胀。这表明参数化充当模型预测误差增长的非线性滤波器。为了证明一致性，对海洋状况不同的各个区域进行了实验。混合 EnKF 数据同化系统能够实际使用动态系综，捕获流相关误差，这些误差与静态或气候协方差混合，适用于系统性低估模型误差的情况。