A new Hybrid ensemble data assimilation system is implemented with a Massachusetts Institute of Technology general circulation model (MITgcm) of the Red Sea. The system is based on the Data Assimilation Research Testbed (DART) and combines a time‐varying ensemble generated by the Ensemble Adjustment Kalman Filter (EAKF) with a pre‐selected quasi‐static (monthly varying) ensemble as used in an Ensemble Optimal Interpolation (EnOI) scheme. The goal is to develop an efficient system that enhances the state estimate and model forecasting skill in the Red Sea with reduced computational load compared to the EAKF. Observations of satellite sea‐surface temperature (SST), altimeter sea‐surface height (SSH), and in situ temperature and salinity profiles are assimilated to evaluate the new system. The performance of the Hybrid scheme (hereafter Hybrid‐EAKF) is assessed with respect to the EnOI and the EAKF results. The comparisons are based on the daily averaged forecasts against satellite SST and SSH measurements and independent in situ temperature and salinity profiles. Hybrid‐EAKF yields significant improvements in terms of ocean state estimates compared to both EnOI and EAKF, in particular mitigating for dynamical imbalances that affect EnOI. Hybrid‐EAKF improves the estimation of SST and SSH root‐mean‐square differences by up to 20% compared to EAKF. High‐resolution mesoscale eddy features, which dominate the Red Sea circulation, are further better represented in Hybrid‐EAKF. Important reduction, by about 75%, in computational cost is also achieved with the Hybrid‐EAKF system compared to the EAKF. These significant improvements were obtained with the Hybrid‐EAKF after accounting for uncertainties in the atmospheric forcing and internal model physics in the time‐varying ensemble.

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基于混合集成调整卡尔曼滤波器的红海高分辨率数据同化系统：实施和评估

一种新的混合集合数据同化系统是由红海的麻省理工学院通用循环模型（MITgcm）实现的。该系统基于数据同化研究测试台（DART），并将“集合调整卡尔曼滤波器（EAKF）”生成的随时间变化的集合与在“集合最佳插值”中使用的预先选择的准静态（每月变化）集合相结合。 （EnOI）方案。我们的目标是开发一种有效的系统，与EAKF相比，可以通过减少计算量来提高红海中的状态估计和模型预测技能。卫星海面温度（SST），高度计海面高度（SSH）和原位观测温度和盐度曲线被吸收以评估新系统。针对EnOI和EAKF结果评估了Hybrid方案（以下称为Hybrid-EAKF）的性能。这些比较是基于对卫星SST和SSH测量的每日平均预测以及原位独立进行的温度和盐度曲线。与EnOI和EAKF相比，Hybrid-EAKF在海洋状态估计方面产生了显着改善，尤其是减轻了影响EnOI的动力失衡。与EAKF相比，Hybrid-EAKF将SST和SSH均方根差异的估计值提高了20％。Hybrid-EAKF进一步代表了主导红海环流的高分辨率中尺度涡旋特征。与EAKF相比，Hybrid-EAKF系统还可以显着降低约75％的计算成本。在考虑了时变集合中大气强迫和内部模型物理的不确定性之后，Hybrid-EAKF获得了这些重大改进。