Accurate prediction of ocean surface currents is important for marine safety, ship routing, tracking of pollutants and in coupled forecasting. Presently, velocity observations are not routinely assimilated in global ocean forecasting systems, largely due to the sparsity of the observation network. Several satellite missions are now being proposed with the capability to measure Total Surface Current Velocities (TSCV). If successful, these would substantially increase the coverage of ocean current observations and could improve accuracy of ocean current forecasts through data assimilation. In this paper, Observing System Simulation Experiments (OSSEs) are used to assess the impact of assimilating TSCV in the Met Office’s global ocean forecasting system. Synthetic observations are generated from a high-resolution model run for all standard observation types (sea surface temperature, profiles of temperature and salinity, sea level anomaly and sea ice concentration) as well as TSCV observations from a Sea surface KInematics Multiscale monitoring (SKIM) like satellite. The assimilation of SKIM like TSCV observations is tested over an 11 month period. Preliminary experiments assimilating idealised single TSCV observations demonstrate that ageostrophic velocity corrections are not well retained in the model. We propose a method for improving ageostrophic currents through TSCV assimilation by initialising Near Inertial Oscillations with a rotated incremental analysis update (IAU) scheme. The OSSEs show that TSCV assimilation has the potential to significantly improve the prediction of velocities, particularly in the Western Boundary Currents, Antarctic Circumpolar Current and in the near surface equatorial currents. For global surface velocity the analysis root-mean-square-errors (RMSEs) are reduced by 23% and there is a 4-day gain in forecast RMSE. There are some degradations to the subsurface in the tropics, generally in regions with complex vertical salinity structures. However, outside of the tropics, improvements are seen to velocities throughout the water column. Globally there are also improvements to temperature and sea surface height when TSCV are assimilated. The TSCV assimilation largely corrects the geostrophic ocean currents, but results using the rotated IAU method show that the energy at inertial frequencies can be improved with this method. Overall, the experiments demonstrate significant potential benefit of assimilating TSCV observations in a global ocean forecasting system.

中文翻译：

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评估英国气象局全球海洋预报系统中同化总表面流速度的潜在影响

海洋表面流的准确预测对于海洋安全、船舶航线、污染物跟踪和耦合预测具有重要意义。目前，速度观测并未常规地纳入全球海洋预报系统，这主要是由于观测网络的稀疏性。目前正在提议多个具有测量总表面流速度（TSCV）能力的卫星任务。如果成功，这些将大大增加洋流观测的覆盖范围，并可以通过数据同化提高洋流预测的准确性。在本文中，观测系统模拟实验（OSSE）用于评估同化 TSCV 对英国气象局全球海洋预报系统的影响。综合观测结果是从针对所有标准观测类型（海面温度、温度和盐度分布、海平面异常和海冰浓度）运行的高分辨率模型以及来自海面运动学多尺度监测 (SKIM) 的 TSCV 观测结果生成的就像卫星一样。 SKIM 的同化（如 TSCV 观测结果）经过 11 个月的测试。同化理想化单个 TSCV 观测结果的初步实验表明，模型中没有很好地保留地转速度修正。我们提出了一种通过 TSCV 同化改善地转电流的方法，通过使用旋转增量分析更新 (IAU) 方案初始化近惯性振荡。 OSSE表明，TSCV同化有可能显着改善速度预测，特别是在西边界流、南极绕极流和近地表赤道流中。对于全球表面速度，分析均方根误差 (RMSE) 减少了 23%，预测 RMSE 增加了 4 天。热带地区的地下存在一些退化，通常是在垂直盐度结构复杂的地区。然而，在热带地区之外，整个水柱的速度有所改善。当 TSCV 被同化时，全球范围内的温度和海面高度也有所改善。 TSCV同化很大程度上纠正了地转洋流，但使用旋转IAU方法的结果表明，该方法可以改善惯性频率下的能量。总体而言，这些实验证明了在全球海洋预报系统中同化 TSCV 观测结果的巨大潜在好处。