Operational ocean nowcast/forecast systems require real-time sampling of oceanic data for representing realistic oceanic conditions. Satellite altimetry plays a key role in detecting mesoscale variability of the ocean currents. The 10-day sampling period and horizontal gaps between the altimetry tracks of 100 km cause difficulties in capturing shorter-term/smaller-scale ocean current variations. Operational systems based on a three-dimensional variational method (3dVar) do not take into account temporal variability of the data within data assimilation time windows. Four-dimensional data assimilation technique is considered as a possible tool for more efficient utilization of the observations arriving from satellite altimeters by the dynamically constrained interpolation. In this study, we develop and test the performance of the adjoint-free four-dimensional variational method (a4dVar) for operational use in regional models. Numerical experiments targeting the Kuroshio path variations south of Japan demonstrate that the a4dVar scheme dynamically corrects the initial condition so as to effectively reduce the satellite altimetry data misfit during a 9-day time window. The corrected initial condition further contributes to improvements in the skill of reconstruction of the Kuroshio path variation in a 30-day lead hindcast run.

中文翻译：

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将无伴随的4dVar同化应用到日本南部黑潮的建模中

运行中的海洋临近预报/预报系统需要对海洋数据进行实时采样，以代表现实的海洋状况。卫星测高仪在检测洋流的中尺度变化中起着关键作用。10天的采样周期和100 km的测高轨道之间的水平间隙给捕获短期/小规模海流变化带来了困难。基于三维变分方法（3dVar）的操作系统未考虑数据同化时间窗口内数据的时间可变性。多维数据同化技术被认为是通过动态约束插值更有效地利用从卫星高度计到达的观测数据的一种可能工具。在这个研究中，我们开发并测试了在区域模型中用于运营的无伴随四维变分方法（a4dVar）的性能。针对日本南部黑潮路径变化的数值实验表明，a4dVar方案可以动态校正初始条件，从而有效地减少了9天时间范围内卫星测高仪数据的不匹配情况。校正后的初始条件进一步有助于提高30天领先后播运行中黑潮路径变化的重建技能。针对日本南部黑潮路径变化的数值实验表明，a4dVar方案可以动态校正初始条件，从而有效地减少了9天时间范围内卫星测高仪数据的不匹配情况。校正后的初始条件进一步有助于提高30天领先后播运行中黑潮路径变化的重建技能。针对日本南部黑潮路径变化的数值实验表明，a4dVar方案可以动态校正初始条件，从而有效地减少了9天时间范围内卫星测高仪数据的不匹配情况。校正后的初始条件进一步有助于提高30天领先后播运行中黑潮路径变化的重建技能。