论文标题
未解决的中尺度涡流的随机数据驱动参数化
Stochastic data-driven parameterization of unresolved mesoscale eddies
论文作者
论文摘要
在这项工作中，提出了基于物理运输原理的随机表示，以解释中尺度涡流对大型海洋循环的影响。这种随机框架是由拉格朗日速度分解为平稳的成分和高度振荡的噪声项。该随机模型的一个重要特征是，它可以保留解决方案的总能量，以实现任何实现。这种能量保存表示形式成功地以良好的多层准地藻动力学核心实现。未解决的噪声的经验空间相关性是根据涡流解析的模拟数据校准的。特别是，可以通过Girsanov转换在噪声中引入固定校正漂移。这个非直观的术语似乎在繁殖的粗网架上是风驱动双gyre循环的向东射流的重要性。此外，已经提出了一种投影方法来限制沿垂直分层的ISO曲面的噪声。最终的噪声使我们能够改善大规模电流的内在低频变异性。
In this work, a stochastic representation based on a physical transport principle is proposed to account for mesoscale eddy effects on the large-scale oceanic circulation. This stochastic framework arises from a decomposition of the Lagrangian velocity into a smooth-in-time component and a highly oscillating noise term. One important characteristic of this random model is that it conserves the total energy of the resolved flow for any realization. Such an energy-preserving representation is successfully implemented in a well established multi-layered quasi-geostrophic dynamical core. The empirical spatial correlation of the unresolved noise is calibrated from the eddy-resolving simulation data. In particular, a stationary correction drift can be introduced in the noise through Girsanov transformation. This non intuitive term appears to be important in reproducing on a coarse mesh the eastwards jet of the wind-driven double-gyre circulation. In addition, a projection method has been proposed to constrain the noise living along the iso-surfaces of the vertical stratification. The resulting noise enables us to improve the intrinsic low-frequency variability of the large-scale current.
