论文标题
湍流区域的观测系统之间的协同作用
Synergy between Observation Systems Oceanic in Turbulent Regions
论文作者
论文摘要
海洋动力学构成了确定海洋在复杂气候现象中的作用方面的不一度。当前的观察系统在达到三维海洋数据的足够统计精度方面存在局限性。描述内部海洋结构的行为是至关重要的知识。我们介绍了数据驱动的方法，这些方法探讨了在海湾流和黑鲁氏电流的扩展中建模海洋动力学的潜在类别回归和深层回归神经网络。获得的结果表明，在湍流区域的空间和时间尺寸中，了解海洋特征（包括盐度和温度）的有希望的数据驱动方向。我们的源代码可在https://github.com/v18nguye/gulfstream-lrm和https://github.com/sagudelor/kuroshio上公开获得。
Ocean dynamics constitute a source of incertitude in determining the ocean's role in complex climatic phenomena. Current observation systems have limitations in achieving sufficiently statistical precision for three-dimensional oceanic data. It is crucial knowledge to describe the behavior of internal ocean structures. We present the data-driven approaches which explore latent class regressions and deep regression neural networks in modeling ocean dynamics in the extensions of Gulf Stream and Kuroshio currents. The obtained results show a promising data-driven direction for understanding the ocean's characteristics, including salinity and temperature, in both spatial and temporal dimensions in the turbulent regions. Our source codes are publicly available at https://github.com/v18nguye/gulfstream-lrm and at https://github.com/sagudelor/Kuroshio.
