EddyScan: A physically consistent ocean eddy monitoring application

James H. Faghmous, L. Styles, Varun Mithal, S. Boriah, S. Liess, Vipin Kumar, F. Vikebø, M. D. Mesquita
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引用次数: 26

Abstract

Rotating coherent structures of water known as ocean eddies are the oceanic analog of storms in the atmosphere and a crucial component of ocean dynamics. In addition to dominating the ocean's kinetic energy, eddies play a significant role in the transport of water, salt, heat, and nutrients. Therefore, understanding current and future eddy activity is a central challenge to address future sustainability of marine ecosystems. The emergence of sea surface height observations from satellite radar altimeter has recently enabled researchers to track eddies at a global scale. The majority of studies that identify eddies from observational data employ highly parametrized connected component algorithms using expert filtered data, effectively making reproducibility and scalability challenging. In this paper, we improve upon the state-of-the-art connected component eddy monitoring algorithms to track eddies globally. This work makes three main contributions: first, we do not pre-process the data therefore minimizing the risk of wiping out important signals within the data. Second, we employ a physically-consistent convexity requirement on eddies based on theoretical and empirical studies to improve the accuracy and computational complexity of our method from quadratic to linear time in the size of each eddy. Finally, we accurately separate eddies that are in close spatial proximity, something existing methods cannot accomplish. We compare our results to those of the state of the art and discuss the impact of our improvements on the difference in results.
EddyScan:物理上一致的海洋涡流监测应用程序
被称为海洋涡流的旋转相干水结构是海洋中大气风暴的模拟物,也是海洋动力学的重要组成部分。除了控制海洋的动能外,漩涡在水、盐、热量和营养物质的运输中也起着重要作用。因此,了解当前和未来的涡旋活动是解决未来海洋生态系统可持续性的核心挑战。最近,卫星雷达高度计海面高度观测的出现使研究人员能够在全球范围内跟踪漩涡。大多数从观测数据中识别涡流的研究都采用了高度参数化的连接组件算法,使用专家过滤的数据,有效地提高了可重复性和可扩展性。在本文中,我们改进了最先进的连接组件涡流监测算法来全局跟踪涡流。这项工作有三个主要贡献:首先,我们没有对数据进行预处理,因此最大限度地降低了数据中重要信号被删除的风险。其次,在理论和实证研究的基础上,我们采用了涡流的物理一致性凸性要求,以提高我们的方法在每个涡流大小上从二次时间到线性时间的精度和计算复杂度。最后,我们准确地分离出空间接近的涡流,这是现有方法无法做到的。我们将我们的结果与最先进的结果进行比较,并讨论我们的改进对结果差异的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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