论文标题
使用卷积神经网络从广泛的高度数据中过滤内部潮汐
Filtering Internal Tides From Wide-Swath Altimeter Data Using Convolutional Neural Networks
论文作者
论文摘要
即将到来的地表水海形（SWOT）卫星高度学任务预计将对海面高度（SSH）产生二维的高分辨率测量，从而可以更好地对中尺度和子尺度涡流涡流进行更好的特征。但是，要履行此任务的承诺，必须过滤SSH测量的潮汐成分。这个具有挑战性的问题至关重要，因为物理海洋学家使用SWOT数据进行的后验研究将在很大程度上取决于所选的过滤方案。在本文中，我们将此问题投入到一个有监督的学习框架中，并建议使用卷积神经网络（Convnets）来估计没有内部潮汐信号的字段。基于海洋循环的高级北大西洋模拟（ENATL60）的数值实验表明，即使在神经网络看不见的区域，我们的Convnet也大大降低了SSH数据中内部波的烙印。我们还研究了考虑来自其他海面变量（例如海面温度（SST））的其他数据的相关性。
The upcoming Surface Water Ocean Topography (SWOT) satellite altimetry mission is expected to yield two-dimensional high-resolution measurements of Sea Surface Height (SSH), thus allowing for a better characterization of the mesoscale and submesoscale eddy field. However, to fulfill the promises of this mission, filtering the tidal component of the SSH measurements is necessary. This challenging problem is crucial since the posterior studies done by physical oceanographers using SWOT data will depend heavily on the selected filtering schemes. In this paper, we cast this problem into a supervised learning framework and propose the use of convolutional neural networks (ConvNets) to estimate fields free of internal tide signals. Numerical experiments based on an advanced North Atlantic simulation of the ocean circulation (eNATL60) show that our ConvNet considerably reduces the imprint of the internal waves in SSH data even in regions unseen by the neural network. We also investigate the relevance of considering additional data from other sea surface variables such as sea surface temperature (SST).
