Satellite altimeters provide global observations of sea surface height (SSH) and present a unique data set for advancing our theoretical understanding of upper‐ocean dynamics and monitoring its variability. Considering that mesoscale SSH patterns can evolve on timescales comparable to or shorter than satellite return periods, it is challenging to accurately reconstruct the continuous SSH evolution as currently available altimetry observations are still spatially and temporally sparse. Here we explore the possibility of SSH interpolation via Deep Learning by using synthetic observations from an idealized quasigeostrophic model of baroclinic ocean turbulence. We demonstrate that Convolutional Neural Networks with Residual Learning are superior in SSH reconstruction to linear and recently developed dynamical interpolation techniques. Also, the deep neural networks can provide a skillful state estimate of unobserved deep ocean currents at mesoscales. These conspicuous results suggest that SSH patterns of eddies might contain substantial information about the underlying deep ocean currents that are necessary for SSH prediction. Our training data are focused on highly idealized physics and diversification of processes needs to be considered to more accurately represent the real ocean. In addition, methodological improvements such as transfer learning and implementation of dynamically aware loss functions might be necessary to consider before its ultimate use with real satellite observations. Nonetheless, by providing a proof of concept based on synthetic data, our results point to deep learning as a viable alternative to existing interpolation and, more generally, state estimation methods for satellite observations of eddying currents.

中文翻译：

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地转海洋湍流时空海面高度插值和深流估计的深度学习方法

卫星高度计可提供全球海平面高度（SSH）观测结果，并提供独特的数据集，以增进我们对上层海洋动力学的理论理解并监测其变化。考虑到中尺度SSH模式可以在与卫星返回周期相当或比其短的时间尺度上演化，由于当前可用的测高观测值在空间和时间上仍然稀疏，因此要准确地重建连续SSH演化是一项挑战。在这里，我们通过使用来自斜压海洋湍流理想化拟营养理论的综合观测资料，探索了通过深度学习进行SSH插值的可能性。我们证明了具有残差学习的卷积神经网络在SSH重建中优于线性和最近开发的动态插值技术。也，深度神经网络可以提供中尺度上未观测到的深海流的熟练状态估计。这些明显的结果表明，涡流的SSH模式可能包含有关SSH预测所需的潜在深海流的大量信息。我们的培训数据侧重于高度理想化的物理，并且需要考虑过程的多样化以更准确地表示真实的海洋。此外，在将其最终用于实际卫星观测之前，可能需要考虑进行方法上的改进，例如传递学习和动态感知损失函数的实现。尽管如此，通过提供基于综合数据的概念证明，我们的结果表明深度学习是现有插值方法的可行替代方法，并且更广泛地说，