Global surface currents are usually inferred from directly observed quantities like sea-surface height, wind stress by applying diagnostic balance relations (like geostrophy and Ekman flow), which provide a good approximation of the dynamics of slow, large-scale currents at large scales and low Rossby numbers. However, newer generation satellite altimeters (like the upcoming SWOT mission) will capture more of the high wavenumber variability associated with the unbalanced components, but the low temporal sampling can potentially lead to aliasing. Applying these balances directly may lead to an incorrect un-physical estimate of the surface flow. In this study we explore Machine Learning (ML) algorithms as an alternate route to infer surface currents from satellite observable quantities. We train our ML models with SSH, SST and wind stress from available primitive equation ocean GCM simulation outputs as the inputs and make predictions of surface currents (u,v), which are then compared against the true GCM output. As a baseline example, we demonstrate that a linear regression model is ineffective at predicting velocities accurately beyond localized regions. In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy+Ekman. Using a local stencil of neighboring grid points as additional input features, we can train the deep learning models to effectively ``learn” spatial gradients and the physics of surface currents. By passing the stenciled variables through convolutional filters we can help the model learn spatial gradients much faster. Various training strategies are explored using systematic feature hold out and multiple combinations of point and stenciled input data fed through convolutional filters (2D / 3D), to understand the effect of each input feature on the NN’s ability to accurately represent surface flow. A model sensitivity analysis reveals that besides SSH, geographic information in some form is an essential ingredient required for making accurate predictions of surface currents with deep learning models.

中文翻译：

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通过机器学习估算海面洋流

通常通过应用诊断平衡关系（如地球动力学和埃克曼流），从直接观测到的量（如海面高度，风应力）推断出全球表面电流，这可以很好地近似大范围内慢速，大电流的动力学。低Rossby数。但是，新一代卫星高度计（如即将进行的SWOT任务）将捕获更多与不平衡分量相关的高波数可变性，但是低时间采样可能会导致混叠。直接应用这些平衡可能会导致对表面流进行不正确的非物理估算。在这项研究中，我们探索机器学习（ML）算法，作为从卫星可观测量推断表面电流的替代途径。我们使用SSH训练我们的ML模型，来自可用原始图元海洋GCM模拟输出的SST和风应力作为输入，并预测表面电流（u，v），然后将其与真实GCM输出进行比较。作为基线示例，我们证明了线性回归模型无法有效预测超出局部区域的速度。相比之下，相对简单的神经网络（NN）可以准确预测全球大部分海洋的地表流，且均方误差比Geostrophy + Ekman低。使用相邻网格点的局部模具作为附加输入特征，我们可以训练深度学习模型以有效地``学习''空间梯度和表面电流的物理特性。通过将模版变量通过卷积滤波器，我们可以帮助模型更快地学习空间梯度。利用系统特征支持以及通过卷积滤波器（2D / 3D）馈入的点和模板输入数据的多种组合，探索了各种训练策略，以了解每个输入特征对NN准确表示表面流的能力的影响。模型敏感性分析表明，除了SSH之外，某种形式的地理信息也是使用深度学习模型准确预测地表电流所需的基本要素。