A Deep Learning Approach to Extract Balanced Motions From Sea Surface Height Snapshot

Extracting balanced geostrophic motions (BM) from sea surface height (SSH) observations obtained by wide-swath altimetry holds great significance in enhancing our understanding of oceanic dynamic processes at submesoscale wavelength. However, SSH observations derived from wide-swath altimetry are characterized by high spatial resolution while relatively low temporal resolution, thereby posing challenges to extract the BM from a single SSH snapshot. To address this issue, this paper proposes a deep learning model called the BM-UBM Network, which takes an instantaneous SSH snapshot as input and outputs the projection corresponding to the BM. Training experiments are conducted both in the Gulf Stream and South China Sea, and three metrics are considered to diagnose model's outputs. The favorable results highlight the potential capability of the BM-UBM Network to process SSH measurements obtained by wide-swath altimetry. Oceanic dynamic processes can be classified into two categories: balanced geostrophic motions (BM), including large-scale circulation, mesoscale and submesoscale eddy turbulence, and unbalanced wave motions (UBM), including barotropic tides, and inertia-gravity waves (IGWs). Both types of motions coexist and have respective contributions to the sea surface height (SSH). How to extract the BM from the total SSH observations obtained by satellite altimetry is the crucial problem to be solved in this paper. To tackle this issue, we propose a deep learning model named the BM-UBM Network to establish the relationship between the total SSH and the BM component. The BM-UBM Network can generate SSH estimations for the BM when provided with a well-resolved SSH snapshot. A Deep learning model is developed to extract balanced motions from sea surface height snapshot based on a realistic simulation Diagnostics of three metrics reveal the effectiveness of the model in extracting balanced motions The model exhibits remarkable advantages over the Gaussian filter (baseline) in capturing the gradient and Laplacian information