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A deep learning approach to extract internal tides scattered by geostrophic turbulence

Internal tides (ITs) are inertia-gravity waves at tidal frequencies, important to oceanographers due to their roles in deep/upper ocean mixing and so on. Conventionally, for altimetric observations of Sea Surface Height (SSH) data, ITs have been extracted by harmonically fitting over observed time sequences. However, in presence of strong incoherence induced by interactions with flows or changes in stratifications,  harmonic fits do not work well for data with coarse temporal sampling. Such problem would be exacerbated in the upcoming Surface Water Ocean Topography (SWOT) satellite mission due to the finer spatial scales to be resolved. However, SWOT's wide swaths will produce SSH snapshots that are spatially two-dimensional, which allows the community to treat tidal extraction as an operation on two-dimensional images. Here, we regard tidal extraction purely as an image translation problem. We design and train what we call "Toronto Internal Tide Emulator" (TITE), a conditional Generative Adversarial Network, which, given a snapshot of raw SSH from an idealized numerical eddying simulation, generates a snapshot of the embedded tidal component. No temporal information or physical knowledge is required for TITE to work. We test TITE on data whose dynamical regimes are different from the data provided during training. Despite the diversity and complexity of data, it accurately extracts tidal components in most individual snapshots considered and reproduces physically meaningful statistical properties. Predictably, TITE's performance decreases with the intensity of the turbulent flow.

Event series  Brewer-Wilson Seminar Series