A deep learning approach to extract internal tides scattered by
geostrophic turbulence
Abstract
Extraction of internal tidal (IT) signals is central to the
interpretation of Sea Surface Height (SSH) data. The increased spatial
resolution of future wide-swath satellite missions poses a challenge for
traditional harmonic analysis, due to prominent and unsteady wave-mean
interactions at finer scales. However, the wide swaths will also produce
spatially two-dimensional SSH snapshots, which allows us to treat IT
extraction as an image translation problem for the first time. We design
and train 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 IT component. We test
it on data whose dynamical regimes are different from the data provided
during training. Despite the diversity and complexity of data, it
accurately extracts ITs in most individual snapshots considered and
reproduces physically meaningful statistical properties. Predictably,
TITE’s performance decreases with the intensity of the turbulent flow.