A Deep Learning Approach to Extract Internal Tides Scattered by Geostrophic Turbulence

Abstract

A proper extraction of internal tidal 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 SSH snapshots that are spatially two‐dimensional, which allows us to treat tidal extraction as an image translation problem. We design and train 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. 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 tidal components in most individual snapshots considered and reproduces physically meaningful statistical properties. Predictably, Toronto Internal Tide Emulator's performance decreases with the intensity of the turbulent flow.

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

“…Additionally, some studies treated the snapshot-style separation of the BM and UBM as an image-to-image translation problem, and used deep learning approaches to solve it. Wang, Grisouard, et al (2022) used Generative Adversarial Network (GAN) to extract internal tides (IT) from an idealized eddying simulated SSH snapshot. Lguensat et al (2020) used Residual Network (ResNet) to filter IGWs from SSH data based on a realistic numerical simulation (eNATL60).…”

“…The researches of Wang, Grisouard, et al. (2022) and Lguensat et al. (2020) have its own advantages.…”

“…The researches of Wang, Grisouard, et al (2022) and Lguensat et al (2020) have its own advantages. Wang, Grisouard, et al (2022) carried out tests under different relative magnitudes of the BM and UBM (by setting different configurations for idealized simulation) in order to mimic various dynamical regimes in the real-ocean scenarios. Lguensat et al (2020) was an important attempt based on a realistic (rather than idealized) numerical simulation.…”

“…Wang, Grisouard, et al. (2022) carried out tests under different relative magnitudes of the BM and UBM (by setting different configurations for idealized simulation) in order to mimic various dynamical regimes in the real‐ocean scenarios. Lguensat et al.…”

“…Wang, Grisouard, et al. (2022) used Generative Adversarial Network (GAN) to extract internal tides (IT) from an idealized eddying simulated SSH snapshot. Lguensat et al.…”