Disentangling Internal Tides from Balanced Motions with Deep Learning and Surface Field Synergy

TL;DR

This paper demonstrates that a simplified deep learning approach effectively disentangles internal tides from surface ocean data, emphasizing the importance of surface velocity and multi-platform observations.

Contribution

It introduces a computationally efficient deep learning algorithm that leverages surface fields, especially velocity, to extract internal tide signatures from satellite data.

Findings

All surface fields contribute to IT disentanglement, with velocity being most informative.

The simpler algorithm performs comparably to previous complex models when learning rate is annealed.

Surface velocity and scattering-medium information are crucial for accurate internal tide extraction.

Abstract

A fundamental challenge in ocean dynamics is disentangling balanced motions and internal waves. Extracting internal tidal (IT) imprints from surface data is a central part of this challenge. Traditional harmonic analysis can fail under strong incoherence and poor temporal sampling, as in global satellite observations. New wide-swath satellites provide two-dimensional spatial coverage, allowing IT extraction to be reformulated as image translation. Building on our earlier deep-learning approach for extracting IT signatures from sea surface height (SSH) in an idealized turbulent simulation, we show that a simpler, computationally cheaper algorithm performs comparably in our experiments when the learning rate is annealed during training. Using this algorithm, we test different combinations of surface inputs: SSH, surface temperature, and surface velocity. All fields contribute synergistically to disentanglement in our deterministic benchmark, with surface velocity by far the most informative. These findings underscore the value of coordinated multi-platform observations and highlight the importance of surface velocity for separating balanced motions and internal waves. Additional analysis shows that both wave-signature information and scattering-medium information aid IT extraction. To exploit large-scale, mesoscale-reaching information in the scattering medium, the algorithm must be highly non-local. Residual errors concentrate at small spatial scales near mode-2 tidal wavelengths, likely reflecting incomplete input information, uncertainty in the simulation-derived reference fields, including possible Doppler-shift contamination, and limitations of the present deterministic architecture.