Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics

Abstract

Ocean eddies help shape marine ecosystems, large-scale ocean circulation, and global climate through their non-linear interactions. Observing eddies poses a major challenge due to their chaotic evolution across a wide range of spatio-temporal scales. Satellite-derived estimates of surface ocean currents significantly distort and smooth eddies and, consequently, strongly underestimate the strength of non-linear eddy interactions. Here, we use deep learning to develop a new global estimate of surface currents from satellite observations, capturing ocean eddies with accuracy and resolution that surpasses state-of-the-art reconstructions. We achieved this by synthesising satellite observations of sea surface height with sea surface temperature across the Global Ocean using a single neural network that generalises across regions with diverse eddy dynamics. Our new reconstructions reveal dramatic changes in the inferred eddy dynamics in many regions, highlighting the existence of strongly seasonal non-linear eddy interactions. These eddy interactions transfer large amounts of kinetic energy from small to large scales, emphasising the prominent role of small-scale eddies in setting the seasonal cycle of kinetic energy in the ocean. Our study demonstrates that deep learning can dramatically improve our observations of ocean eddy dynamics from space, providing a new paradigm for satellite oceanography.

Ocean Satellite Data Fusion for High-Resolution Surface Current Maps

“…Nardelli et al [7] use simulated data from the Mediterranean Sea [38] to reconstruct SSH and surface currents. In more recent developments, Archambault et al [41] and Martin et al [42,43] present training strategies for learning the SSH field without simulated data by computing loss only along the tracks of SSH observations. Self-supervised or unsupervised methods can help close the domain gap between simulations (often in precise geographical contexts) and real data at a global scale.…”