A Machine Learning-Based Model Infers the Sea Surface Velocity of Surface Water and Ocean Topography (SWOT)

Abstract

High-resolution sea surface velocity (SSV) is crucial for advancing our understanding of ocean sub-mesoscale processes, energy cascades, etc. The recently launched Surface Water and Ocean Topography (SWOT) satellite measures sea surface height with a sub-mesoscale resolved resolution. Based on geostrophic balance, the so-called geostrophic velocity in SWOT is estimated. Although the SWOT-derived velocity is not true geostrophic velocity as it does not consider the separation of balanced and unbalanced motions, it offers valuable insights into both geostrophic and ageostrophic velocities. Here we propose a machine learning-based model to infer the SSV using SWOT and drifter data. The result demonstrates the error between the geostrophic velocities from SWOT and the total velocities from the drifter are reduced by about 50%. Furthermore, the kinetic energy of inferred velocities aligns more closely with reanalysis data, particularly at low latitudes. This study thus presents a promising approach for inferring global SSV using SWOT data.