Physics-guided Statistical Data Fusion for Reconstructing 3D Current Fields of Oceanic Eddies

We propose a physics-guided modeling and learning framework for multi-source data fusion to estimate the three-dimensional (3D) current structure of oceanic eddies by integrating satellite altimetry and temperature data, ocean reanalysis data, and in situ drifting buoy observations.

The approach leverages geostrophic balance, derived from the Navier-Stokes equations, to guide a neural network trained on GLORYS reanalysis data in inferring subsurface currents from surface conditions. The surface conditions were estimated using a high-dimensional linear mixed model, which integrates systematically biased satellite altimetry and sparse drifting buoy data, allowing for spatially adaptive bias correction and yielding more accurate and spatially coherent surface velocity fields.