Learning Implicit Subsurface Velocity Fields from Argo Hydrography Using Physics-Informed Neural Emulation

Abstract: Argo floats provide a global dataset of subsurface temperature and salinity profiles but lack direct velocity observations. This limits the reconstruction of Lagrangian ocean transport using the Argo data. We propose a physics-informed machine learning emulator that infers latent horizontal velocity fields from Argo hydrographic observations. The model learns a neural velocity representation using 3D temperature–salinity gradients, which is constrained by advection–diffusion equations. This approach implicitly recovers flow patterns that are consistent with the observed changes in properties and enables the simulation of synthetic trajectory without the input of explicit velocity data. Sparse years are handled via physics-based self-supervision and spatio-temporal regularization. Preliminary experiments in the Mediterranean Sea demonstrate that the learned velocities reproduce qualitatively the known major gyres and boundary currents, achieving realistic float displacements and energy spectra that are comparable to those in reanalysis fields. This framework offers a new way to reconstruct Lagrangian dynamics directly from hydrography, providing an efficient, observation-driven alternative to numerical trajectory modeling.