[2604.03292] Impact of geophysical fields on Deep Learning-based Lagrangian drift simulations

Physics > Atmospheric and Oceanic Physics arXiv:2604.03292 (physics) [Submitted on 27 Mar 2026] Title:Impact of geophysical fields on Deep Learning-based Lagrangian drift simulations Authors:Daria Botvynko (Lab-STICC_OSE, IMT Atlantique - MEE, IMT Atlantique), Carlos Granero-Belinchon (ODYSSEY, IMT Atlantique - MEE, Lab-STICC_OSE), Simon Van Gennip (MOi), Abdesslam Benzinou (ENIB), Ronan Fablet (IMT Atlantique - MEE, Lab-STICC_OSE, ODYSSEY) View a PDF of the paper titled Impact of geophysical fields on Deep Learning-based Lagrangian drift simulations, by Daria Botvynko (Lab-STICC_OSE and 10 other authors View PDF Abstract:We assess the influence of different Eulerian geophysical input fields on Lagrangian drift simulations using DriftNet, a learning-based method designed to simulate Lagrangian drift on the sea surface. Two experiments are conducted: a fully numerical experiment (Benchmark B1) and a real-world drifters-based experiment (Benchmark B2). Both experiments are performed in two regions with different ocean dynamics: North East Pacific and Gulf Stream regions. The performance of DrifNet is evaluated with three different metrics: separation distance between simulated and ground-truth trajectories, the normalized cumulative Lagrangian separation and the autocorrelation of Lagrangian velocities. In both regions, results from B1 show that combining assimilated sea surface currents (SSC) with fully observed sea surface height (SSH) leads to greatest improvement in traj...