Neural prediction of Lagrangian drift trajectories on the sea surface

Abstract This study proposes a new Deep Learning approach for the simulation of Lagrangian drift at the sea surface with the objective to overcome current limitations of existing model-based and learning-based methods. The proposed framework, called DriftNet, is inspired by the Eulerian Fokker-Planck representation of Lagrangian drift. DriftNet is able to simulate the Lagrangian trajectory of a fluid parcel given the corresponding Eulerian Sea Surface Currents and the spatially-explicit encoding of the parcel’s initial position. The efficacy of DriftNet is demonstrated through three benchmarks: two benchmarks involving fully simulated ocean data and one combining operational ocean reanalysis model along with in-situ drifters data. The study focuses on two regions with different dynamical regimes: North East Pacific and Gulf Stream. The findings indicate that DriftNet outperforms current state-of-the-art model-based and learning-based methods. Additionally, this study explores how Sea Surface Height - derived from both modeled and observed ocean - affects Lagrangian drift simulations when used as input to DriftNet.