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

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 trajectory simulation. This configuration reduces separation distance by over 50\% and significantly decreases normalized cumulative Lagrangian separation and metrics related to velocities autocorrelation functions compared to the baseline using SSC alone. On the other hand, the inclusion of sea surface temperature (SST) either alone or in combination with SSC generally degrades performance. In B2, using satellite-derived SSH, Ekman and winds velocities improves surface drifters trajectories simulation, particularly in the North East Pacific. While the satellite-derived SST in combination with reanalysis-based SSC configuration leads to better trajectories simulation in the Gulf Stream. Overall, we highlight the added value of combining multiple geophysical fields to improve Lagrangian drift simulation on both numerical and real-world experiments.

Figures (5)

• Figure 1: Training and evaluation procedures of multivariate DriftNet's extension: the input geophysical fields $\mathbf{g}$ over 9 days (here zonal $\mathbf{U}$ and meridional $\mathbf{V}$ components of the velocity field, SST and SSH) coupled to the intial spatio-temporal positional encoding $\mathbf{y_{0}}$ are fed to the DriftNet in order for it to generate the target trajectories to be compared to the ground-truth ones. Once DriftNet is trained, those geophysical fields are then used as input to it in order to simulate Lagrangian trajectories which are then evaluated on the set of test trajectories using evaluation metrics described in Section \ref{['sec:eval_metrics']}.
• Figure 2: North East Pacific: SSC velocity in m/s, SST in $^{\circ} C$, SSH in m, wind velocity in m/s, Ekman in m/s. Snapshot on 01/06/2015 12:00:00 UTC.
• Figure 3: Gulf Stream: SSC velocity in m/s, SST in $^{\circ} C$, SSH in m, wind velocity in m/s, Ekman component in m/s. Snapshot on 01/06/2015 12:00:00 UTC.
• Figure 4: Examples of simulated trajectories for Benchmark B1: Panel (a): North East Pacific, Panel (b): Gulf Stream. The reference trajectories simulated with Ocean Parcels using Nature Run SSC L1, the baseline ones L2 simulated using SSC from OSSE. Eight randomly-selected trajectories are superimposed to the mean relative vorticity of the Nature Run SSC.
• Figure 5: Examples of simulated trajectories for Benchmark B2: Panel (a): North East Pacific, Panel (b): Gulf Stream. We depict real drifters trajectories in black (L3) and trajectories simulated with DriftNet using various input geophysical fields. Eight randomly-selected trajectories are superimposed to the mean relative vorticity of GLORYS12.