Machine-Learning Ocean Dynamics from Lagrangian Drifter Trajectories

Abstract

Lagrangian ocean drifters provide highly accurate approximations of ocean surface currents but are sparsely located across the globe. As drifters passively follow ocean currents, there is minimal control on where they will be making measurements, providing limited temporal coverage for a given region. Complementary Eulerian velocity data is available with global coverage, but is itself limited by the spatial and temporal resolution possible with satellite altimetry measurements. Additionally, altimetry measurements approximate geostrophic components of ocean currents but neglect smaller sub-mesoscale motions and require smoothing and interpolation from raw satellite track measurements. In an effort to harness the rich dynamics available in ocean drifter datasets, we have trained a recurrent neural network on the time history of drifter motion to minimize the error in a reduced-order Maxey-Riley drifter model. This approach relies on a slow-manifold approximation to determine the most mathematically-relevant variables with which to train, subsequently improving the temporal and spatial resolution of the underlying velocity field. By adding this neural network component, we also correct drifter trajectories near sub-mesoscale features missed by deterministic models using only satellite and wind reanalysis data. The effect of varying similarity between training and testing trajectory datasets for the blended model was evaluated, as was the effect of seasonality in the Gulf of Mexico.