  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.
