Drift Field Net: Learning Ocean Lagrangian advection fields from in-situ and satellite observations
DFN is a deep neural network that predicts ocean surface flow fields from operational satellite observations that reduces the mean positioning error after a 7-day forecast compared with the operational model and demonstrates the potential of deep learning for ocean surface flow prediction.
T. Archambault, Pierre Garcia, Mattia Romero et al.
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