Drift Field Net: Learning Ocean Lagrangian advection fields from in-situ and satellite observations

Drift Field Net (DFN) is a deep neural network that predicts ocean surface flow fields from satellite observations, trained via a two‑stage strategy combining simulated data pretraining and Lagrangian fine‑tuning with an advection‑consistent loss. DFN improves particle trajectory forecasts, reducing mean positioning error by 20 km over a 7‑day period compared to an operational physics‑based model, and further decreasing error by 10 km when the advection loss is applied. The study demonstrates that incorporating Lagrangian constraints into deep‑learning training enhances ocean surface flow prediction accuracy.

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• fine-tuning