Learning effective Sargassum transport dynamics from limited drifter observations
Floating-material transport is influenced by unresolved processes that are often absent from available circulation products. We develop a data-driven transport-learning framework for learning effective transport corrections from limited Lagrangian observations using physically motivated ocean--atmosphere diagnostics and finite-memory representations motivated in part by inertial-particle memory effects. The diagnostic representation is analyzed through predictive and sparse symbolic-discovery approaches under leave-one-trajectory-out validation. Applications to Sargassum-following drifters in the Puerto Rico region and the Gulf Stream show that the diagnostics contain transport-relevant i...
| Authors | F. J. Beron-VEra, M. J. Olascoaga, J. Morell et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 28 May 2026 |
| Last updated at source | 28 May 2026 |
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