Using surface drifters to characterise near-surface ocean dynamics in the southern North Sea: a data-driven approach

Medina-Rubio, Jimena; Nussbaum, Madlene; van den Bremer, Ton S.; van Sebille, Erik

doi:10.5194/os-22-49-2026

Articles | Volume 22, issue 1

https://doi.org/10.5194/os-22-49-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/os-22-49-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 1

Research article

|

07 Jan 2026

Research article |

| 07 Jan 2026

Using surface drifters to characterise near-surface ocean dynamics in the southern North Sea: a data-driven approach

Jimena Medina-Rubio, Madlene Nussbaum, Ton S. van den Bremer, and Erik van Sebille

Supplement

https://doi.org/10.5194/os-22-49-2026-supplement

Data sets

North Sea drifter trajectories 2024 Erik van Sebille https://doi.org/10.5281/zenodo.14198921

Tyrrhenian Sea drifter trajectories 2025 Erik van Sebille https://doi.org/10.5281/zenodo.14198920

Random forest models trained on North Sea drifter trajectories Jimena Medina Rubio https://doi.org/10.5281/zenodo.17901303

Support vector regression models trained on North Sea drifter trajectories Jimena Medina Rubio https://doi.org/10.5281/zenodo.17901907

Model code and software

ML surface drifters analysis Jimena Medina-Rubio https://github.com/jimena-medinarubio/ML_surface-drifters.git

Short summary

We study how tides, wind, and waves interact at the ocean surface by tracking ultra-thin drifters released in the southern North Sea for two months. Using model data together with data-driven machine learning models, we determine the relative contribution of each forcing mechanism in driving the drifters' velocity and improve the prediction of their trajectories. We also test the generalisability of this method by applying it to the same drifters in the Tyrrhenian Sea.