Articles | Volume 22, issue 1
https://doi.org/10.5194/os-22-49-2026
https://doi.org/10.5194/os-22-49-2026
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

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Cited articles

Aksamit, N. O., Sapsis, T., and Haller, G.: Machine-Learning Mesoscale and Submesoscale Surface Dynamics from Lagrangian Ocean Drifter Trajectories, J. Phys. Oceanogr., 50, 1179–1196, https://doi.org/10.1175/JPO-D-19-0238.1, 2020. a
Allen, A. A.: Leeway Divergence, Tech. Rep. CG-D-05-05, US Coast Guard Research and Development Center, https://apps.dtic.mil/sti/pdfs/ADA435435.pdf (last access: 18 December 2025), 2005. a
Apley, D. W. and Zhu, J.: Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models, J. Roy. Stat. Soc. Ser. B, 82, 1059–1086, https://doi.org/10.1111/rssb.12377, 2020. a, b
Apple Inc.: AirTag – Technical Specifications, Apple Inc., https://support.apple.com/en-us/111847 (last access: 18 December 2025), 2021. a
Behrens, T., Schmidt, K., MacMillan, R. A., and Viscarra Rossel, R. A.: Multiscale contextual spatial modelling with the Gaussian scale space, Geoderma, 310, 128–137, https://doi.org/10.1016/j.geoderma.2017.09.015, 2018. a
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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.
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