Merge pull request #256 from Parcels-code/medina-rubio_paper

Adding Medina-Rubio paper

Author: erikvansebille

src/data/papers-citing-parcels.ts

@@ -2782,4 +2782,14 @@ export const papersCitingParcels: Paper[] = [
     abstract:
       'Forward- and backward-in-time Lagrangian advection, used to determine fate and origin of material in the ocean, are mathematically consistent. However, their numerical computations are hampered by round-off and truncation errors. Trajectory calculations are stable to errors (i.e., errors are dampened) in zones of velocity convergence and unstable (errors are amplified) in regions of divergence. The stability to errors thus flips when time integration is reversed, which, depending on the numerical configuration, can lead to significant discrepancies between forward- and backward-in-time trajectories. As divergence statistics can be asymmetrical and may be inhomogeneously distributed in space, this can lead to what we call the “stability bias.” Using representative numerical set-ups, we show that already for timescales of less than half a year, there can be systematic basin-scale biases in which regions are identified as particle origins or sinks. While the stability bias is linked to divergence, it is not only limited to 2D trajectories in 3D flows, as we discuss how inappropriate treatment of surface boundary conditions in 3D Lagrangian studies can also introduce an effective non-zero divergence. Backtracking is typically applied to material that has accumulated in convergent zones, for which the stability bias especially impedes source attribution studies. Furthermore, we show how discrepancies between forward and backward trajectories can make a Bayesian approach to backtracking unsuitable. We advise modelers to routinely compare forward- and backward trajectories and assess the bias in different numerical set-ups to increase study robustness. Analytical integration methods are less error-prone and may be preferred over RK4.',
   },
+  {
+    title:
+      'Using surface drifters to characterise near-surface ocean dynamics in the southern North Sea: a data-driven approach',
+    published_info: 'Ocean Science, 22, 49-74',
+    authors:
+      'Medina-Rubio, J, M Nussbaum, TS van den Bremer, E van Sebille  (2026)',
+    doi: 'https://doi.org/10.5194/os-22-49-2026',
+    abstract:
+      'The large size of traditional drifters limits their ability to mimic the transport of buoyant objects at the ocean surface, which are subject to complex interactions among direct wind drag, fast-moving surface currents, and wave-induced transport. To better capture these dynamics, we track the trajectories of 12 novel, ultra-thin surface drifters deployed in the southern North Sea over 68 d. We adopt a data-driven approach to model drifter velocity using hydrodynamic and atmospheric data, applying both a linear leeway parameterisation and two machine learning models: random forest and support vector regression. Machine learning model-agnostic interpretation techniques reveal that tidal forcing predominantly drives zonal motion, whereas wind is the main driver in the meridional direction in this region. Notably, the wind exhibits a saturation effect, and its contribution to explaining the variance of the drifter velocity decreases at higher speeds. In trajectory prediction experiments, we find that machine learning models, particularly random forest, outperform linear models, with the latter achieving comparable accuracy only at short time scales. Using a hybrid approach and deriving a non-linear function of the wind from machine learning interpretable methods to include in the leeway parameterisation significantly improves the model prediction of the drifter trajectory. Finally, we test the generalisability of the North Sea-trained models using an independent drifter dataset from the Tyrrhenian Sea. Despite the differences in ocean dynamics between the regions, the machine learning models reproduce the observed trajectories with comparable accuracy to state-of-the-art studies, demonstrating robust explanatory skill and a low degree of overfitting in this instance.',
+  },
 ]