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Search and rescue (SAR) modeling applications, mostly based on Lagrangian tracking particle algorithms, rely on the accuracy of met-ocean forecast models. Skill assessment methods are therefore required to evaluate the performance of ocean models in predicting particle trajectories. The Skill Score (SS), based on the Normalized Cumulative Lagrangian Separation (NCLS) distance between simulated and satellite-tracked drifter trajectories, is a commonly used metric. However, its applicability in coastal areas, where most of the SAR incidents occur, is difficult and sometimes unfeasible, because of the high variability that characterizes the coastal dynamics and the lack of drifter observations. In this study, we assess the performance of four models available in the Ibiza Channel (Western Mediterranean Sea) and evaluate the applicability of the SS in such coastal risk-prone regions seeking for a functional implementation in the context of SAR operations. We analyze the SS sensitivity to different forecast horizons and examine the best way to quantify the average model performance, to avoid biased conclusions. Our results show that the SS increases with forecast time in most cases. At short forecast times (i.e., 6 h), the SS exhibits a much higher variability due to the short trajectory lengths observed compared to the separation distance obtained at timescales not properly resolved by the models. However, longer forecast times lead to the overestimation of the SS due to the high variability of the surface currents. Findings also show that the averaged SS, as originally defined, can be misleading because of the imposition of a lower limit value of zero. To properly evaluate the averaged skill of the models, a revision of its definition, the so-called SS∗, is recommended. Furthermore, whereas drifters only provide assessment along their drifting paths, we show that trajectories derived from high-frequency radar (HFR) effectively provide information about the spatial distribution of the model performance inside the HFR coverage. HFR-derived trajectories could therefore be used for complementing drifter observations. The SS is, on average, more favorable to coarser-resolution models because of the double-penalty error, whereas higher-resolution models show both very low and very high performance during the experiments.

Windrow is a long-established term for the aggregations of seafoam, seaweeds, plankton and natural debris that appear on the ocean surface. Here, we define a “litter windrow” as any aggregation of floating litter at the submesoscale domain (<10 km horizontally), regardless of the force inducing the surface convergence, be it wind or other forces such as tides or density-driven currents. The marine litter windrows observed to date usually form stripes from tens up to thousands of meters long, with litter densities often exceeding 10 small items (<2 cm) per m2 or 1 large item (>2 cm) per 10 m2. Litter windrows are generally overlooked in research due to their dispersion, small size and ephemeral nature. However, applied research on windrows offers unique possibilities to advance on the knowledge and management of marine litter pollution. Litter windrows are hot spots of interaction with marine life. In addition, since the formation of dense litter windrows requires especially high loads of floating litter in the environment, their detection from space-borne sensors, aerial surveys or other platforms might be used to flag areas and periods of severe pollution. Monitoring and assessing of management plans, identification of pollution sources, or impact prevention are identified as some of the most promising fields of application for the marine litter windrows. In the present Perspective, we develop a conceptual framework and point out the main obstacles, opportunities and methodological approaches to address the study of litter windrows.