Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications - Révelard, Adèle; Reyes, Emma; Mourre, Baptiste; Hernández-Carrasco, Ismael; Rubio, Anna; Lorente, Pablo; Fernández, Christian De Lera; Mader, Julien; Álvarez-Fanjul, Enrique; Tintoré, Joaquín (Frontiers in Marine Science, 2021)
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.