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The use of high-frequency radar (HFR) data is increasing worldwide for different applications in the field of operational oceanography and data assimilation, as it provides real-time coastal surface currents at high temporal and spatial resolution. In this work, a Lagrangian-based, empirical, real-time, short-term prediction (L-STP) system is presented in order to provide short-term forecasts of up to 48 h of ocean currents. The method is based on finding historical analogs of Lagrangian trajectories obtained from HFR surface currents. Then, assuming that the present state will follow the same temporal evolution as the historical analog, we perform the forecast. The method is applied to two HFR systems covering two areas with different dynamical characteristics: the southeast Bay of Biscay and the central Red Sea. A comparison of the L-STP methodology with predictions based on persistence and reference fields is performed in order to quantify the error introduced by this approach. Furthermore, a sensitivity analysis has been conducted to determine the limit of applicability of the methodology regarding the temporal horizon of Lagrangian prediction. A real-time skill score has been developed using the results of this analysis, which allows for the identification of periods when the short-term prediction performance is more likely to be low, and persistence can be used as a better predictor for the future currents.

High-frequency radar, HFR, is a cost-effective monitoring technique that allows us to obtain high-resolution continuous surface currents, providing new insights for understanding small-scale transport processes in the coastal ocean. In the last years, the use of Lagrangian metrics to study mixing and transport properties has been growing in importance. A common condition among all the Lagrangian techniques is that complete spatial and temporal velocity data are required to compute trajectories of virtual particles in the flow. However, hardware or software failures in the HFR system can compromise the availability of data, resulting in incomplete spatial coverage fields or periods without data. In this regard, several methods have been widely used to fill spatiotemporal gaps in HFR measurements. Despite the growing relevance of these systems there are still many open questions concerning the reliability of gap-filling methods for the Lagrangian assessment of coastal ocean dynamics. In this paper, we first develop a new methodology to reconstruct HFR velocity fields based on self-organizing maps (SOMs). Then, a comparative analysis of this method with other available gap-filling techniques is performed, i.e., open-boundary modal analysis (OMA) and data interpolating empirical orthogonal functions (DINEOFs). The performance of each approach is quantified in the Lagrangian frame through the computation of finite-size Lyapunov exponents, Lagrangian coherent structures and residence times. We determine the limit of applicability of each method regarding four experiments based on the typical temporal and spatial gap distributions observed in HFR systems unveiled by a K-means clustering analysis. Our results show that even when a large number of data are missing, the Lagrangian diagnoses still give an accurate description of oceanic transport properties.

The southeastern Bay of Biscay has been described as a "dead end" for floating marine litter, often accumulating along small-scale linear streaks. Coastal Current Convergence Structures (CCS), often associated with vertical motions at river plume edges, estuarine fronts, or other physical processes, can be at the origin of the accumulation. Understanding the formation of CCS and their role in the transport of marine litter is essential to better quantify and to help mitigate marine litter pollution. The Lagrangian framework, used to estimate the absolute dispersion, and the finite-size Lyapunov exponents (FSLE), have proved very effective for identifying CCS in the current velocity field. However, the quality of CCS identification depends strongly on the Eulerian fields. Two surface current velocity data sets were used in the analysis: the remotely sensed velocities from the EuskOOS High-Frequency Radar (HFR) network and velocities from three-dimensional model outputs. They were complemented by drifting buoy velocity measurements. An optimization method, involving the fusion of drifting buoys and HFR velocities is proposed to better reconstruct the fine-scale structure of the current velocity field. Merging these two sources of velocity data reduced the mean Lagrangian error and the Root Mean Square Error (RMSE) by 50 % and 30 % respectively, significantly improving velocity reconstruction. FSLE ridgelines obtained from the Lagrangian analysis of optimized velocities were compared with remotely sensed concentrations of Chlorophyll-a. It was shown that ridgelines control the spatial distribution of phytoplankton. They fundamentally represent the CCS which can potentially affect marine litter aggregation. Analysis of the absolute dispersion revealed large stirring in the alongshore direction which was also confirmed by spatial distribution of FSLE ridgelines. The alignment between FSLE ridgelines and patterns of high Chlorophyll-a concentration was observed, often determining the limits of river plume expansion in the study area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)