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. High-frequency radar, HFR, is a cost-effective monitoringtechnique that allows us to obtain high-resolution continuous surface currents,providing new insights for understanding small-scale transport processes inthe coastal ocean. In the last years, the use of Lagrangian metrics to studymixing and transport properties has been growing in importance. A common conditionamong all the Lagrangian techniques is that complete spatial and temporalvelocity data are required to compute trajectories of virtual particles in theflow. However, hardware or software failures in the HFR system can compromise theavailability of data, resulting in incomplete spatial coverage fields orperiods without data. In this regard, several methods have been widely usedto fill spatiotemporal gaps in HFR measurements. Despite the growingrelevance of these systems there are still many open questions concerning thereliability of gap-filling methods for the Lagrangian assessment ofcoastal ocean dynamics. In this paper, we first develop a new methodology toreconstruct HFR velocity fields based on self-organizing maps (SOMs). Then, acomparative analysis of this method with other available gap-fillingtechniques is performed, i.e., open-boundary modal analysis (OMA) and datainterpolating empirical orthogonal functions (DINEOFs). The performance ofeach approach is quantified in the Lagrangian frame through the computationof finite-size Lyapunov exponents, Lagrangian coherent structures andresidence times. We determine the limit of applicability of each methodregarding four experiments based on the typical temporal and spatial gapdistributions observed in HFR systems unveiled by a K-means clusteringanalysis. Our results show that even when a large number of data are missing,the Lagrangian diagnoses still give an accurate description of oceanictransport properties.

Impact of HF radar current gap-filling methodologies on the Lagrangian assessment of coastal dynamics