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

Abstract

Abstract. In coastal basins HF radar, HFR, is a cost-effective monitoring technique that allows to obtain high-resolution continuous surface currents, providing new insights for understanding small-scale coastal ocean transport and dispersion processes. In the last years the use of Lagrangian diagnosis to study mixing and transport properties is growing in importance. A common condition among all the Lagrangian techniques is that complete spatial and temporal velocity data is required to compute the trajectories of virtual particles in the flow. However, hardware or software failures in HFR can compromise the availability of data, resulting in incomplete spatial coverage fields or non-data periods. In this regard, several methods have been widely used to fill spatio-temporal gaps in HFR measurements. Despite the growing relevance of these systems there are still many open questions concerning the reliability of the gap-filling methods for the Lagrangian assessment of the coastal ocean dynamics. In this paper, we first develop a new methodology to fill gaps in the HFR velocity field based on Self-Organizing Maps (SOM). Then a comparative analysis of this method with the most extended available methods for gap-filling in HFR systems, i.e., Open-Boundary Modal Analysis (OMA) and Data Interpolating Empirical Orthogonal Functions (DINEOF), is performed. The error introduced by each approach is quantified in the Lagrangian computations, i.e., 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 gaps distribution observed in HFR systems unveiled by a k-means clustering analysis. The data sets used for this study are the hourly radial velocities obtained from the HFR system located in the SE Bay of Biscay, with a spatial resolution of 5 km in range and 5º in angle. Our, results, show that even when the number of missing data is large, the Lagrangian diagnosis still give an accurate description of the oceanic transport properties.