The validation of HF radar systems remains an area with significant scope for advancement, particularly in terms of linking data quality with system operational parameters, fully utilizing the potential of redundant data (e.g., overlapping radial measurements), and accurately capturing the spatiotemporal variability observed by independent devices, such as drifters. In this study, we conducted a large-scale Lagrangian measurement campaign in the Tuscan Archipelago, aimed at validating surface current data from the HF radar network. This radar network, a recent addition to the area, monitors an oceanographic region critical to Mediterranean dynamics. The validation was executed using different approaches: a Eulerian method, comparing the radial velocities measured by radar with drifter-derived velocities along radial directions; a Lagrangian method, contrasting the observed drifter trajectories with the synthetic virtual trajectories generated from radar-based flow fields; and radar-to-radar comparisons with the concurrent utilization of two radars in same point. Through fine-tuning of the quality control parameters and an analysis of the impact of different thresholds of such parameters, we assessed the radar’s ability to capture dynamic processes, identifying both strengths and limitations. Our results not only confirm the utility of HF radar in coastal monitoring but also provide a basis for improving calibration strategies, ultimately supporting more accurate, high-resolution radar observations in complex marine environments.

In a previous study an improved Maximum Cross-Correlation technique, called Multi-Window Maximum Cross-Correlation (MW-MCC), was proposed, and applied to noise-free synthetic images in order to show its potential and limits in oceanographic applications. In this work, instead, the application of MW-MCC to high resolution MODIS images, and its capability to provide useful and realistic results for ocean currents, is studied. When applied to real satellite images, the MW-MCC is subject to cloud cover and image quality problems. As a con-sequence the number of useful MODIS images is greatly reduced. However, for every MODIS image, multiple spec-tral bands are available, and it is possible to apply the MW-MCC algorithm to the same scene as many times as the number of these bands, increasing the possibility of finding valid current vectors. Moreover, the comparison among the results from different spectral bands allows to verify both the consistency of the computed current vectors and the validity of using a spectral band as a good tracer for the ocean circulation. Due to the lack of systematic current measurements in the area considered, it has been not possible to perform an ex-tensive error analysis of the MW-MCC results, although a case study of a comparison between HF radar measurements and MW-MCC data is shown. Moreover, some comparison between numerical ocean model simulations and MW-MCC results are also shown. The coherence of the resulting circulation flow, the high number of current vectors found, the agreement among different spectral bands, and conformity with the currents measured by the HF radars or simulated by hydrodynamic models show the validity of the technique.

High-Frequency (HF) radar systems provide high-resolution observations of surface currents in coastal oceans, but their effective representation strongly depends on the spatial discretization used to combine radial measurements. Standard regular grids impose a uniform resolution across the domain, despite the highly heterogeneous sampling geometry of HF radar systems, leading to inefficient use of observations and limited effective resolution in key coastal regions. We propose a fully automated, non-uniform adaptive gridding framework specifically designed for HF radar applications. The method defines grid nodes directly from the spatial distribution of radar observations, redistributing resolution according to local data availability while preserving a consistent discretization of the domain. The resulting unstructured grid naturally refines resolution in densely sampled near‑coastal areas and adopts coarser spacing offshore. Grid nodes can be associated with either Voronoi polygons or equivalent‑area circular supports, providing flexibility in the geometrical representation of spatial support without altering node placement. The proposed discretization yields a more realistic spatial representation of HF radar surface currents, reducing artefacts linked to uniform gridding and enhancing the visibility of coastal circulation structures. Comparison with surface drifter observations indicates that this improved spatial coherence is achieved without compromising overall reconstruction skill. The framework provides a radar‑aware basis for current mapping, interpolation, and data assimilation applications where geometrical consistency and adaptive resolution are critical.

High-Frequency (HF) radar systems provide high-resolution observations of surface currents in coastal oceans, but their effective representation strongly depends on the spatial discretization used to combine radial measurements. Standard regular grids impose a uniform resolution across the domain, despite the highly heterogeneous sampling geometry of HF radar systems, leading to inefficient use of observations and limited effective resolution in key coastal regions. We propose a fully automated, non-uniform adaptive gridding framework specifically designed for HF radar applications. The method defines grid nodes directly from the spatial distribution of radar observations, redistributing resolution according to local data availability while preserving a consistent discretization of the domain. The resulting unstructured grid naturally refines resolution in densely sampled near‑coastal areas and adopts coarser spacing offshore. Grid nodes can be associated with either Voronoi polygons or equivalent‑area circular supports, providing flexibility in the geometrical representation of spatial support without altering node placement. The proposed discretization yields a more realistic spatial representation of HF radar surface currents, reducing artefacts linked to uniform gridding and enhancing the visibility of coastal circulation structures. Comparison with surface drifter observations indicates that this improved spatial coherence is achieved without compromising overall reconstruction skill. The framework provides a radar‑aware basis for current mapping, interpolation, and data assimilation applications where geometrical consistency and adaptive resolution are critical.