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The Mediterranean Sea is a prominent climate-change hot spot, with many socioeconomically vital coastal areas being the most vulnerable targets for maritime safety, diverse met-ocean hazards and marine pollution. Providing an unprecedented spatial and temporal resolution at wide coastal areas, high-frequency radars (HFRs) have been steadily gaining recognition as an effective land-based remote sensing technology for continuous monitoring of the surface circulation, increasingly waves and occasionally winds. HFR measurements have boosted the thorough scientific knowledge of coastal processes, also fostering a broad range of applications, which has promoted their integration in coastal ocean observing systems worldwide, with more than half of the European sites located in the Mediterranean coastal areas. In this work, we present a review of existing HFR data multidisciplinary science-based applications in the Mediterranean Sea, primarily focused on meeting end-user and science-driven requirements, addressing regional challenges in three main topics: (i) maritime safety, (ii) extreme hazards and (iii) environmental transport process. Additionally, the HFR observing and monitoring regional capabilities in the Mediterranean coastal areas required to underpin the underlying science and the further development of applications are also analyzed. The outcome of this assessment has allowed us to provide a set of recommendations for future improvement prospects to maximize the contribution to extending science-based HFR products into societally relevant downstream services to support blue growth in the Mediterranean coastal areas, helping to meet the UN's Decade of Ocean Science for Sustainable Development and the EU's Green Deal goals., peer-reviewed

Due to the semi-enclosed nature of the Mediterranean Sea, natural disasters and anthropogenic activities impose stronger pressures on its coastal ecosystems than in any other sea of the world. With the aim of responding adequately to science priorities and societal challenges, littoral waters must be effectively monitored with high-frequency radar (HFR) systems. This land-based remote sensing technology can provide, in near-real time, fine-resolution maps of the surface circulation over broad coastal areas, along with reliable directional wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network and the future roadmap for orchestrated actions. Ongoing collaborative efforts and recent progress of this regional alliance are not only described but also connected with other European initiatives and global frameworks, highlighting the advantages of this cost-effective instrument for the multi-parameter monitoring of the sea state. Coordinated endeavors between HFR operators from different multi-disciplinary institutions are mandatory to reach a mature stage at both national and regional levels, striving to do the following: (i) harmonize deployment and maintenance practices; (ii) standardize data, metadata, and quality control procedures; (iii) centralize data management, visualization, and access platforms; and (iv) develop practical applications of societal benefit that can be used for strategic planning and informed decision-making in the Mediterranean marine environment. Such fit-for-purpose applications can serve for search and rescue operations, safe vessel navigation, tracking of marine pollutants, the monitoring of extreme events, the investigation of transport processes, and the connectivity between offshore waters and coastal ecosystems. Finally, future prospects within the Mediterranean framework are discussed along with a wealth of socioeconomic, technical, and scientific challenges to be faced during the implementation of this integrated HFR regional network., This study has been partially developed in the framework of the Interreg MED Strategic Project SHAREMED, co-financed by the European Regional Development Fund under the funding program Interreg MED 2014–2020. Website: https://sharemed.interreg-med.eu/ (last access: 31 March 2022). We are also grateful for the partial support of the (i) the project PO FEAMP 2014/2020 (Misura 2.51) funded by Regione Campania (Italy), (ii) the 2017 PRIN project EMME (Exploring the fate of Mediterranean microplastic: from distribution pathways to biological effects) funded by the Italian Ministry for Research (grant agreement no. 2017WERYZP), and (iii) the CMEMS-INSTAC phase II, which provides the context of the activities for HFR data harmonization, standardization, and distribution. Collaborative discussion on data management harmonization at the European level has also been carried out thanks to the contribution of the projects INCREASE (CMEMS Service Evolution Call for Tenders 21-SE-CALL1) and SeaDataCloud (EU-H2020 GA no. 730960).

HF radar (HFR) is recognized as a cost-effective solution to provide high spatiotemporal resolution maps of ocean surface currents over wide coastal areas, suitable for many applications for coastal management. While the value of Near Real Time (NRT) data has been highlighted on many occasions for monitoring and predicting the surface drift of floating objects, long-term data series are key for the study of coastal ocean processes, their interplay, air sea interactions and connectivity between marine areas. To enhance the data use for these applications, the availability of reliable and standardized data sets of surface currents is crucial. Here we present the recent efforts made by the HFR community, in the framework of different projects and under the umbrella of the EuroGOOS HFR Task Team, to build the first historical European standardized HFR data set.The data quality control and processing methodology consists in five steps: (i) harvesting standardized data from the European HFR Node NRT catalogue and complementary non standardized (raw) data from HFR data providers; (ii) standardizing raw data through the European HFR Node software tools; (iii) applying Advanced Quality Control (AQC) by producing and analysing plots of time-series and maps of the current velocity, reporting number of valid data, basic QC flags and the spatiotemporal coverage 80/80 metric; (iv) disseminating the advanced QC outcomes through an open access repository and (v) reprocessing data in collaboration with each HFR data provider. Following this protocol, data from 11 European networks (comprising 32 radial stations, representing almost 29% of the total) and additional networks from 5 regional nodes from US, have been processed. Long-term HFR surface current data sets from Europe and US HFRs are already available in the Copernicus Marine Service portfolio as ‘radar_total’ dataset in the INSITU_GLO_UV_L2_REP_OBSERVATIONS_013_044 product. Future work will consolidate these efforts by optimizing the tools for AQC and data processing, expanding the available data series, and adding new systems., Le radar HF (HFR) est reconnu comme une solution rentable pour fournir des cartes à haute résolution spatio-temporelle des courants de surface océaniques sur de larges zones côtières, adaptées à de nombreuses applications pour la gestion des côtes. Alors que la valeur des données en temps quasi réel (NRT) a été soulignée à de nombreuses reprises pour la surveillance et la prévision de la dérive en surface des objets flottants, les séries de données à long terme sont essentielles pour l'étude des processus océaniques côtiers, de leur interaction, des interactions air-mer et de la connectivité entre les zones marines. Pour améliorer l'utilisation des données pour ces applications, il est crucial de disposer de séries de données fiables et normalisées sur les courants de surface. Nous présentons ici les efforts récents réalisés par la communauté HFR, dans le cadre de différents projets et sous l'égide de l'équipe de travail HFR d'EuroGOOS, pour construire le premier ensemble de données HFR normalisé européen historique.La méthodologie de traitement et de contrôle de la qualité des données consiste en cinq étapes : (i) récolte des données normalisées du catalogue NRT du nœud européen HFR et des données complémentaires non normalisées (brutes) des fournisseurs de données HFR ; (ii) normalisation des données brutes par le biais des outils logiciels du nœud européen HFR ; (iii) l'application d'un contrôle de qualité avancé (CQA) en produisant et en analysant des graphiques de séries temporelles et des cartes de la vitesse actuelle, en signalant le nombre de données valides, les indicateurs de CQ de base et la métrique de couverture spatio-temporelle 80/80 ; (iv) la diffusion des résultats du CQA avancé par le biais d'un dépôt en libre accès et (v) le retraitement des données en collaboration avec chaque fournisseur de données HFR. Suivant ce protocole, les données de 11 réseaux européens (comprenant 32 stations radiales, représentant près de 29% du total) et des réseaux supplémentaires de 5 nœuds régionaux des États-Unis, ont été traitées. Les ensembles de données de courant de surface HFR à long terme provenant des HFR européens et américains sont déjà disponibles dans le portefeuille Copernicus Marine Service en tant qu'ensemble de données "radar_total" dans le produit INSITU_GLO_UV_L2_REP_OBSERVATIONS_013_044. Les travaux futurs consolideront ces efforts en optimisant les outils de CQA et de traitement des données, en élargissant les séries de données disponibles et en ajoutant de nouveaux systèmes.

The skills of CODAR SeaSonde coastal high-frequency radars (HFR) located in the West Iberian Peninsula on measuring wave parameters are compared to in situ (buoy) and satellite altimeters (SA) wave observations. Significant wave heights (SWH), wave periods, and wave directions are compared over a time window of 36-months, from January 2017 to December 2019. The ability of HFR systems to capture extreme wave events is also assessed by comparing SWH measurements during the Emma storm, which hit the Iberian Peninsula in March 2018. The analysis presented in this study shows a slight overestimation of the SWH by the HFR systems. Comparisons with in situ observations revealed correlation coefficients (R) of the order of 0.69–0.87, biases below 0.60 m, root-mean-squared errors (RMSE) between 0.89 m to 1.18 m, and a slope regression between 1.01 and 1.26. Using buoy observations as reference ground truth, the comparisons with SA revealed Rs higher than 0.94, biases under 0.19 m, and RMSEs between 0.17 m and 0.42 m. Since in situ observations do not overlap all the HFR range cells (RC), and its correlation coefficients with SA have shown good agreement (R > 0.94), Sentinel-3 SA (SRAL) SWH measurements are further used for the validation of the HFR systems SWH observations. The comparison between the HFR and the SA collocated SWH observations allowed the evaluation of the ability of the radars to retrieve wave data as a function of the distance to the coast, particularly during extreme wave events. The comparison of the lower frequency (4.86 MHz) HFR coastal radars with the SA measurements showed an R of 0.94–0.99, a negative but reduced bias (−0.37), and an RMSE of 0.53 m. The higher frequency HFR systems (12–13.5 MHz) showed R between 0.53 and 0.82, and a clear overestimation of the SWH by the HFR sites.

The hydrodynamic circulation in estuaries is primarily driven by tides, river inflows and surface winds. While tidal and river data can be quite easily obtained for input to hydrodynamic models, sourcing accurate surface wind data is problematic. Inaccurate wind data can lead to inaccuracies in the surface currents computed by three-dimensional hydrodynamic models. In this research, a high-resolution wind model was coupled with a three-dimensional hydrodynamic model of Galway Bay, a semi-enclosed estuary on the west coast of Ireland, to investigate the effect of wind forcing on model accuracy. Two wind-forcing conditions were investigated: (1) using wind data measured onshore on the NUI Galway campus (NUIG) and (2) using offshore wind data provided by a high resolution wind model (HR). A scenario with no wind forcing (NW) was also assessed. The onshore wind data varied with time but the speed and direction were applied across the full model domain. The modeled offshore wind fields varied with both time and space. The effect of wind forcing on modeled hydrodynamics was assessed via comparison of modeled surface currents with surface current measurements obtained from a High-Frequency (HF) radar Coastal Ocean Dynamics Applications Radar (CODAR) observation system. Results indicated that winds were most significant in simulating the north-south surface velocity component. The model using high resolution temporally- and spatially-varying wind data achieved better agreement with the CODAR surface currents than the model using the onshore wind measurements and the model without any wind forcing.

In this paper, the Self-Organizing Map (SOM) method was applied to the surface currents data obtained between February and November 2008 by a network of high-frequency (HF) radars in the northern Adriatic. The sensitivity of the derived SOM solutions was tested in respect to the change of coordinate system of the data introduced to the SOM. In one experiment the original radial data measurements were used, and in the other experiment the Cartesian (total) current vectors derived from original radar data were analyzed. Although the computation of SOM solutions was not a demanding task, comparing both neural lattices yielded the nondeterministic polynomial time (NP) problem for which is difficult to propose a solution that will be globally optimal. Thus, we suggested utilizing the greedy algorithm with underlying assumption of 1-to-1 mapping between lattices. The results suggested that such solution could be local, but not global optimum and that the latter assumption could lower the obtained correlations between the patterns. However, without the assumption of 1-to-1 mapping between lattices, correlation between the derived SOM patterns was quite high, indicating that SOM mapping introduced to the radial current vectors and subsequent transformation into Cartesian coordinate system does not significantly affect obtained patterns in comparison to the SOM mapping done on the derived Cartesian current vectors. The documented similarity corroborates the use of total current vectors in various oceanographic studies, as being representative derivative of original radial measurements. We analyzed high-frequency radar data in the northern Adriatic.Self-Organizing Map method has been applied to radial and Cartesian current vectors.Transformation to Cartesian vectors do not significantly affect the SOM patterns.