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Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 28869

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Special Issue Editor

Dr. Silvia Piedracoba

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CETMAR (Centro Tecnológico del Mar), 36208 Pontevedra, Spain
Interests: physical oceanography; coastal oceanography; coastal radars; physical–biological coupling; ocean observing systems; forecast systems; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

High-frequency radar (HFR) is a unique technology that provides invaluable information on surface currents, wave fields, and surface winds over wide areas with high spatial and temporal resolution.

These land-based remote sensing instruments have demonstrated operational value that is also well recognized through inclusion in operational protocols worldwide.

Most of the HF radars installed over the last two decades have been predominantly dedicated to research and development. During this period, the technology has become widely validated, and today, HF radar technology has achieved a mature level of readiness and is recognized as a key tool in the research and management of the coastal environment. In fact, everything is ready for a transformation of data into information that can be used not only for scientific research but also for answering societal needs.

Combining the high spatial and temporal resolution of the HF radar velocities with other in situ or remote sensing measurements and models will significantly contribute to enhance our understanding of the coastal dynamics, and therefore, this technology can support economic development and minimize environmental impacts in coastal areas. For instance, HF radar can be used in a wide range of applications including oil spills, search and rescue, monitoring of extreme wave events, meteorological support, marine environmental management, tsunami warning systems, coastal current and wave monitoring, routine navigation aid, commercial fishing, HAB (harmful algae bloom) monitoring, assessment of marine energy resources, and climate change, among others.

In this Special Issue, we would like to focus on societal applications derived from this technology. Particularly, this Special Issue is a call to publish papers showing emerging HF radar derivative products in an evolutionary way, focused not only on intermediate users but also end users that do not require very sophisticated training. They could include all the necessary modifications to exploit emerged products aimed at the downstream part of the value chain, providing actionable information to non-specialist sectors.

Dr. Silvia Piedracoba
Guest Editor

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Keywords

HF radar
Multiplatform observations
Circulation models
Products and applications
Open sea and coastal areas monitoring
Physical–biological interactions

Related Special Issue

Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications II in Remote Sensing (2 articles)

Published Papers (14 papers)

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Research

20 pages, 4770 KiB

Open AccessArticle

HF Radar Wind Direction: Multiannual Analysis Using Model and HF Network

by Simona Saviano, Anastasia Angela Biancardi, Florian Kokoszka, Marco Uttieri, Enrico Zambianchi, Luis Alberto Cusati, Andrea Pedroncini and Daniela Cianelli

Remote Sens. 2023, 15(12), 2991; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15122991 - 08 Jun 2023

Viewed by 833

Abstract

HF radar systems have the potential to measure the wind direction, in addition to surface currents and wave fields. However, studies on HF radar for wind direction determination are rare in the scientific literature. Starting with the results presented in Saviano et al. (2021), we here expand on the reliability of the multiannual wind direction data retrieved over two periods, from May 2008 to December 2010 and from January to December 2012, by a network of three SeaSonde high-frequency (HF) radars operating in the Gulf of Naples (Central Tyrrhenian Sea, Western Mediterranean Sea). This study focuses on the measurements obtained by each antenna over three range cells along a coast–offshore transect, pointing to any potential geographically dependent measurement. The scarcity of offshore wind measurements requires the use of model-generated data for comparative purposes. The data here used are obtained from the Mediterranean Wind–Wave Model, which provides indications for both wave and wind parameters, and the ERA5@2km wind dataset obtained by dynamically downscaling ERA5 reanalysis. These data are first compared with in situ data and subsequently with HF-retrieved wind direction measurements. The analysis of the overall performance of the HF radar network in the Gulf of Naples confirms that the HF radar wind data show the best agreement when the wind speed exceeds a 5 m/s threshold, ensuring a sufficiently energetic surface wave field to be measured. The results obtained in the study suggest the necessity of wind measurements in offshore areas to validate the HF radar wind measurements and to improve the extraction algorithms. The present work opens up further investigations on the applications of wind data from SeaSonde HF radars as potential monitoring platforms, both in coastal and offshore areas. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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17 pages, 7610 KiB

Open AccessArticle

Space-Time Cascaded Processing-Based Adaptive Transient Interference Mitigation for Compact HFSWR

by Di Yao, Qiushi Chen and Qiyan Tian

Remote Sens. 2023, 15(3), 651; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030651 - 21 Jan 2023

Cited by 2 | Viewed by 1231

Abstract

In high-frequency (HF) radar systems, transient interference is a common phenomenon that dramatically degrades the performance of target detection and remote sensing. Up until now, various suppression algorithms of transient interference have been proposed. They mainly concentrate on the skywave over-the-horizon radar on the basis of the assumption that the interference is sparse in a coherent processing interval (CPI). However, HF surface wave radar (HFSWR) often faces more complex transient interference due to various extreme types of weather, such as thunderstorm and typhoon, etc. The above algorithms usually suffer dramatic performance loss when transient interference contaminates the enormously continuous parts of a CPI. Especially for the compact HFSWR, which suffers from severe beam broadening and fewer array degrees of freedom. In order to solve the above problem, this study developed a two-dimensional interference suppression algorithm based on space-time cascaded processing. First, according to the spatial correlation of the compact array, the statistical samples of the main-lobe transient interference are estimated using a rotating spatial beam method. Next, an adaptive selection strategy is developed to obtain the optimal secondary samples based on information geometry distance. Finally, based on a quadratic constraint approximation, a precise estimation method of the optimal weight is developed when the interference covariance matrix is singular. The experimental results of simulation and measured data demonstrate that the proposed approach provides far superior suppression performance. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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63 pages, 40347 KiB

Open AccessArticle

Societal Applications of HF Skywave Radar

by Stuart Anderson

Remote Sens. 2022, 14(24), 6287; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246287 - 12 Dec 2022

Viewed by 1768

Abstract

After exploratory research in the 1950s, HF skywave ‘over-the-horizon’ radars (OTHR) were developed as operating systems in the 1960s for defence missions, notably the long-range detection of ballistic missiles, aircraft, and ships. The potential for a variety of non-defence applications soon became apparent, but the size, cost, siting requirements, and tasking priority hindered the implementation of these societal roles. A sister technology—HF surface wave radar (HFSWR)—evolved during the same period but, in this more compact form, the non-defence applications dominated, with hundreds of such radars presently deployed around the world, used primarily for ocean current mapping and wave measurements. In this paper, we examine the ocean monitoring capabilities of the latest generation of HF skywave radars, some shared with HFSWR, some unique to the skywave modality, and explore some new possibilities, along with selected technical details for their implementation. We apply state-of-the-art modelling and experimental data to illustrate the kinds of information that can be generated and exploited for civil, commercial, and scientific purposes. The examples treated confirm the relevance and value of this information to such diverse activities as shipping, fishing, offshore resource extraction, agriculture, communications, weather forecasting, and climate change studies. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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18 pages, 9824 KiB

Open AccessArticle

Deep Learning Aided Time–Frequency Analysis Filter Framework for Suppressing Ionosphere Clutter

by Xiaowei Ji, Jiaming Li, Qiang Yang, Linwei Wang, Ying Suo and Xiaochuan Wu

Remote Sens. 2022, 14(14), 3424; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143424 - 16 Jul 2022

Cited by 3 | Viewed by 1496

Abstract

In a heterogeneous environment, the ionosphere is dynamically changing in the Earth’s middle latitude, and backscatter from fast-moving irregularities in the plasma can cause ionosphere clutter to extend. Suppressing varying ionosphere clutter and exploring obscured targets are challenging tasks for high frequency surface wave radar (HFSWR). For responding to these challenges, this research presents a multi-channel deep learning time–frequency feature filter framework (DL-TFF). Firstly, we observed the behavior of the ionosphere clutter for a long period of time before selecting the representative ionosphere clutter. Secondly, different transform techniques are applied to provide a time–frequency representation of the non-stationary echo signals, and representation results of different echo components are collected as a training set for feature learning. Thirdly, we design a multi-channel time–frequency feature learning network (MTF), which is responsible for mining discriminative time–frequency information between targets and different types of ionosphere clutter. Experimental results on real HFSWR data sets have demonstrated that DL-TFF can remove varying ionosphere clutter and simultaneously reveal covered targets. Moreover, its suppression effectiveness is more ideal than the previous classical method. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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16 pages, 3684 KiB

Open AccessArticle

Space-Time Adaptive Processing Clutter-Suppression Algorithm Based on Beam Reshaping for High-Frequency Surface Wave Radar

by Jiaming Li, Qiang Yang, Xin Zhang, Xiaowei Ji and Dezhu Xiao

Remote Sens. 2022, 14(12), 2935; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122935 - 19 Jun 2022

Cited by 1 | Viewed by 1968

Abstract

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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15 pages, 2560 KiB

Open AccessArticle

Progress towards an HF Radar Wind Speed Measurement Method Using Machine Learning

by Lucy R. Wyatt

Remote Sens. 2022, 14(9), 2098; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092098 - 27 Apr 2022

Cited by 10 | Viewed by 1929

Abstract

HF radars are now an important part of operational coastal observing systems where they are used primarily for measuring surface currents. Their use for wave and wind direction measurement has also been demonstrated. These measurements are based on physical models of radar backscatter from the ocean surface described in terms of its ocean wave directional spectrum and the influence thereon of the surface current. Although this spectrum contains information about the local wind that is generating the wind sea part of the spectrum, it also includes spectral components propagating into the local area having been generated by winds away from the area i.e., swell. In addition, the relationship between the local wind sea and wind speed depends on fetch and duration. Thus, finding a physical model to extract wind speed from the radar signal is not straightforward. In this paper, methods that have been proposed to date will be briefly reviewed and an alternative approach is developed using machine learning methods. These have been applied to three different data sets using different radar systems in different locations. The results presented here are encouraging and proposals for further development are outlined. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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21 pages, 11161 KiB

Open AccessArticle

A Self-Regulating Multi-Clutter Suppression Framework for Small Aperture HFSWR Systems

by Xiaowei Ji, Qiang Yang and Linwei Wang

Remote Sens. 2022, 14(8), 1901; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081901 - 14 Apr 2022

Cited by 4 | Viewed by 1486

Abstract

The problem that this paper is concerned with is High Frequency Surface Wave Radar (HFSWR) detection of desired targets against a complex interference background consisting of sea clutter, ionosphere clutter, Radio Frequency Interference (RFI) and atmospheric noise. Eliminating unwanted echoes and exploring obscured targets contribute to achieving ideal surveillance of sea surface targets. In this paper, a Self-regulating Multi-clutter Suppression Framework (SMSF) has been proposed for small aperture HFSWR. SMSF can remove many types of clutter or RFI; meanwhile, it mines the targets merged into clutter and tracks the travelling path of the ship. In SMSF, a novel Dynamic Threshold Mapping Recognition (DTMR) method is first proposed to reduce the atmospheric noise and recognize each type of unwanted echo; these recognized echoes are fed into the proposed Adaptive Prophase-current Dictionary Learning (APDL) algorithm. To make a comprehensive evaluation, we also designed three novel assessment parameters: Obscured Targets Detection Rate (OTDR), Clutter Purification Rate (CPR) and Erroneous Suppression Rate (ESR). The experiment data collected from a small aperture HFSWR system confirm that SMSF has precise suppression performance over most of the classical algorithms and concurrently reveals the moving targets, and OTDR of SMSF is usually higher than compared methods. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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20 pages, 14682 KiB

Open AccessArticle

Surface Current Variations and Hydrological Characteristics of the Penghu Channel in the Southeastern Taiwan Strait

by Po-Chun Hsu

Remote Sens. 2022, 14(8), 1816; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081816 - 09 Apr 2022

Cited by 5 | Viewed by 2218

Abstract

Coastal ocean dynamics application radar (CODAR) SeaSonde high-frequency (HF) radars deployed along the coast of Taiwan were used to reveal ocean surface current variations both hourly and through climatological seasons in the Penghu Channel (PHC), southeastern Taiwan Strait (TS), from December 2014 to December 2020. The ocean current in the PHC has a semidiurnal tidal cycle, and the seasonal main flow, wind direction, and wind strength significantly affect the direction and speed of the flow passing through the PHC. The speed of the tidal current in the PHC area can reach more than 1 m/s, and the monthly average flow speed in the PHC is between 0.12 (winter) and 0.24 m/s (summer). Several buoys indicated that the southward flow along the western coast of Taiwan drifted through the PHC in fall and winter. The HF radar observations confirmed the same, implying that this occurred during the strong northeastern monsoon. For a weak northerly wind or even southerly wind, the flow in the PHC can be northward. Different wind directions can affect the speed of the flow passing through the PHC and the branch flow in the northern PHC. The HF radar results are highly consistent with the spatial characteristics of satellite data regarding the sea surface temperature, sea surface salinity, and chlorophyll concentrations; however, there are significant differences from the satellite-derived ocean current. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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21 pages, 41868 KiB

Open AccessArticle

Sea Storm Analysis: Evaluation of Multiannual Wave Parameters Retrieved from HF Radar and Wave Model

by Simona Saviano, Anastasia Angela Biancardi, Marco Uttieri, Enrico Zambianchi, Luis Alberto Cusati, Andrea Pedroncini, Giorgio Contento and Daniela Cianelli

Remote Sens. 2022, 14(7), 1696; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071696 - 31 Mar 2022

Cited by 6 | Viewed by 2013

Abstract

Intense atmospheric disturbances, which impact directly on the sea surface causing a significant increase in wave height and sometimes strong storm surges, have become increasingly frequent in recent years in the Mediterranean Sea, producing extreme concern in highly populated coastal areas, such as the Gulf of Naples (Western Mediterranean Sea, Central Tyrrhenian Sea). In this work, fifty-six months of wave parameters retrieved by an HF radar network are integrated with numerical outputs to analyze the seasonality of extreme events in the study area and to investigate the performance of HF radars while increasing their distances from the coast. The model employed is the MWM (Mediterranean Wind-Wave Model), providing a wind-wave dataset based on numerical models (the hindcast approach) and implemented in the study area with a 0.03° spatial resolution. The integration and comparison with the MWM dataset, carried out using wave parameters and spectral information, allowed us to analyze the availability and accuracy of HF sampling during the investigated period. The statistical comparisons highlight agreement between the model and the HF radars during episodes of sea storms. The results confirm the potential of HF radar systems as long-term monitoring observation platforms, and allow us to give further indications on the seasonality of sea storms under different meteorological conditions and on their energy content in semi-enclosed coastal areas, such as the Gulf of Naples. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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18 pages, 628 KiB

Open AccessArticle

A Multi-Stage Vessel Tracklet Association Method for Compact High-Frequency Surface Wave Radar

by Weifeng Sun, Zhenzhen Pang, Weimin Huang, Peng Ma, Yonggang Ji, Yongshou Dai and Xiaotong Li

Remote Sens. 2022, 14(7), 1601; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071601 - 26 Mar 2022

Cited by 3 | Viewed by 1748

Abstract

A compact high-frequency surface wave radar, used for target detection, suffers from a low signal-to-noise ratio, low detection probability, a high false alarm rate, and low positioning accuracy; this is due to its low transmit power and the reduced aperture size of the receiving antenna array. When target tracking algorithms are applied to compact high-frequency surface wave radar data, track fragmentation often occurs and a long track may be broken into several track segments (a.k.a. tracklets), which degrade the tracking continuity for a maritime surveillance system. We present a multi-stage vessel tracklet association method, based on bidirectional prediction and optimal assignment, to associate the broken tracklets belonging to the same target, and connect them to form one continuous track in a multi-target tracking scenario. Firstly, two global motion parameters, i.e., the average heading and average speed, were, respectively, extracted from the newly initiated and terminated tracklets as features for a rough tracklet association, then k-means clustering was used to produce the preliminary tracklet pairs. Subsequently, the temporal and spatial constraints on the initiated and terminated tracklets were considered to refine the preliminary tracklet pairs, to obtain the candidate tracklet pairs. Finally, the tracklet association costs were calculated using Doppler velocity, range, and azimuth to determine the similarity between tracklets in the candidate tracklet pairs, and an association cost matrix was obtained. Then an optimal assignment method based on Jonker–Volgenant–Castanon algorithm was applied to the association matrix to achieve optimal tracklet matching by minimizing the total association costs. Tracklet association experiments with both simulated and field data were conducted; experimental results show that, compared with existing track segment association methods, the association accuracy of the proposed method is significantly improved with better tracking continuity. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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26 pages, 5515 KiB

Open AccessArticle

Improving Ship Detection in Clutter-Edge and Multi-Target Scenarios for High-Frequency Radar

by Zhiqing Yang, Hao Zhou, Yingwei Tian, Weimin Huang and Wei Shen

Remote Sens. 2021, 13(21), 4305; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214305 - 26 Oct 2021

Cited by 6 | Viewed by 2435

Abstract

As one of the main sensors for continuous maritime measurements of sea state parameters, high-frequency surface wave radar (HFSWR) also plays an important role in ship detection and tracking. Compact HFSWR often suffers from missing targets, especially when the target appears near the Doppler region with heavy sea clutter or near another target in a multi-target scenario. To address this problem, an automatic ship detection method based on time–frequency (TF) analysis is presented in this paper. The TF target ridge areas are extracted in the TF image via the eigenvalues of the Hessian matrix, image edge detection, and local maximum search. Then, whether ship signals exist in the TF ridges or not is decided by a decision threshold that is calculated by fitting the probability distribution function (PDF) of sea clutter in the TF domain. The proposed TF method can separate TF ridges of similar Doppler frequency and performs constant false alarm rate (CFAR) detection for TF targets, which facilitates detecting these targets that are masked by sea clutter and other large targets. Experimental results show that the number of detected ships that match with the automatic identification system (AIS) records is four times more than that obtained by the conventional constant false alarm rate (CFAR) detectors and 1.3 times more than that by the state-of-the-art TF method in consideration of approximately the same number of detected targets. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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20 pages, 5780 KiB

Open AccessFeature PaperArticle

Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements

by Max C. Bradbury and Daniel C. Conley

Remote Sens. 2021, 13(19), 3896; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13193896 - 29 Sep 2021

Cited by 5 | Viewed by 2138

Abstract

An extensive record of current velocities at all levels in the water column is an indispensable requirement for a tidal resource assessment and is fully necessary for accurate determination of available energy throughout the water column as well as estimating likely energy capture for any particular device. Traditional tidal prediction using the least squares method requires a large number of harmonic parameters calculated from lengthy acoustic Doppler current profiler (ADCP) measurements, while long-term in situ ADCPs have the advantage of measuring the real current but are logistically expensive. This study aims to show how these issues can be overcome with the use of a neural network to predict current velocities throughout the water column, using surface currents measured by a high-frequency radar. Various structured neural networks were trained with the aim of finding the network which could best simulate unseen subsurface current velocities, compared to ADCP data. This study shows that a recurrent neural network, trained by the Bayesian regularisation algorithm, produces current velocities highly correlated with measured values: r² (0.98), mean absolute error (0.05 ms⁻¹), and the Nash–Sutcliffe efficiency (0.98). The method demonstrates its high prediction ability using only 2 weeks of training data to predict subsurface currents up to 6 months in the future, whilst a constant surface current input is available. The resulting current predictions can be used to calculate flow power, with only a 0.4% mean error. The method is shown to be as accurate as harmonic analysis whilst requiring comparatively few input data and outperforms harmonics by identifying non-celestial influences; however, the model remains site specific. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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23 pages, 18696 KiB

Open AccessArticle

HF Radars for Wave Energy Resource Assessment Offshore NW Spain

by Ana Basañez and Vicente Pérez-Muñuzuri

Remote Sens. 2021, 13(11), 2070; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112070 - 24 May 2021

Cited by 3 | Viewed by 2348

Abstract

Wave energy resource assessment is crucial for the development of the marine renewable industry. High-frequency radars (HF radars) have been demonstrated to be a useful wave measuring tool. Therefore, in this work, we evaluated the accuracy of two CODAR Seasonde HF radars for describing the wave energy resource of two offshore areas in the west Galician coast, Spain (Vilán and Silleiro capes). The resulting wave characterization was used to estimate the electricity production of two wave energy converters. Results were validated against wave data from two buoys and two numerical models (SIMAR, (Marine Simulation) and WaveWatch III). The statistical validation revealed that the radar of Silleiro cape significantly overestimates the wave power, mainly due to a large overestimation of the wave energy period. The effect of the radars’ data loss during low wave energy periods on the mean wave energy is partially compensated with the overestimation of wave height and energy period. The theoretical electrical energy production of the wave energy converters was also affected by these differences. Energy period estimation was found to be highly conditioned to the unimodal interpretation of the wave spectrum, and it is expected that new releases of the radar software will be able to characterize different sea states independently. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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16 pages, 5045 KiB

Open AccessArticle

Wind Direction Data from a Coastal HF Radar System in the Gulf of Naples (Central Mediterranean Sea)

by Simona Saviano, Giovanni Esposito, Roberta Di Lemma, Paola de Ruggiero, Enrico Zambianchi, Stefano Pierini, Pierpaolo Falco, Berardino Buonocore, Daniela Cianelli and Marco Uttieri

Remote Sens. 2021, 13(7), 1333; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071333 - 31 Mar 2021

Cited by 9 | Viewed by 2418

Abstract

Results on the accuracy of SeaSonde High Frequency (HF) radar wind direction measurements in the Gulf of Naples (Southern Tyrrhenian Sea, Central Mediterranean Sea) are here presented. The investigation was carried out for a winter period (2 February–6 March) and for one summer month (August) of the reference year 2009. HF radar measurements were compared with in situ recordings from a weather station and with model data, with the aim of resolving both small scale and large scale dynamics. The analysis of the overall performance of the HF radar system in the Gulf of Naples shows that the data are reliable when the wind speed exceeds a 5 m/s threshold. Despite such a limitation, this study confirms the potentialities of these systems as monitoring platforms in coastal areas and suggests further efforts towards their improvement. Full article

(This article belongs to the Special Issue Sustained Ocean Surface Observation Using HF Radar: From Data to Societal Applications)

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