HF radar for wind waves measurements in the Malta-Sicily Channel

Orasi, Arianna; Picone, Marco; Drago, Aldo; Capodici, Fulvio; Gauci, Adam; Nardone, Gabriele; Inghilesi, R.; Azzopardi, Joel; Galea, Anthony; Ciraolo, Giuseppe; Musulin, Jorge Sánchez; Alonso-Martirena, Andrés

doi:10.1016/j.measurement.2018.06.060

Cited by 21 publications

(17 citation statements)

References 34 publications

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“…HFrs are currently recognized as powerful monitoring systems due to their ability to provide simultaneous measurements of currents, waves and wind directions [63,64]. The skill of these systems to resolve the surface current and wave field is well proven [6,7,[26][27][28][29][30][31][32][33][34], and this has been pivotal in the implementation of observation systems [2] and in operational applications [1,65]. However, the use of backscattered echoes to determine the direction of wind blowing over the sea surface is relatively underexplored for SeaSonde systems [20,21].…”

Section: Discussionmentioning

confidence: 99%

“…The first-order echoes are typically used to retrieve surface currents, while second order ones are analyzed to reconstruct the wave field [4]. While the analysis of surface currents is well established and the retrieval of wave parameters is an emerging topic [6][7][8][9], the use of first-order peaks to measure wind direction is still poorly explored in the SeaSonde system, while in the literature some investigation on acquisition and validation using phased-array systems are available. The first numerical model to extract the wind direction from HFr backscatter was suggested by Long and Trizna [10].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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 (2th February–6th 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Although buoys are considered the most used and reliable instruments for in situ measurements of sea waves (Orasi et al, 2018), the high maintenance costs, together with the recurring damages and then lack of data, make the proposed microseismbased method a valid complementary tool for the monitoring of the sea state. Furthermore, once the regression model has been determined and if the seismic data are available, such a method could allow reconstructing the time series of sea wave height during periods prior to the buoy installation, with wide applications in many fields, first of all climate studies.…”

Section: Discussionmentioning

confidence: 99%

Insights Into Microseism Sources by Array and Machine Learning Techniques: Ionian and Tyrrhenian Sea Case of Study

et al. 2020

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In this work, we investigated the microseism recorded by a network of broadband seismic stations along the coastline of Eastern Sicily. Microseism is the most continuous and ubiquitous seismic signal on Earth and is mostly generated by the ocean-solid earth interaction. On the basis of spectral content, it is possible to distinguish three types of microseism: primary, secondary, and short-period secondary microseism (SPSM). We showed how most of the microseism energy recorded in Eastern Sicily is contained in the secondary and SPSM bands. This energy exhibits strong seasonal patterns, with maxima during the winters. By applying array techniques, we observed how the SPSM sources are located in areas of extended shallow water depth: the Catania Gulf and a part of the Northern Sicily coastlines. Finally, by using the significant wave height data recorded by two buoys installed in the Ionian and Tyrrhenian Seas, we developed an innovative method, selected among up-to-date machine learning techniques (MLTs), able to reconstruct the time series of sea wave parameters from microseism recorded in the three microseism period bands by distinct seismic stations. In particular, the developed model, based on random forest regression, allowed estimating the significant wave height with a low average error (∼0.14-0.18 m). The regression analysis suggests that the closer the seismic station to the sea, the more information concerning the sea state are contained in the recorded microseism. This is particularly important for the future development of an experimental monitoring system of the sea state conditions based on microseism recordings.

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“…Among the different instruments used to monitor the sea state, e.g., [4], wave buoys can be currently considered the most used and reliable instruments for in situ measurements of the offshore and coastal wave climate. However, they show serious drawbacks because of the high costs of both installation and maintenance and the permissions needed for their installation [5]. Other useful tools to monitor the sea state are remote sensing instruments such as radar altimeters and synthetic aperture radar, providing data from large marine areas.…”

Section: Introductionmentioning

confidence: 99%

“…Despite their good spatial coverage, they suffer from limited temporal resolution [6][7][8]. An important improvement in sea state monitoring is represented by high-frequency (HF) radar measurements, providing directional wave spectra at a high temporal resolution, with a spatial resolution that depends on both bandwidth and antenna [5,9,10].…”

Section: Introductionmentioning

confidence: 99%

Unravelling the Relationship Between Microseisms and Spatial Distribution of Sea Wave Height by Statistical and Machine Learning Approaches

et al. 2020

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Global warming is making extreme wave events more intense and frequent. Hence, the importance of monitoring the sea state for marine risk assessment and mitigation is increasing day-by-day. In this work, we exploit the ubiquitous seismic noise generated by energy transfer from the ocean to the solid earth (called microseisms) to infer the sea wave height data provided by hindcast maps. To this aim, we use a combined approach based on statistical analysis and machine learning. In particular, a random forest model shows very promising results in the spatial and temporal reconstruction of sea wave height by microseisms. The observed dependence of input importance from the distance sea grid cell-seismic station suggests how the reliable monitoring of the sea state in a wide area by microseisms needs data recorded by dense networks, comprising stations evenly distributed along the coastlines.

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HF radar for wind waves measurements in the Malta-Sicily Channel

Cited by 21 publications

References 34 publications

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

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

Insights Into Microseism Sources by Array and Machine Learning Techniques: Ionian and Tyrrhenian Sea Case of Study

Unravelling the Relationship Between Microseisms and Spatial Distribution of Sea Wave Height by Statistical and Machine Learning Approaches

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