Articles | Volume 14, issue 8
https://doi.org/10.5194/essd-14-3615-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-3615-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Aug 2022
Data description paper |
| 11 Aug 2022
| 11 Aug 2022
Wind waves in the North Atlantic from ship navigational radar: SeaVision development and its validation with the Spotter wave buoy and WaveWatch III
Natalia Tilinina
CORRESPONDING AUTHOR
Université Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des
Géosciences de l'Environnement, 70 rue de la Physique, 38400, Grenoble,
France
Dmitry Ivonin
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
Alexander Gavrikov
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
Vitali Sharmar
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
Sergey Gulev
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
A. M. Obukhov Institute of Atmospheric Physics, RAS, Pyzhevskiy Lane 3, 109017, Moscow, Russia
Alexander Suslov
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
Vladimir Fadeev
Joint stock company “Marine Complexes and Systems”, Aleksandrovskoy
Fermy ave. 2 office 2H, 192174, Saint Petersburg, Russia
Boris Trofimov
Joint stock company “Marine Complexes and Systems”, Aleksandrovskoy
Fermy ave. 2 office 2H, 192174, Saint Petersburg, Russia
Sergey Bargman
Joint stock company “Marine Complexes and Systems”, Aleksandrovskoy
Fermy ave. 2 office 2H, 192174, Saint Petersburg, Russia
Leysan Salavatova
Moscow Institute of Physics and Technology, Institutskiy Pereulok
9, 141701, Dolgoprudny, Moscow Region, Russia
Vasilisa Koshkina
Moscow Institute of Physics and Technology, Institutskiy Pereulok
9, 141701, Dolgoprudny, Moscow Region, Russia
Polina Shishkova
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
Elizaveta Ezhova
Moscow Institute of Physics and Technology, Institutskiy Pereulok
9, 141701, Dolgoprudny, Moscow Region, Russia
Mikhail Krinitsky
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
Olga Razorenova
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
Klaus Peter Koltermann
Faculty of Geography, Lomonosov Moscow State University, Moscow, 119991, Russia
Vladimir Tereschenkov
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
Alexey Sokov
Shirshov Institute of Oceanology, RAS, Nakhimovsky ave. 36, 117997,
Moscow, Russia
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Cited
12 citations as recorded by crossref.
- Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images V. Rezvov et al. 10.3103/S0027134923070275
- Determination of the Significant Wave Height from Doppler Radar Images of the Sea Surface A. Ermoshkin et al. 10.1134/S000143702470084X
- Dispersion relation for wind waves with account for the drift current Y. Plaksina et al. 10.31857/S0002351524030024
- Dispersion Relation for Wind Waves with Account for the Drift Current Y. Plaksina et al. 10.1134/S0001433824700300
- THE VALIDITY DOMAIN OF SATELLITE ALTIMETRY DATA FOR VALIDATION OF ALGORITHMS FOR ESTIMATION WIND WAVE HEIGHT E. Ezhova et al. 10.29006/1564-2291.JOR-2024.52(4).3
- Estimating Significant Wave Height from X-Band Navigation Radar Using Convolutional Neural Networks M. Krinitskiy et al. 10.3103/S0027134923070159
- Verifying Measurements of Surface Current Velocities by X-Band Coherent Radar Using Drifter Data I. Gorbunov et al. 10.32603/1993-8985-2023-26-3-99-110
- MEAN WIND WAVE PERIOD ESTIMATION FROM INDIVIDUAL SHIP NAVIGATION RADAR IMAGES USING DEEP LEARNING METHODS V. Golikov et al. 10.29006/1564-2291.JOR-2024.52(4).2
- Improving data-driven estimation of significant wave height through preliminary training on synthetic X-band radar sea clutter imagery V. Rezvov et al. 10.3389/fmars.2024.1363135
- Measurement of Sea Surface Characteristics from Radar Images Using Gradient Methods K. Laptev et al. 10.32603/1993-8985-2024-27-5-41-53
- MEAN WIND WAVE PERIOD ESTIMATION FROM INDIVIDUAL SHIP NAVIGATION RADAR IMAGES USING DEEP LEARNING METHODS V. Golikov et al. 10.29006/1564-2291.JOR-2024.52(4).2
- Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images V. Rezvov et al. 10.3103/S0027134923070275
10 citations as recorded by crossref.
- Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images V. Rezvov et al. 10.3103/S0027134923070275
- Determination of the Significant Wave Height from Doppler Radar Images of the Sea Surface A. Ermoshkin et al. 10.1134/S000143702470084X
- Dispersion relation for wind waves with account for the drift current Y. Plaksina et al. 10.31857/S0002351524030024
- Dispersion Relation for Wind Waves with Account for the Drift Current Y. Plaksina et al. 10.1134/S0001433824700300
- THE VALIDITY DOMAIN OF SATELLITE ALTIMETRY DATA FOR VALIDATION OF ALGORITHMS FOR ESTIMATION WIND WAVE HEIGHT E. Ezhova et al. 10.29006/1564-2291.JOR-2024.52(4).3
- Estimating Significant Wave Height from X-Band Navigation Radar Using Convolutional Neural Networks M. Krinitskiy et al. 10.3103/S0027134923070159
- Verifying Measurements of Surface Current Velocities by X-Band Coherent Radar Using Drifter Data I. Gorbunov et al. 10.32603/1993-8985-2023-26-3-99-110
- MEAN WIND WAVE PERIOD ESTIMATION FROM INDIVIDUAL SHIP NAVIGATION RADAR IMAGES USING DEEP LEARNING METHODS V. Golikov et al. 10.29006/1564-2291.JOR-2024.52(4).2
- Improving data-driven estimation of significant wave height through preliminary training on synthetic X-band radar sea clutter imagery V. Rezvov et al. 10.3389/fmars.2024.1363135
- Measurement of Sea Surface Characteristics from Radar Images Using Gradient Methods K. Laptev et al. 10.32603/1993-8985-2024-27-5-41-53
2 citations as recorded by crossref.
- MEAN WIND WAVE PERIOD ESTIMATION FROM INDIVIDUAL SHIP NAVIGATION RADAR IMAGES USING DEEP LEARNING METHODS V. Golikov et al. 10.29006/1564-2291.JOR-2024.52(4).2
- Improvement of the AI-Based Estimation of Significant Wave Height Based on Preliminary Training on Synthetic X-Band Radar Sea Clutter Images V. Rezvov et al. 10.3103/S0027134923070275
Latest update: 23 Apr 2025
Short summary
We present wind wave parameter data from research cruises in the North Atlantic in 2020 and 2021 and the SeaVision system for measuring wind wave characteristics with a standard marine navigation X-band radar. We promote the potential of ship navigation X-band radars (when assembled with SeaVision or similar systems) for the development of a new near-global observational network, providing a much larger number of wind wave observations.
We present wind wave parameter data from research cruises in the North Atlantic in 2020 and 2021...
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