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.
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
Related authors
No articles found.
Pedro Colombo, Bernard Barnier, Thierry Penduff, Jérôme Chanut, Julie Deshayes, Jean-Marc Molines, Julien Le Sommer, Polina Verezemskaya, Sergey Gulev, and Anne-Marie Treguier
Geosci. Model Dev., 13, 3347–3371, https://doi.org/10.5194/gmd-13-3347-2020, https://doi.org/10.5194/gmd-13-3347-2020, 2020
Short summary
Short summary
In the ocean circulation model NEMO, the representation of the overflow of dense Arctic waters through the Denmark Strait is investigated. In this
z-coordinate context, sensitivity tests show that the mixing parameterizations preferably act along the model grid slope. Thus, the representation of the overflow is more sensitive to resolution than to parameterization and is best when the numerical grid matches the local topographic slope.
Nikolay Alexeevsky, Dmitry V. Magritsky, Klaus Peter Koltermann, Inna Krylenko, and Pavel Toropov
Nat. Hazards Earth Syst. Sci., 16, 1289–1308, https://doi.org/10.5194/nhess-16-1289-2016, https://doi.org/10.5194/nhess-16-1289-2016, 2016
Short summary
Short summary
Inundations on the Black Sea coast of the Krasnodar territory of the Russian Federation were analysed for 1945 to 2013. Risks, hazards and damage from inundations here are some of the highest in the country. The large quantity and the extremeness of rainfall, and the intense flood regimes of the rivers are the main contributors. Additionally, anthropogenic impact such as badly planned economic activities in channels, floodplains and on river watersheds strongly enhance the effects.
V. S. Arkhipkin, F. N. Gippius, K. P. Koltermann, and G. V. Surkova
Nat. Hazards Earth Syst. Sci., 14, 2883–2897, https://doi.org/10.5194/nhess-14-2883-2014, https://doi.org/10.5194/nhess-14-2883-2014, 2014
Related subject area
Domain: ESSD – Ocean | Subject: Physical oceanography
The DTU21 global mean sea surface and first evaluation
A dataset for investigating socio-ecological changes in Arctic fjords
Dataset of depth and temperature profiles obtained from 2012 to 2020 using commercial fishing vessels of the AdriFOOS fleet in the Adriatic Sea
Measurements and modeling of water levels, currents, density, and wave climate on a semi-enclosed tidal bay, Cádiz (southwest Spain)
Wind wave and water level dataset for Hornsund, Svalbard (2013–2021)
Hyperspectral reflectance of pristine, ocean weathered and biofouled plastics from dry to wet and submerged state
Deep-water hydrodynamic observations around a cold-water coral habitat in a submarine canyon in the eastern Ligurian Sea (Mediterranean Sea)
Ocean cross-validated observations from R/Vs L'Atalante, Maria S. Merian, and Meteor and related platforms as part of the EUREC4A-OA/ATOMIC campaign
A global Lagrangian eddy dataset based on satellite altimetry
The sea level time series of Trieste, Molo Sartorio, Italy (1869–2021)
Measurements of Nearshore Waves through Coherent Arrays of Free-Drifting Wave Buoys
Southern Europe and western Asian marine heatwaves (SEWA-MHWs): a dataset based on macroevents
An evaluation of long-term physical and hydrochemical measurements at the Sylt Roads Marine Observatory (1973–2019), Wadden Sea, North Sea
Annual hydrographic variability in Antarctic coastal waters infused with glacial inflow
Argo salinity: bias and uncertainty evaluation
Improved global sea surface height and current maps from remote sensing and in situ observations
Extension of high temporal resolution sea level time series at Socoa (Saint Jean-de-Luz, France) back to 1875
Sea surface height anomaly and geostrophic current velocity from altimetry measurements over the Arctic Ocean (2011–2020)
SDUST2020 MSS: a global 1′ × 1′ mean sea surface model determined from multi-satellite altimetry data
Synoptic observations of sediment transport and exchange mechanisms in the turbid Ems Estuary: the EDoM campaign
A compilation of global bio-optical in situ data for ocean colour satellite applications – version three
Deep-water hydrodynamic observations of two moorings sites on the continental slope of the southern Adriatic Sea (Mediterranean Sea)
Hydrodynamic and hydrological processes within a variety of coral reef lagoons: field observations during six cyclonic seasons in New Caledonia
Reconstructing ocean subsurface salinity at high resolution using a machine learning approach
A Mediterranean drifters dataset: 1998–2022
The HYPERMAQ dataset: bio-optical properties of moderately to extremely turbid waters
Mesoscale observations of temperature and salinity in the Arctic Transpolar Drift: a high-resolution dataset from the MOSAiC Distributed Network
SDUST2021GRA: global marine gravity anomaly model recovered from Ka-band and Ku-band satellite altimeter data
Reanalyses of Maskelyne's tidal data at St. Helena in 1761
Twenty-one years of hydrological data acquisition in the Mediterranean Sea: quality, availability, and research
A new operational Mediterranean diurnal optimally interpolated sea surface temperature product within the Copernicus Marine Service
Ole Baltazar Andersen, Stine Kildegaard Rose, Adili Abulaitijiang, Shengjun Zhang, and Sara Fleury
Earth Syst. Sci. Data, 15, 4065–4075, https://doi.org/10.5194/essd-15-4065-2023, https://doi.org/10.5194/essd-15-4065-2023, 2023
Short summary
Short summary
The mean sea surface (MSS) is an important reference for mapping sea-level changes across the global oceans. It is widely used by space agencies in the definition of sea-level anomalies as mapped by satellite altimetry from space. Here a new fully global high-resolution mean sea surface called DTU21MSS is presented, and a suite of evaluations are performed to demonstrate its performance.
Robert W. Schlegel and Jean-Pierre Gattuso
Earth Syst. Sci. Data, 15, 3733–3746, https://doi.org/10.5194/essd-15-3733-2023, https://doi.org/10.5194/essd-15-3733-2023, 2023
Short summary
Short summary
A single dataset was created for investigations of changes in the socio-ecological systems within seven Arctic fjords by amalgamating roughly 1400 datasets from a number of sources. The many variables in these data were organised into five distinct categories and classified into 14 key drivers. Data for seawater temperature and salinity are available from the late 19th century, with some other drivers having data available from the 1950s and 1960s and the others starting from the 1990s onward.
Pierluigi Penna, Filippo Domenichetti, Andrea Belardinelli, and Michela Martinelli
Earth Syst. Sci. Data, 15, 3513–3527, https://doi.org/10.5194/essd-15-3513-2023, https://doi.org/10.5194/essd-15-3513-2023, 2023
Short summary
Short summary
This work presents the pressure (depth) and temperature profile dataset provided by the AdriFOOS infrastructure in the Adriatic Sea (Mediterranean basin) from 2012 to 2020. Data were subject to quality assurance (QA) and quality control (QC). This infrastructure, based on the ships of opportunity principle and involving the use of commercial fishing vessels, is able to produce huge amounts of useful data both for operational oceanography and fishery biology purposes.
Carmen Zarzuelo, Alejandro López-Ruiz, María Bermúdez, and Miguel Ortega-Sánchez
Earth Syst. Sci. Data, 15, 3095–3110, https://doi.org/10.5194/essd-15-3095-2023, https://doi.org/10.5194/essd-15-3095-2023, 2023
Short summary
Short summary
This paper presents a hydrodynamic dataset for the Bay of Cádiz in southern Spain, a paradigmatic example of a tidal bay of complex geometry under high anthropogenic pressure. The dataset brings together measured and modeled data on water levels, currents, density, and waves for the period 2012–2015. It allows the characterization of the bay dynamics from intratidal to seasonal scales. Potential applications include the study of ocean–bay interactions, wave propagation, or energy assessments.
Zuzanna M. Swirad, Mateusz Moskalik, and Agnieszka Herman
Earth Syst. Sci. Data, 15, 2623–2633, https://doi.org/10.5194/essd-15-2623-2023, https://doi.org/10.5194/essd-15-2623-2023, 2023
Short summary
Short summary
Monitoring ocean waves is important for understanding wave climate and seasonal to longer-term (years to decades) changes. In the Arctic, there is limited freely available observational wave information. We placed sensors at the sea bottom of six bays in Hornsund fjord, Svalbard, and calculated wave energy, wave height and wave period for full hours between July 2013 and February 2021. In this paper, we present the procedure of deriving wave properties from raw pressure measurements.
Robin V. F. de Vries, Shungudzemwoyo P. Garaba, and Sarah-Jeanne Royer
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-209, https://doi.org/10.5194/essd-2023-209, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper presents a unique dataset of hyperspectral measurements of various plastics, including aged plastics harvested from the open ocean (North Pacific Ocean) and COVID-19 related plastic items. These datasets are vital as input for the development of remote sensing technology to better map and locate plastic litter pollution in the natural environment. In this study, there is specific emphasis on the spectral characteristics of submerged plastics.
Tiziana Ciuffardi, Zoi Kokkini, Maristella Berta, Marina Locritani, Andrea Bordone, Ivana Delbono, Mireno Borghini, Maurizio Demarte, Roberta Ivaldi, Federica Pannacciulli, Anna Vetrano, Davide Marini, and Giovanni Caprino
Earth Syst. Sci. Data, 15, 1933–1946, https://doi.org/10.5194/essd-15-1933-2023, https://doi.org/10.5194/essd-15-1933-2023, 2023
Short summary
Short summary
This paper presents the results of the first 2 years of the Levante Canyon Mooring, a mooring line placed since 2020 in the eastern Ligurian Sea, to study a canyon area at about 600 m depth characterized by the presence of cold-water living corals. It provides hydrodynamic and thermohaline measurements along the water column, describing a water-mass distribution coherent with previous evidence in the Ligurian Sea. The data also show a Northern Current episodic and local reversal during summer.
Pierre L'Hégaret, Florian Schütte, Sabrina Speich, Gilles Reverdin, Dariusz B. Baranowski, Rena Czeschel, Tim Fischer, Gregory R. Foltz, Karen J. Heywood, Gerd Krahmann, Rémi Laxenaire, Caroline Le Bihan, Philippe Le Bot, Stéphane Leizour, Callum Rollo, Michael Schlundt, Elizabeth Siddle, Corentin Subirade, Dongxiao Zhang, and Johannes Karstensen
Earth Syst. Sci. Data, 15, 1801–1830, https://doi.org/10.5194/essd-15-1801-2023, https://doi.org/10.5194/essd-15-1801-2023, 2023
Short summary
Short summary
In early 2020, the EUREC4A-OA/ATOMIC experiment took place in the northwestern Tropical Atlantic Ocean, a dynamical region where different water masses interact. Four oceanographic vessels and a fleet of autonomous devices were deployed to study the processes at play and sample the upper ocean, each with its own observing capability. The article first describes the data calibration and validation and second their cross-validation, using a hierarchy of instruments and estimating the uncertainty.
Tongya Liu and Ryan Abernathey
Earth Syst. Sci. Data, 15, 1765–1778, https://doi.org/10.5194/essd-15-1765-2023, https://doi.org/10.5194/essd-15-1765-2023, 2023
Short summary
Short summary
Nearly all existing datasets of mesoscale eddies are based on the Eulerian method because of its operational simplicity. Using satellite observations and a Lagrangian method, we present a global Lagrangian eddy dataset (GLED v1.0). We conduct the statistical comparison between two types of eddies and the dataset validation. Our dataset offers relief from dilemma that the Eulerian eddy dataset is nearly the only option for studying mesoscale eddies.
Fabio Raicich
Earth Syst. Sci. Data, 15, 1749–1763, https://doi.org/10.5194/essd-15-1749-2023, https://doi.org/10.5194/essd-15-1749-2023, 2023
Short summary
Short summary
In the changing climate, long sea level time series are essential for studying the variability of the mean sea level and the occurrence of extreme events on different timescales. This work summarizes the rescue and quality control of the ultra-centennial sea level data set of Trieste, Italy. The whole time series is characterized by a linear trend of about 1.4 mm yr−1, the period corresponding to the altimetry coverage by a trend of about 3.0 mm yr−1, similarly to the global ocean.
Edwin John Rainville, Jim Thomson, Melissa Moulton, and Morteza Derakhti
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-64, https://doi.org/10.5194/essd-2023-64, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Measuring ocean waves nearshore is essential for understanding how the waves impact our coastlines. We designed and deployed many small wave buoys into the nearshore ocean over 27 days in Duck, North Carolina, USA, in 2021. The wave buoys measure their motion as they drift. In this paper, we describe how we use the measurements from the buoys to measure nearshore wave properties. We find that the buoy measurements are accurate and reliable compared to other nearby instruments.
Giulia Bonino, Simona Masina, Giuliano Galimberti, and Matteo Moretti
Earth Syst. Sci. Data, 15, 1269–1285, https://doi.org/10.5194/essd-15-1269-2023, https://doi.org/10.5194/essd-15-1269-2023, 2023
Short summary
Short summary
We present a unique observational dataset of marine heat wave (MHW) macroevents and their characteristics over southern Europe and western Asian (SEWA) basins in the SEWA-MHW dataset. This dataset is the first effort in the literature to archive extremely hot sea surface temperature macroevents. The advantages of the availability of SEWA-MHWs are avoiding the waste of computational resources to detect MHWs and building a consistent framework which would increase comparability among MHW studies.
Johannes J. Rick, Mirco Scharfe, Tatyana Romanova, Justus E. E. van Beusekom, Ragnhild Asmus, Harald Asmus, Finn Mielck, Anja Kamp, Rainer Sieger, and Karen H. Wiltshire
Earth Syst. Sci. Data, 15, 1037–1057, https://doi.org/10.5194/essd-15-1037-2023, https://doi.org/10.5194/essd-15-1037-2023, 2023
Short summary
Short summary
The Sylt Roads (Wadden Sea) time series is illustrated. Since 1984, the water temperature has risen by 1.1 °C, while pH and salinity decreased by 0.2 and 0.3 units. Nutrients (P, N) displayed a period of high eutrophication until 1998 and have decreased since 1999, while Si showed a parallel increase. Chlorophyll did not mirror these changes, probably due to a switch in nutrient limitation. Until 1998, algae were primarily limited by Si, and since 1999, P limitation has become more important.
Maria Osińska, Kornelia A. Wójcik-Długoborska, and Robert J. Bialik
Earth Syst. Sci. Data, 15, 607–616, https://doi.org/10.5194/essd-15-607-2023, https://doi.org/10.5194/essd-15-607-2023, 2023
Short summary
Short summary
Water properties, including temperature, conductivity, turbidity and pH as well as the dissolved oxygen, dissolved organic matter, chlorophyll-a and phycoerythrin contents, were investigated in 31 different locations at up to 100 m depth over a period of 38 months in a glacial bay in Antarctica. These investigations were carried out 142 times in all seasons of the year, resulting in a unique dataset of information about seasonal and long-term changes in polar water properties.
Annie P. S. Wong, John Gilson, and Cécile Cabanes
Earth Syst. Sci. Data, 15, 383–393, https://doi.org/10.5194/essd-15-383-2023, https://doi.org/10.5194/essd-15-383-2023, 2023
Short summary
Short summary
This article describes the instrument bias in the raw Argo salinity data from 2000 to 2021. The main cause of this bias is sensor drift. Using Argo data without filtering out this instrument bias has been shown to lead to spurious results in various scientific applications. We describe the Argo delayed-mode process that evaluates and adjusts such instrument bias, and we estimate the uncertainty of the Argo delayed-mode salinity dataset. The best ways to use Argo data are illustrated.
Maxime Ballarotta, Clément Ubelmann, Pierre Veillard, Pierre Prandi, Hélène Etienne, Sandrine Mulet, Yannice Faugère, Gérald Dibarboure, Rosemary Morrow, and Nicolas Picot
Earth Syst. Sci. Data, 15, 295–315, https://doi.org/10.5194/essd-15-295-2023, https://doi.org/10.5194/essd-15-295-2023, 2023
Short summary
Short summary
We present a new gridded sea surface height and current dataset produced by combining observations from nadir altimeters and drifting buoys. This product is based on a multiscale and multivariate mapping approach that offers the possibility to improve the physical content of gridded products by combining the data from various platforms and resolving a broader spectrum of ocean surface dynamic than in the current operational mapping system. A quality assessment of this new product is presented.
Md Jamal Uddin Khan, Inge Van Den Beld, Guy Wöppelmann, Laurent Testut, Alexa Latapy, and Nicolas Pouvreau
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-443, https://doi.org/10.5194/essd-2022-443, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Established in 1875, Socoa tide gauge is one of the long-running permanent tide-gauge of the South-Western France region. However, a large part of its record was in paper format in various archives facing risk of damage. Through data archaeology, these data and associated metadata documents are rescued, digitized, and constructed into a uniform hourly sea level time series from 1875 to date. This new dataset will be useful for climate research on sea level rise, tide, and storm surges.
Francesca Doglioni, Robert Ricker, Benjamin Rabe, Alexander Barth, Charles Troupin, and Torsten Kanzow
Earth Syst. Sci. Data, 15, 225–263, https://doi.org/10.5194/essd-15-225-2023, https://doi.org/10.5194/essd-15-225-2023, 2023
Short summary
Short summary
This paper presents a new satellite-derived gridded dataset, including 10 years of sea surface height and geostrophic velocity at monthly resolution, over the Arctic ice-covered and ice-free regions, up to 88° N. We assess the dataset by comparison to independent satellite and mooring data. Results correlate well with independent satellite data at monthly timescales, and the geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km.
Jiajia Yuan, Jinyun Guo, Chengcheng Zhu, Zhen Li, Xin Liu, and Jinyao Gao
Earth Syst. Sci. Data, 15, 155–169, https://doi.org/10.5194/essd-15-155-2023, https://doi.org/10.5194/essd-15-155-2023, 2023
Short summary
Short summary
The mean sea surface (MSS) is a relative steady-state sea level within a finite period with important applications in geodesy, oceanography, and other disciplines. In this study, the Shandong University of Science and Technology 2020 (SDUST2020), a new global MSS model, was established with a 19-year moving average method from multi-satellite altimetry data. Its global coverage is from 80 °S to 84 °N, the grid size is 1'×1', and the reference period is from January 1993 to December 2019.
Dirk S. van Maren, Christian Maushake, Jan-Willem Mol, Daan van Keulen, Jens Jürges, Julia Vroom, Henk Schuttelaars, Theo Gerkema, Kirstin Schulz, Thomas H. Badewien, Michaela Gerriets, Andreas Engels, Andreas Wurpts, Dennis Oberrecht, Andrew J. Manning, Taylor Bailey, Lauren Ross, Volker Mohrholz, Dante M. L. Horemans, Marius Becker, Dirk Post, Charlotte Schmidt, and Petra J. T. Dankers
Earth Syst. Sci. Data, 15, 53–73, https://doi.org/10.5194/essd-15-53-2023, https://doi.org/10.5194/essd-15-53-2023, 2023
Short summary
Short summary
This paper reports on the main findings of a large measurement campaign aiming to better understand how an exposed estuary (the Ems Estuary on the Dutch–German border) interacts with a tidal river (the lower Ems River). Eight simultaneously deployed ships measuring a tidal cycle and 10 moorings collecting data throughout a spring–neap tidal cycle have produced a dataset providing valuable insight into processes determining exchange of water and sediment between the two systems.
André Valente, Shubha Sathyendranath, Vanda Brotas, Steve Groom, Michael Grant, Thomas Jackson, Andrei Chuprin, Malcolm Taberner, Ruth Airs, David Antoine, Robert Arnone, William M. Balch, Kathryn Barker, Ray Barlow, Simon Bélanger, Jean-François Berthon, Şükrü Beşiktepe, Yngve Borsheim, Astrid Bracher, Vittorio Brando, Robert J. W. Brewin, Elisabetta Canuti, Francisco P. Chavez, Andrés Cianca, Hervé Claustre, Lesley Clementson, Richard Crout, Afonso Ferreira, Scott Freeman, Robert Frouin, Carlos García-Soto, Stuart W. Gibb, Ralf Goericke, Richard Gould, Nathalie Guillocheau, Stanford B. Hooker, Chuamin Hu, Mati Kahru, Milton Kampel, Holger Klein, Susanne Kratzer, Raphael Kudela, Jesus Ledesma, Steven Lohrenz, Hubert Loisel, Antonio Mannino, Victor Martinez-Vicente, Patricia Matrai, David McKee, Brian G. Mitchell, Tiffany Moisan, Enrique Montes, Frank Muller-Karger, Aimee Neeley, Michael Novak, Leonie O'Dowd, Michael Ondrusek, Trevor Platt, Alex J. Poulton, Michel Repecaud, Rüdiger Röttgers, Thomas Schroeder, Timothy Smyth, Denise Smythe-Wright, Heidi M. Sosik, Crystal Thomas, Rob Thomas, Gavin Tilstone, Andreia Tracana, Michael Twardowski, Vincenzo Vellucci, Kenneth Voss, Jeremy Werdell, Marcel Wernand, Bozena Wojtasiewicz, Simon Wright, and Giuseppe Zibordi
Earth Syst. Sci. Data, 14, 5737–5770, https://doi.org/10.5194/essd-14-5737-2022, https://doi.org/10.5194/essd-14-5737-2022, 2022
Short summary
Short summary
A compiled set of in situ data is vital to evaluate the quality of ocean-colour satellite data records. Here we describe the global compilation of bio-optical in situ data (spanning from 1997 to 2021) used for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The compilation merges and harmonizes several in situ data sources into a simple format that could be used directly for the evaluation of satellite-derived ocean-colour data.
Francesco Paladini de Mendoza, Katrin Schroeder, Leonardo Langone, Jacopo Chiggiato, Mireno Borghini, Patrizia Giordano, Giulio Verazzo, and Stefano Miserocchi
Earth Syst. Sci. Data, 14, 5617–5635, https://doi.org/10.5194/essd-14-5617-2022, https://doi.org/10.5194/essd-14-5617-2022, 2022
Short summary
Short summary
This work presents the dataset of continuous monitoring in the southern Adriatic Margin, providing a unique observatory of deep-water dynamics. The study area is influenced by episodic dense-water cascading, which is a fundamental process for water renewal and deep-water dynamics. Information about the frequency and intensity variations of these events is observed along a time series. The monitoring activities are still ongoing and the moorings are part of the EMSO-ERIC network.
Oriane Bruyère, Benoit Soulard, Hugues Lemonnier, Thierry Laugier, Morgane Hubert, Sébastien Petton, Térence Desclaux, Simon Van Wynsberge, Eric Le Tesson, Jérôme Lefèvre, Franck Dumas, Jean-François Kayara, Emmanuel Bourassin, Noémie Lalau, Florence Antypas, and Romain Le Gendre
Earth Syst. Sci. Data, 14, 5439–5462, https://doi.org/10.5194/essd-14-5439-2022, https://doi.org/10.5194/essd-14-5439-2022, 2022
Short summary
Short summary
From 2014 to 2021, extensive monitoring of hydrodynamics was deployed within five contrasted lagoons of New Caledonia during austral summers. These coastal physical observations encompassed unmonitored lagoons and captured eight major atmospheric events ranging from tropical depression to category 4 cyclone. The main objectives were to characterize the processes controlling hydrodynamics of these lagoons and record the signature of extreme events on land–lagoon–ocean continuum functioning.
Tian Tian, Lijing Cheng, Gongjie Wang, John Abraham, Wangxu Wei, Shihe Ren, Jiang Zhu, Junqiang Song, and Hongze Leng
Earth Syst. Sci. Data, 14, 5037–5060, https://doi.org/10.5194/essd-14-5037-2022, https://doi.org/10.5194/essd-14-5037-2022, 2022
Short summary
Short summary
A high-resolution gridded dataset is crucial for understanding ocean processes at various spatiotemporal scales. Here we used a machine learning approach and successfully reconstructed a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 (monthly) by merging in situ salinity profile observations with high-resolution satellite remote-sensing data. This new product could be useful in various applications in ocean and climate fields.
Alberto Ribotti, Antonio Bussani, Milena Menna, Andrea Satta, Roberto Sorgente, Andrea Cucco, and Riccardo Gerin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-344, https://doi.org/10.5194/essd-2022-344, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
Over a hundred of experiments were realised between 1998 and 2022 in the Mediterranean Sea using surface coastal and offshore Lagrangian drifters. Raw data were initially unified and pre-processed. Then the integrity of the received data packages was checked, and incomplete ones were discarded. Deployment information was retrieved and integrated into the PostgreSQL database. After all procedures, from the initial 138 experiments, a dataset of 204 tracks was obtained in NetCDF format.
Héloïse Lavigne, Ana Dogliotti, David Doxaran, Fang Shen, Alexandre Castagna, Matthew Beck, Quinten Vanhellemont, Xuerong Sun, Juan Ignacio Gossn, Pannimpullath Remanan Renosh, Koen Sabbe, Dieter Vansteenwegen, and Kevin Ruddick
Earth Syst. Sci. Data, 14, 4935–4947, https://doi.org/10.5194/essd-14-4935-2022, https://doi.org/10.5194/essd-14-4935-2022, 2022
Short summary
Short summary
Because of the large diversity of case 2 waters and the complexity of light transfer, retrieving main biogeochemical parameters in these waters is still challenging. By providing optical and biogeochemical parameters for 180 sampling stations with turbidity and chlorophyll-a concentration ranging from low to extreme values, the HYPERMAQ dataset will contribute to a better description of marine optics in optically complex water bodies and can help the scientific community to develop algorithms.
Mario Hoppmann, Ivan Kuznetsov, Ying-Chih Fang, and Benjamin Rabe
Earth Syst. Sci. Data, 14, 4901–4921, https://doi.org/10.5194/essd-14-4901-2022, https://doi.org/10.5194/essd-14-4901-2022, 2022
Short summary
Short summary
The role of eddies and fronts in the oceans is a hot topic in climate research, but there are still many related knowledge gaps, particularly in the ice-covered Arctic Ocean. Here we present a unique dataset of ocean observations collected by a set of drifting buoys installed on ice floes as part of the 2019/2020 MOSAiC campaign. The buoys recorded temperature and salinity data for 10 months, providing extraordinary insights into the properties and processes of the ocean along their drift.
Chengcheng Zhu, Jinyun Guo, Jiajia Yuan, Zhen Li, Xin Liu, and Jinyao Gao
Earth Syst. Sci. Data, 14, 4589–4606, https://doi.org/10.5194/essd-14-4589-2022, https://doi.org/10.5194/essd-14-4589-2022, 2022
Short summary
Short summary
Accurate marine gravity anomalies play an important role in the fields of submarine topography, Earth structure, and submarine exploitation. With the launch of different altimetry satellites, the density of altimeter data can meet the requirements of inversion of high-resolution and high-precision gravity anomaly models. We construct the global marine gravity anomaly model (SDUST2021GRA) from altimeter data (including HY-2A). The accuracy of the model is high, especially in the offshore area.
Philip L. Woodworth and John M. Vassie
Earth Syst. Sci. Data, 14, 4387–4396, https://doi.org/10.5194/essd-14-4387-2022, https://doi.org/10.5194/essd-14-4387-2022, 2022
Short summary
Short summary
An electronic data set of tidal measurements at St. Helena in 1761 by Nevil Maskelyne is described. These data were first analysed by Cartwright in papers on changing tides, but his data files were never archived. The now newly digitised Maskelyne data have been reanalysed in order to obtain an updated impression of whether the tide has changed at that location in over two and a half centuries. Our main conclusion is that the major tidal constituent (M2) has changed little.
Alberto Ribotti, Roberto Sorgente, Federica Pessini, Andrea Cucco, Giovanni Quattrocchi, and Mireno Borghini
Earth Syst. Sci. Data, 14, 4187–4199, https://doi.org/10.5194/essd-14-4187-2022, https://doi.org/10.5194/essd-14-4187-2022, 2022
Short summary
Short summary
Over 1468 hydrological vertical profiles were acquired in 21 years in the Mediterranean Sea. This allowed us to follow the diffusion of the Western Mediterranean Transient along all western seas or make some important repetitions across straits, channels, or at defined locations. These data are now available in four open-access online datasets, including profiles of water temperature, conductivity, dissolved oxygen, chlorophyll α fluorescence, and, after 2004, turbidity and pH.
Andrea Pisano, Daniele Ciani, Salvatore Marullo, Rosalia Santoleri, and Bruno Buongiorno Nardelli
Earth Syst. Sci. Data, 14, 4111–4128, https://doi.org/10.5194/essd-14-4111-2022, https://doi.org/10.5194/essd-14-4111-2022, 2022
Short summary
Short summary
A new operational diurnal sea surface temperature (SST) product has been developed within the Copernicus Marine Service, providing gap-free hourly mean SST fields from January 2019 to the present. This product is able to accurately reproduce the diurnal cycle, the typical day–night SST oscillation mainly driven by solar heating, including extreme diurnal warming events. This product can thus represent a valuable dataset to improve the study of those processes that require a subdaily frequency.
Cited articles
Alberello, A., Bennetts, L., Toffoli, A., and Derkani, M.: Antarctic
Circumnavigation Expedition 2017: WaMoS Data, Ver. 3, Australian Antarctic
Data Centre, https://doi.org/10.26179/5ed0a30aaf764, 2020.
An, J., Huang, W., and Gill, E. W.: A Self-Adaptive Wavelet-Based Algorithm
for Wave Measurement Using Nautical Radar,
IEEE T. Geosci. Remote, 53, 567–577, https://doi.org/10.1109/tgrs.2014.2325782,
2015.
Andreas, E. L.: Fallacies of the enthalpy transfer coefficient over the
ocean in high winds, J. Atmos. Sci., 68, 1435–1445,
https://doi.org/10.1175/2011JAS3714.1, 2011.
Babanin, A. V.: On a wave-induced turbulence and a wave-mixed upper ocean
layer, Geophys. Res. Lett., 33, L20605, https://doi.org/10.1029/2006GL027308, 2006.
Babanin, A. V.: Breaking and Dissipation of Ocean Surface Waves, Cambridge,
Cambridge University Press, https://doi.org/10.1017/CBO9780511736162, 2011.
Blomquist, B. W., Brumer, S. E., Fairall, C. W., Huebert, B. J., Zappa, C.
J., Brooks, I. M., Yang, M., Bariteau, L., Prytherch, J., Hare, J. E.,
Czerski, H., Matei, A., and Pascal, R. W.: Wind speed and sea state
dependencies of air-sea gas transfer: Results from the high wind speed gas
exchange study (HiWinGS), J. Geophys. Res.-Oceans, 122,
8034–8062, https://doi.org/10.1002/2017JC013181, 2017.
Buckingham, C. E., Lucas, N. S., Belcher, S. E., Rippeth, T. P., Grant, A.
L. M., Le Sommer, J., Ajayi, Opeoluwa, A., and Garabato, A. C. N.: The
contribution of surface and submesoscale processes to turbulence in the open
ocean surface boundary layer, J. Adv. Model. Earth Sy.,
11, 4066–4094, https://doi.org/10.1029/2019MS001801, 2019.
Buckley, J. R. and Aler, J.: Estimation of ocean wave height from grazing incidence microwave backscatter, in:
IGARSS'97, 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings, Remote
Sensing – A Scientific Vision for Sustainable Development, 2, 1015–1017,
https://doi.org/10.1109/IGARSS.1997.615328, 1997.
Buckley, J. R. and Aler, J.: Enhancements in the determination of ocean surface wave height from grazing
incidence microwave backscatter, in: IGARSS'98, Sensing and Managing the Environment. 1998 IEEE
International Geoscience and Remote Sensing, Symposium Proceedings, Cat. No.98CH36174, 5, 2487–2489,
https://doi.org/10.1109/IGARSS.1998.702254, 1998.
Campana, J., Terrill, E. J., and de Paolo, T.: A new inversion method to
obtain upper-ocean current-depth profiles using X-band observations of
deep-water waves, J. Atmos. Ocean. Tech., 34, 957–970,
https://doi.org/10.1175/JTECH-D-16-0120.1, 2017.
Casas-Prat, M., Wang, X. L., and Swart, N.: CMIP5-based global wave climate
projections including the entire Arctic Ocean, Ocean Model., 123, 66–85,
2018.
Cavaleri, L., Fox-Kemper, B., and Hemer, M.: Wind waves in the coupled
climate system, B. Am. Meteorol. Soc., 93, 1651–1661, 2012.
Cavaleri, L., Barbariol, F., and Benetazzo, A.: Wind-wave modeling: Where we
are, where to go, Journal of Marine Science and Engineering, 260, 8,
https://doi.org/10.3390/JMSE8040260, 2020.
Chen, Z., He, Y., Zhang, B., and Qiu, Z.: Determination of nearshore sea
surface wind vector from marine X-band radar images, Ocean Eng., 96,
79–85, https://doi.org/10.1016/J.OCEANENG.2014.12.019, 2015.
Chen, Z., Zhang, B., Kudryavtsev, V., He, Y., and Chu, X.: Estimation of sea
surface current from X-band marine radar images by cross-spectrum analysis,
Remote Sensing, 11, 1031, https://doi.org/10.3390/rs11091031, 2019.
Crombie, D. D.: Doppler spectrum of sea echo at 13.56 Mc./s, Nature, 175, 681–682, https://doi.org/10.1038/175681a0, 1955.
Cronin, M. F., Gentemann, C. L., Edson, J., Ueki, I., Bourassa, M., Brown,
S., Clayson, C. A., Fairall, C. W., Farrar, J. T., Gille, S.T., Gulev, S.,
Josey, S. A., Kato, S., Katsumata, M., Kent, E., Krug, M., Minnett, P. J.,
Parfitt, R., Pinker, R. T., Stackhouse, P. W. Jr, Swart, S., Tomita, H.,
Vandemark, D., Weller, R. A., Yoneyama, K., Yu, L. and Zhang, D.: Air-sea
fluxes with a focus on heat and momentum, Front. Mar. Sci., 6, 430, https://doi.org/10.3389/fmars.2019.00430, 2019.
Dankert, H. and Horstmann, J.: A marine radar wind sensor, J. Atmos. Ocean. Tech., 24, 1629–1642,
https://doi.org/10.1175/JTECH2083.1, 2007.
Dankert, H., Horstmann, J., and Rosenthal, W.: Ocean wind fields retrieved
from radar-image sequences, J. Geophys. Res.-Oceans,
108, 3352, https://doi.org/10.1029/2003jc002056, 2003.
Derkani, M., Alberello, A., and Toffoli, A.: Antarctic Circumnavigation
Expedition 2017: WaMoS Data Product, Ver. 1, Australian Antarctic Data
Centre, https://doi.org/10.26179/5e9d038c396f2, 2020.
Derkani, M. H., Alberello, A., Nelli, F., Bennetts, L. G., Hessner, K. G., MacHutchon, K., Reichert, K., Aouf, L., Khan, S., and Toffoli, A.: Wind, waves, and surface currents in the Southern Ocean: observations from the Antarctic Circumnavigation Expedition, Earth Syst. Sci. Data, 13, 1189–1209, https://doi.org/10.5194/essd-13-1189-2021, 2021.
Drouet, C., Cellier, N., Raymond, J., and Martigny, D.: Sea state estimation
based on ship motions measurements and data fusion, in Proceedings of the
International Conference on Offshore Mechanics and Arctic Engineering –
OMAE, 5, Nantes, France, 9–14 June 2013, OMAE2013-10657,
https://doi.org/10.1115/OMAE2013-10657, 2013.
Fan, Y. and Griffies, S. M.: Impacts of parameterized langmuir turbulence
and nonbreaking wave mixing in global climate simulations, J.
Climate, 27, 4752–4775, https://doi.org/10.1175/JCLI-D-13-00583.1, 2014.
Gangeskar, R.: Wave height derived by texture analysis of X-band radar sea surface images, IGARSS 2000,
IEEE 2000 International Geoscience and Remote Sensing Symposium, Taking the Pulse of the Planet: The Role
of Remote Sensing in Managing the Environment, Proceedings, Cat. No.00CH37120, Honolulu, Hawaii, USA,
24–28 July, 7, 2952–2959, https://doi.org/10.1109/IGARSS.2000.860301, 2000.
Gangeskar, R.: An algorithm for estimation of wave height from shadowing in
X-band radar sea surface images, IEEE T. Geosci. Remote, 52, 3373–3381, https://doi.org/10.1109/TGRS.2013.2272701, 2014.
Gavrikov, A., Ivonin, D., Sharmar, V., Tilinina, N., Gulev, S., Suslov, A.,
Fadeev, V., Trofimov, B., Bargman, S., Salavatova, L., Koshkina, V.,
Shishkova, P., and Sokov, A.,: Wind waves in the North Atlantic and Arctic
from ship navigational radar (SeaVision system) and wave buoy Spotter during
three research cruises in 2020 and 2021, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.939620, 2021.
Greenwood, C., Vogler, A., Morrison, J., and Murray, A.: The approximation
of a sea surface using a shore mounted X-band radar with low grazing angle.,
Remote Sens. Environ., 204, 439–447,
https://doi.org/10.1016/j.rse.2017.10.012, 2018.
Gulev, S. K. and Hasse, L.: North Atlantic wind waves and wind stress from
voluntary observing data, J. Phys.Oceanogr., 28, 1107–1130, 1998.
Gulev, S. K., Grigorieva, V., Sterl, A., and Woolf, D.: Assessment of the
reliability of wave observations from voluntary observing ships: insights
from the validation of a global wind wave climatology based on voluntary
observing ship data, J. Geophys. Res.-Oceans, 108, 3236,
https://doi.org/10.1029/2002JC001437, 2003.
Hasselmann, K.: Theory of synthetic aperture radar ocean imaging: a MARSEN
view, J. Geophys. Res., 90,
4659–4686,
https://doi.org/10.1029/JC090iC03p04659, 1985.
Hasselmann, S., Hasselmann, K., Allender, J. H., and Barnett, T. P.:
Computations and parameterizations of the nonlinear energy transfer in a
gravity-wave spectrum. Part II: parameterizations of the nonlinear energy
transfer for application in wave models, J. Phys. Oceanogr.,
15, 1378–1391, https://doi.org/10.1175/1520-0485(1985)015<1378:CAPOTN>2.0.CO;2, 1985.
Hatten, H., Seemann, J., Horstmann, J., and Ziemer, F.: Azimuthal dependence of the radar cross section and
the spectral background noise of a nautical radar at grazing incidence, in: IGARSS'98, Sensing and Managing
the Environment, 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings, Cat.
No.98CH36174, Seattle, WA, USA, 6–10 July, 5, 2490–2492, https://doi.org/10.1109/IGARSS.1998.702255,
1998.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R.,
Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J.,
Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes,
M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P.,
Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis,
Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Hessner, K., Reichert, K., Dittmer J., Nieto-Borge, J. C., and Heinz, G.: Evaluation of Wamos II Wave Data, in: Ocean
Wave Measurement and Analysis (2001), edited by: Edge, B. L. and Hemsley, J. M., American Society of Civil
Engineers, 221–230, https://doi.org/10.1061/40604(273)23, 2001.
Hessner, K. and Hanson, J. L.: Extraction of coastal wavefield properties from X-band radar, in: 2010 IEEE
International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, USA, 25–30 July, 4326–4329,
https://doi.org/10.1109/IGARSS.2010.5650134, 2010.
Hilmer, T. and Thornhill, E.: Observations of predictive skill for real-time Deterministic Sea Waves from the
WaMoS II, in: OCEANS 2015 – MTS/IEEE Washington, 1–7,
https://doi.org/10.23919/OCEANS.2015.7404496, 2015.
Huang, W., Gill, E. W., and An, J.: Iterative least-squares-based wave
measurement using X-band nautical radar, IET Radar Sonar Nav.,
8, 853–863, 2014.
Hwang, P. A., Sletten, M. A., and Toporkov, J. V.: A note on doppler
processing of coherent radar backscatter from the water surface: With
application to ocean surface wave measurements, J. Geophys. Res.-Oceans, 115, C03026, https://doi.org/10.1029/2009JC005870, 2010.
Ivonin, D. V., Telegin, V. A., Chernyshov, P. V., Myslenkov, S. A., and
Kuklev S. B.: Possibilities of X-band nautical radars for monitoring of wind
waves near the coast, Oceanology, 56, 591–600,
https://doi.org/10.1134/S0001437016030103, 2016.
Johnson, J. T., Burkholder, R. J., Toporkov, J. V., Lyzenga, D. R., and
Plant, W. J.: A numerical study of the retrieval of sea surface height
profiles from low grazing angle radar data, IEEE T. Geosci. Remote, 47, 1641–1650, https://doi.org/10.1109/IGARSS.2010.5650134, 2009.
Kanevsky, M. B.: Radar Imaging of the Ocean Waves, Elsevier, ISBN 9780444532091, 179–180, https://doi.org/10.1016/B978-0-444-53209-1.00010-1,
2009.
Karaev, V. Y., Kanevsky, M. B., Meshkov, E. M., Titov, V. I., and Balandina, G.
N.: Measurement of the variance of water surface slopes by a radar:
Verification of algorithms, Radiophys. Quantum El., 51, 360–371,
https://doi.org/10.1007/s11141-008-9042-6, 2008.
McWilliams, J. C. and Fox-Kemper, B.: Oceanic wave-balanced surface fronts
and filaments, J. Fluid Mech., 730, 464–490,
https://doi.org/10.1017/jfm.2013.348, 2013.
Morim, J., Trenham, C., Hemer, M., Wang, X. L., Mori, N., Casas-Prat, M., Semedo, A., Shimura, T., Timmermans,
B., Camus, P., Bricheno, L., Mentaschi, L., Dobrynin, M., Feng, Y., and Erikson, L.: A global ensemble of ocean wave
climate projections from CMIP5-driven models, Sci. Data, 7, 105,
https://doi.org/10.1038/s41597-020-0446-2, 2020.
Morim, J., Erikson, L. H., Hemer, M., Young, I., Wang, X., Mori, N., Shimura, T., Stopa, J., Trenham, C., Mentaschi,
L., Gulev, S., Sharmar, V. D., Bricheno, L., Wolf, J., Aarnes, O., Perez, J., Bidlot, J., Semedo, A., Reguero, B., and
Wahl, T.: A global ensemble of ocean wave
climate statistics from contemporary wave reanalysis and hindcasts, Sci. Data,
9, 358, https://doi.org/10.1038/s41597-022-01459-3, 2022.
Nagai, T., Satomi, S., Terada, Y., Kato, T., Nukada, K., and Kudaka, M.: GPS buoy and seabed installed wave
gauge application to offshore tsunami observation, Proceedings of the International Offshore and Polar
Engineering Conference, Seoul, Korea, 19–24 June, ISOPE-I-05-282,
https://onepetro.org/ISOPEIOPEC/proceedings-abstract/ISOPE05/All-ISOPE05/ISOPE-I-05-282/9448 (last access: 4 August 2022),
2005.
Nieto-Borge, J. C. N. and Soares, G. C.: Analysis of directional wave fields
using X-band navigation radar, Coast. Eng., 40, 375–391,
https://doi.org/10.1016/S0378-3839(00)00019-3, 2000.
Nieto-Borge, J. C. N., Reichert, K., and Dittmer, J.: Use of nautical radar as a wave
monitoring instrument, Coast. Eng., 37, 331–342, 1999.
Nieto-Borge, J. C. N., Jarabo-Amores, P., De La Mata-Moya, D., and
López-Ferreras, F.: Estimation of ocean wave heights from temporal
sequences of X-band marine radar images, European Signal Processing
Conference, Lüneburg, Germany 4–9 June 2006, 35–41,
https://doi.org/10.1115/OMAE2006-92015, 2006.
Nieto-Borge, J. C. N., Hessner, K., Jarabo-Amores, P., and De La Mata-Moya, D.:
Signal-to-noise ratio analysis to estimate ocean wave heights from X-band
marine radar image time series, IET Radar Sonar Nav., 2, 35–41,
https://doi.org/10.1049/iet-rsn:20070027, 2008.
Park, G. I., Choi, J. W., Kang, Y. T., Ha, M. K., Jang, H. S., Park, J. S.,
and Kwon, S. H.: The application of marine X-band radar to measure wave
condition during sea trial, Journal of Ship and Ocean Technology, 10,
34–48,
2006.
Plant, W. J., Keller, W. C., Reeves, A. B., Uliana, E. A., and Johnson, J.
W.: Airborne microwave doppler measurements of ocean wave directional
spectra, Int. J. Remote Sens., 8, 315–330,
https://doi.org/10.1080/01431168708948644, 1987.
Plant, W. J.: A model for microwave Doppler sea return at high incidence
angles: Bragg scattering from bound, Tilted waves, J. Geophys. Res.-Oceans, 102, 21131–21146, https://doi.org/10.1029/97JC01225, 1997.
Raghukumar, K., Chang, G., Spada, F., Jones, C., Janssen, T., and Gans, A.:
Performance characteristics of “spotter,”' a newly developed real-time
wave measurement buoy, J. Atmos. Ocean. Tech., 36, 1127–1141,
https://doi.org/10.1175/JTECH-D-18-0151.1, 2019.
Reichert, K., Hessner, K., Dannenberg, J., and Traenkmann, I.: X-Band radar
as a tool to determine spectral and single wave properties, in Proceedings
of the International Conference on Offshore Mechanics and Arctic Engineering
– OMAE, Hamburg, Germany, 4–9 June 2006, 683–688,
https://doi.org/10.1115/OMAE2006-92015, 2006.
Ribal, A. and Young, I. R.: Publisher Correction: 33 years of globally
calibrated wave height and wind speed data based on altimeter observations
Sci. Data, 6, 77,
https://doi.org/10.1038/s41597-019-0108-4, 2019.
Ribas-Ribas, M., Helleis, F., Rahlff, J., and Wurl, O.: Air-Sea CO2-exchange
in a large annular wind-wave tank and the effects of surfactants, Front.
Mar. Sci., 5, 457, https://doi.org/10.3389/fmars.2018.00457, 2018.
Rogers, W. E., Babanin, A. V., and Wang, D. W.: Observation-consistent input
and whitecapping dissipation in a model for wind-generated surface waves:
Description and simple calculations, J. Atmos. Ocean. Tech., 29, 1329–1345, https://doi.org/10.1175/JTECH-D-11-00092.1, 2012.
Seemann, J., Ziemer, F., and Senet, C. M.: Method for computing calibrated
ocean wave spectra from measurements with a nautical X-band radar, in: Oceans
Conference Record (IEEE), 2, Halifax, NS, Canada, 6–9 October 1997,
https://doi.org/10.1109/OCEANS.1997.624154, 1997.
Semedo, A., Dobrynin, M., Lemos, G., Behrens, A., Staneva, J., De Vries, H., Sterl, A., Bidlot, J.-R., Miranda, P. M.
A., and Murawski, J.: CMIP5-Derived Single-Forcing,
Single-Model, and Single-Scenario Wind-Wave Climate Ensemble: Configuration
and Performance Evaluation, Journal of Marine Science and Engineering, 6,
90, https://doi.org/10.3390/jmse6030090, 2018.
Senet, C. M., Seemann, J., and Ziemer, F.: The near-surface current velocity
determined from image sequences of the sea surface, IEEE T.
Geosci. Remote, 39, 492–505, https://doi.org/10.1109/36.911108,
2001.
Sharmar, V. D., Markina, M. Y., and Gulev, S. K.: Global ocean wind-wave model
hindcasts forced by different reanalyzes: a comparative assessment, J. Geophys. Res.-Oceans, 126, e2020JC016710, https://doi.org/10.1029/2020JC016710, 2021.
Smit, P. B., Houghton, I. A., Jordanova, K., Portwood, T., Shapiro, E., Clark,
D., Sosa, M., and Janssen, T. T.: Assimilation of significant wave height
from distributed ocean wave sensors, Ocean Model., 15, 101738,
https://doi.org/10.1016/j.ocemod.2020.101738, 2021.
Story, W. R., Fu, T. C., and Hackett, E. E.: Radar measurement of ocean waves, in: Proceedings of the
International Conference on Offshore Mechanics and Arctic Engineering – OMAE TS71, Rotterdam, The
Netherlands, 19–24 June, 6, 707–717, https://doi.org/10.1115/OMAE2011-49895, 2011.
Studholme, J., Fedorov, A. V., Gulev, S. K., Emanuel, K., and Hodges, K.:
Poleward expansion of tropical cyclone latitudes in warming climates, Nat.
Geosci., 15, 14–28, https://doi.org/10.1038/s41561-021-00859-1, 2021.
Swail, V., Jensen, R. E., Lee, B., Turton, J., Thomas, J., Gulev, S.,
Yelland, M., Etala, P., Meldrum, D., Birkemeier, W., Burnett, B., and
Warren, G.: Wave measurements, needs and developments for the next decade in in:
Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society (Vol. 2), Venice, Italy,
21–25 September 2009, edited by: Hall, J., Harrison, D. E., and Stammer, D., ESA Publication WPP-306,
https://doi.org/10.5270/OceanObs09.cwp.87, 2010.
The WAMDI Group: The WAM Model – A Third Generation Ocean Wave Prediction Model, J. Phys. Oceanogr.,
18, 1775–1810, https://doi.org/10.1175/1520-0485(1988)018<1775:TWMTGO>2.0.CO;2, 1988.
Verezemskaya, P., Barnier, B., Gulev, S. K., Gladyshev, S., Molines, J. M.,
Gladyshev, V., Lellouche, J. M., and Gavrikov, A.: Assessing eddying
(1/12∘) ocean reanalysis GLORYS12 using the 14-yr instrumental
record from 59.5∘ N section in the Atlantic, J. Geophys. Res.-Oceans, 126, e2020JC016317, https://doi.org/10.1029/2020JC016317,
2021.
Vicen-Bueno, R., Lido-Muela, C., and Borge, J. C. N.: Estimate of significant
wave height from noncoherent marine radar images by multilayer perceptrons,
Eurasip J. Adv. Sig. Pr., 1, 2012,
https://doi.org/10.1186/1687-6180-2012-84, 2012.
Vicen-Bueno, R., Horstmann, J., Terril, E., de Paolo, T., and Dannenberg, J.:
Real-time ocean wind vector retrieval from marine radar image sequences
acquired at grazing angle, J. Atmos. Ocean. Tech.,
30, 127–139, https://doi.org/10.1175/JTECH-D-12-00027.1, 2013.
WAVEWATCH III Development Group (WW3DG): User manual and system
documentation of WAVEWATCH III R version 6.07. Tech. Note 333,
NOAA/NWS/NCEP/MMAB, College Park, MD, USA, 465 + Appendices,
https://github.com/NOAA-EMC/WW3/wiki/Manual (last access: 5 August 2022), 2019.
Xu, X., Voermans, J. J., Ma, H., Guan, C., and Babanin, A. V.: A Wind–Wave-Dependent Sea Spray Volume Flux
Model Based on Field Experiments, J. Mar. Sci. Eng., 9, 1168, https://doi.org/10.3390/jmse9111168, 2021.
Young, I. R., Rosenthal, W., and Ziemer, F.: A three-dimensional analysis of
marine radar images for the determination of ocean wave directionality and
surface currents, J. Geophys. Res., 90, 1049–1059,
https://doi.org/10.1029/JC090iC01p01049, 1985.
Zieger, S., Babanin, A. V., Rogers, W. E., and Young, I. R.:
Observation-based source terms in the third-generation wave model WAVEWATCH,
Ocean Model., 96, 2–25, 2015.
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...
Altmetrics
Final-revised paper
Preprint