Articles | Volume 21, issue 1
https://doi.org/10.5194/os-21-63-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-21-63-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
15 Jan 2025
Research article | Highlight paper |
| 15 Jan 2025
| 15 Jan 2025
Integrating wide-swath altimetry data into Level-4 multi-mission maps
Maxime Ballarotta
CORRESPONDING AUTHOR
Collecte Localisation Satellites (CLS), Ramonville-Saint-Agne, France
Clément Ubelmann
Datlas, Saint-Martin-d'Hères, France
Valentin Bellemin-Laponnaz
Université Grenoble Alpes (UGA), Grenoble, France
Florian Le Guillou
European Space Agency (ESA), Frascati, Italy
Guillaume Meda
Collecte Localisation Satellites (CLS), Ramonville-Saint-Agne, France
Cécile Anadon
Collecte Localisation Satellites (CLS), Ramonville-Saint-Agne, France
Alice Laloue
Collecte Localisation Satellites (CLS), Ramonville-Saint-Agne, France
Antoine Delepoulle
Collecte Localisation Satellites (CLS), Ramonville-Saint-Agne, France
Yannice Faugère
Centre National d'Études Spatiales (CNES), Toulouse, France
Marie-Isabelle Pujol
Collecte Localisation Satellites (CLS), Ramonville-Saint-Agne, France
Ronan Fablet
IMT Atlantique, Plouzané, France
Gérald Dibarboure
Centre National d'Études Spatiales (CNES), Toulouse, France
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Florian Le Guillou, Lucile Gaultier, Maxime Ballarotta, Sammy Metref, Clément Ubelmann, Emmanuel Cosme, and Marie-Helène Rio
Ocean Sci., 19, 1517–1527, https://doi.org/10.5194/os-19-1517-2023, https://doi.org/10.5194/os-19-1517-2023, 2023
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Antonio Sánchez-Román, M. Isabelle Pujol, Yannice Faugère, and Ananda Pascual
Ocean Sci., 19, 793–809, https://doi.org/10.5194/os-19-793-2023, https://doi.org/10.5194/os-19-793-2023, 2023
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Maxime Beauchamp, Quentin Febvre, Hugo Georgenthum, and Ronan Fablet
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Oscar Vergara, Rosemary Morrow, Marie-Isabelle Pujol, Gérald Dibarboure, and Clément Ubelmann
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Maxime Ballarotta, Clément Ubelmann, Pierre Veillard, Pierre Prandi, Hélène Etienne, Sandrine Mulet, Yannice Faugère, Gérald Dibarboure, Rosemary Morrow, and Nicolas Picot
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Marie-Isabelle Pujol, Stéphanie Dupuy, Oscar Vergara, Antonio Sánchez-Román, Yannice Faugère, Pierre Prandi, Mei-Ling Dabat, Quentin Dagneaux, Marine Lievin, Emeline Cadier, Gérald Dibarboure, and Nicolas Picot
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-292, https://doi.org/10.5194/essd-2022-292, 2022
Manuscript not accepted for further review
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Ocean Sci., 18, 469–481, https://doi.org/10.5194/os-18-469-2022, https://doi.org/10.5194/os-18-469-2022, 2022
Short summary
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Earth Syst. Sci. Data, 14, 1087–1107, https://doi.org/10.5194/essd-14-1087-2022, https://doi.org/10.5194/essd-14-1087-2022, 2022
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Pierre Prandi, Jean-Christophe Poisson, Yannice Faugère, Amandine Guillot, and Gérald Dibarboure
Earth Syst. Sci. Data, 13, 5469–5482, https://doi.org/10.5194/essd-13-5469-2021, https://doi.org/10.5194/essd-13-5469-2021, 2021
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We investigate how mapping sea level in the Arctic Ocean can benefit from combining data from three satellite radar altimeters: CryoSat-2, Sentinel-3A and SARAL/AltiKa. A dedicated processing for SARAL/AltiKa provides a baseline for the cross-referencing of CryoSat-2 and Sentinel-3A before mapping. We show that by combining measurements coming from three missions, we are able to increase the resolution of gridded sea level fields in the ice-covered Arctic Ocean.
Sandrine Mulet, Marie-Hélène Rio, Hélène Etienne, Camilia Artana, Mathilde Cancet, Gérald Dibarboure, Hui Feng, Romain Husson, Nicolas Picot, Christine Provost, and P. Ted Strub
Ocean Sci., 17, 789–808, https://doi.org/10.5194/os-17-789-2021, https://doi.org/10.5194/os-17-789-2021, 2021
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Satellite altimetry has revolutionized ocean observation by allowing the sea level to be monitored with very good spatiotemporal coverage. However, only the sea level anomalies are retrieved; to monitor the whole oceanic signal a temporal mean (called mean dynamic topography, MDT) must be added to these anomalies. In this study we present the newly updated CNES-CLS18 MDT. An evaluation of this new solution shows significant improvements in both strong currents and coastal areas.
R. Fablet, M. M. Amar, Q. Febvre, M. Beauchamp, and B. Chapron
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 295–302, https://doi.org/10.5194/isprs-annals-V-3-2021-295-2021, https://doi.org/10.5194/isprs-annals-V-3-2021-295-2021, 2021
Loren Carrere, Brian K. Arbic, Brian Dushaw, Gary Egbert, Svetlana Erofeeva, Florent Lyard, Richard D. Ray, Clément Ubelmann, Edward Zaron, Zhongxiang Zhao, Jay F. Shriver, Maarten Cornelis Buijsman, and Nicolas Picot
Ocean Sci., 17, 147–180, https://doi.org/10.5194/os-17-147-2021, https://doi.org/10.5194/os-17-147-2021, 2021
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Internal tides can have a signature of several centimeters at the ocean surface and need to be corrected from altimeter measurements. We present a detailed validation of several internal-tide models using existing satellite altimeter databases. The analysis focuses on the main diurnal and semidiurnal tidal constituents. Results show the interest of the methodology proposed, the quality of the internal-tide models tested and their positive contribution for estimating an accurate sea level.
Olivier Pannekoucke and Ronan Fablet
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Learning physics from data using a deep neural network is a challenge that requires an appropriate but unknown network architecture. The package introduced here helps to design an architecture by translating known physical equations into a network, which the experimenter completes to capture unknown physical processes. A test bed is introduced to illustrate how this learning allows us to focus on truly unknown physical processes in the hope of making better use of data and digital resources.
Guillaume Taburet, Antonio Sanchez-Roman, Maxime Ballarotta, Marie-Isabelle Pujol, Jean-François Legeais, Florent Fournier, Yannice Faugere, and Gerald Dibarboure
Ocean Sci., 15, 1207–1224, https://doi.org/10.5194/os-15-1207-2019, https://doi.org/10.5194/os-15-1207-2019, 2019
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This paper deals with sea level altimetery products. These geophysical data are distributed as along-track and gridded data through Copernicus programs CMEMS and C3S. We present in detail a new reprocessing of the data (DT2018) from 1993 to 2017. The main changes and their impacts since the last version (DT2014) are carefully discussed. This comparison is made using an independent dataset. DT2018 sea level products are improved at the global and regional scale, especially in coastal areas.
Maxime Ballarotta, Clément Ubelmann, Marie-Isabelle Pujol, Guillaume Taburet, Florent Fournier, Jean-François Legeais, Yannice Faugère, Antoine Delepoulle, Dudley Chelton, Gérald Dibarboure, and Nicolas Picot
Ocean Sci., 15, 1091–1109, https://doi.org/10.5194/os-15-1091-2019, https://doi.org/10.5194/os-15-1091-2019, 2019
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This study investigates the resolving capabilities of the DUACS gridded products delivered through the CMEMS catalogue. Our method is based on the noise-to-signal ratio approach. While altimeter along-track data resolve scales on the order of a few tens of kilometers, we found that the merging of these along-track data into continuous maps in time and space leads to effective resolution ranging from ~ 800 km wavelength at the Equator to 100 km wavelength at high latitude.
Fabrice Ardhuin, Yevgueny Aksenov, Alvise Benetazzo, Laurent Bertino, Peter Brandt, Eric Caubet, Bertrand Chapron, Fabrice Collard, Sophie Cravatte, Jean-Marc Delouis, Frederic Dias, Gérald Dibarboure, Lucile Gaultier, Johnny Johannessen, Anton Korosov, Georgy Manucharyan, Dimitris Menemenlis, Melisa Menendez, Goulven Monnier, Alexis Mouche, Frédéric Nouguier, George Nurser, Pierre Rampal, Ad Reniers, Ernesto Rodriguez, Justin Stopa, Céline Tison, Clément Ubelmann, Erik van Sebille, and Jiping Xie
Ocean Sci., 14, 337–354, https://doi.org/10.5194/os-14-337-2018, https://doi.org/10.5194/os-14-337-2018, 2018
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The Sea surface KInematics Multiscale (SKIM) monitoring mission is a proposal for a future satellite that is designed to measure ocean currents and waves. Using a Doppler radar, the accurate measurement of currents requires the removal of the mean velocity due to ocean wave motions. This paper describes the main processing steps needed to produce currents and wave data from the radar measurements. With this technique, SKIM can provide unprecedented coverage and resolution, over the global ocean.
Marie-Isabelle Pujol, Yannice Faugère, Guillaume Taburet, Stéphanie Dupuy, Camille Pelloquin, Michael Ablain, and Nicolas Picot
Ocean Sci., 12, 1067–1090, https://doi.org/10.5194/os-12-1067-2016, https://doi.org/10.5194/os-12-1067-2016, 2016
F. d'Ovidio, A. Della Penna, T. W. Trull, F. Nencioli, M.-I. Pujol, M.-H. Rio, Y.-H. Park, C. Cotté, M. Zhou, and S. Blain
Biogeosciences, 12, 5567–5581, https://doi.org/10.5194/bg-12-5567-2015, https://doi.org/10.5194/bg-12-5567-2015, 2015
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Field campaigns are instrumental in providing ground truth for understanding and modeling global ocean biogeochemical budgets. A survey however can only inspect a fraction of the global oceans, typically a region hundreds of kilometers wide for a temporal window of the order of (at most) several weeks. In this spatiotemporal domain, mesoscale variability can mask climatological contrasts. Here we propose the use of multisatellite-based Lagrangian diagnostics to solve this issue.
M. Ballarotta, F. Roquet, S. Falahat, Q. Zhang, and G. Madec
Clim. Past Discuss., https://doi.org/10.5194/cpd-11-3597-2015, https://doi.org/10.5194/cpd-11-3597-2015, 2015
Revised manuscript not accepted
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We investigate the impact of the ocean geothermal heating (OGH) on a glacial ocean state using numerical simulations. We found that the OGH is a significant forcing of the abyssal ocean and thermohaline circulation. Applying the OGH warms the Antarctic Bottom Water by ~0.4°C and strengthens the deep circulation by 15% to 30%. The geothermally heated waters are advected from the Indo-Pacific to the North Atlantic basin, indirectly favouring the deep convection in the North Atlantic.
M. Ballarotta, S. Falahat, L. Brodeau, and K. Döös
Ocean Sci., 10, 907–921, https://doi.org/10.5194/os-10-907-2014, https://doi.org/10.5194/os-10-907-2014, 2014
M. Ballarotta, L. Brodeau, J. Brandefelt, P. Lundberg, and K. Döös
Clim. Past, 9, 2669–2686, https://doi.org/10.5194/cp-9-2669-2013, https://doi.org/10.5194/cp-9-2669-2013, 2013
M. Ballarotta, L. Brodeau, J. Brandefelt, P. Lundberg, and K. Döös
Clim. Past Discuss., https://doi.org/10.5194/cpd-9-297-2013, https://doi.org/10.5194/cpd-9-297-2013, 2013
Revised manuscript has not been submitted
Related subject area
Approach: Remote Sensing | Properties and processes: Mesoscale to submesoscale dynamics
Advances in surface water and ocean topography for fine-scale eddy identification from altimeter sea surface height merging maps in the South China Sea
Enhanced resolution capability of SWOT sea surface height measurements and their application in monitoring ocean dynamics variability
Generation of super-resolution gap-free ocean colour satellite products using data-interpolating empirical orthogonal functions (DINEOF)
Synoptic observation of full mesoscale eddy lifetime and its secondary instabilities in the Gulf of Mexico
Sargassum accumulation and transport by mesoscale eddies
Blending 2D topography images from the Surface Water and Ocean Topography (SWOT) mission into the altimeter constellation with the Level-3 multi-mission Data Unification and Altimeter Combination System (DUACS)
Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms
Monitoring the coastal–offshore water interactions in the Levantine Sea using ocean color and deep supervised learning
Multiple timescale variations in fronts in the Seto Inland Sea, Japan
MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion
Deep learning for the super resolution of Mediterranean sea surface temperature fields
Impact of surface and subsurface-intensified eddies on sea surface temperature and chlorophyll a in the northern Indian Ocean utilizing deep learning
Regional mapping of energetic short mesoscale ocean dynamics from altimetry: performances from real observations
Ocean 2D eddy energy fluxes from small mesoscale processes with SWOT
Xiaoya Zhang, Lei Liu, Jianfang Fei, Zhijin Li, Zexun Wei, Zhiwei Zhang, Xingliang Jiang, Zexin Dong, and Feng Xu
Ocean Sci., 21, 1033–1045, https://doi.org/10.5194/os-21-1033-2025, https://doi.org/10.5194/os-21-1033-2025, 2025
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Our research evaluated the precision of mapping the ocean's surface with combined data from a couple of satellites, focusing on dynamic aspects revealed by sea level changes. The results show that 2DVAR (two-dimensional variation), a new mapping product, aligns more closely and with less error with the most advanced satellite observations than a widely used mapping product called AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic). The results suggest that 2DVAR detects minor ocean movements better, making it more valuable and reliable for ocean dynamic study.
Yong Wang, Shengjun Zhang, and Yongjun Jia
Ocean Sci., 21, 931–944, https://doi.org/10.5194/os-21-931-2025, https://doi.org/10.5194/os-21-931-2025, 2025
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The distance-weighted averaging method was used to calculate the along-orbit sea surface height (SSH) wavenumber spectra of four satellites and to evaluate the along-track resolution capability of the four satellites. The results show that the resolution of Surface Water and Ocean Topography (SWOT) in the Kuroshio region is 25 km, which is twice the resolution of conventional satellites. A parameter was defined using the cross-power-spectrum approach and used to analyse the global ocean.
Aida Alvera-Azcárate, Dimitry Van der Zande, Alexander Barth, Antoine Dille, Joppe Massant, and Jean-Marie Beckers
Ocean Sci., 21, 787–805, https://doi.org/10.5194/os-21-787-2025, https://doi.org/10.5194/os-21-787-2025, 2025
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This work presents an approach for increasing the spatial resolution of satellite data and interpolating gaps due to cloud cover, using a method called DINEOF (data-interpolating empirical orthogonal functions). The method is tested on turbidity and chlorophyll-a concentration data in the Belgian coastal zone and the North Sea. The results show that we are able to improve the spatial resolution of these data in order to perform analyses of spatial and temporal variability in coastal regions.
Charly de Marez
EGUsphere, https://doi.org/10.5194/egusphere-2025-1592, https://doi.org/10.5194/egusphere-2025-1592, 2025
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Oceanic eddies are giant swirling currents that help transport heat, nutrients, and pollutants across the ocean. However, their full life cycle has never been observed in detail. Using new satellite data, we tracked an intense eddy in the Gulf of Mexico, describing its life cycle from birth to dissipation. Our observations reveal small scale turbulence surrounding it and interactions with other structures that shape its evolution and decay, validating decades of numerical studies.
Rosmery Sosa-Gutierrez, Julien Jouanno, and Leo Berline
EGUsphere, https://doi.org/10.5194/egusphere-2025-514, https://doi.org/10.5194/egusphere-2025-514, 2025
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Since 2010, pelagic Sargassum blooms have increased in several tropical Atlantic regions, causing socioeconomic and ecosystem impacts. Offshore the structuration of Sargassum by the mesoscale dynamics may influence transport and growth. Sargassum, stays afloat, constantly interacting with currents, waves, winds, and mesoscale eddies. We find that anticyclonic and cyclonic effectively trap Sargassum throughout their propagation, with a greater tendency for cyclones to accumulate Sargassum.
Gerald Dibarboure, Cécile Anadon, Frédéric Briol, Emeline Cadier, Robin Chevrier, Antoine Delepoulle, Yannice Faugère, Alice Laloue, Rosemary Morrow, Nicolas Picot, Pierre Prandi, Marie-Isabelle Pujol, Matthias Raynal, Anaelle Tréboutte, and Clément Ubelmann
Ocean Sci., 21, 283–323, https://doi.org/10.5194/os-21-283-2025, https://doi.org/10.5194/os-21-283-2025, 2025
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The Surface Water and Ocean Topography (SWOT) mission delivers unprecedented swath-altimetry products. In this paper, we describe how we extended the Level-3 algorithms to handle SWOT’s unique swath-altimeter data. We also illustrate and discuss the benefits, relevance, and limitations of Level-3 swath-altimeter products for various research domains.
Daniele Ciani, Claudia Fanelli, and Bruno Buongiorno Nardelli
Ocean Sci., 21, 199–216, https://doi.org/10.5194/os-21-199-2025, https://doi.org/10.5194/os-21-199-2025, 2025
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Ocean surface currents are routinely derived from satellite observations of the sea level, allowing regional- to global-scale synoptic monitoring. In order to overcome the theoretical and instrumental limits of this methodology, we exploit the synergy of multi-sensor satellite observations. We rely on deep learning, physics-informed algorithms to predict ocean currents from sea surface height and sea surface temperature observations. Results are validated by means of in situ measurements.
Georges Baaklini, Julien Brajard, Leila Issa, Gina Fifani, Laurent Mortier, and Roy El Hourany
Ocean Sci., 20, 1707–1720, https://doi.org/10.5194/os-20-1707-2024, https://doi.org/10.5194/os-20-1707-2024, 2024
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Understanding the flow of the Levantine Sea surface current is not straightforward. We propose a study based on learning techniques to follow interactions between water near the shore and further out at sea. Our results show changes in the coastal currents past 33.8° E, with frequent instances of water breaking away along the Lebanese coast. These events happen quickly and sometimes lead to long-lasting eddies. This study underscores the need for direct observations to improve our knowledge.
Menghong Dong and Xinyu Guo
Ocean Sci., 20, 1527–1546, https://doi.org/10.5194/os-20-1527-2024, https://doi.org/10.5194/os-20-1527-2024, 2024
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We employed a gradient-based algorithm to identify the position and intensity of the fronts in a coastal sea using sea surface temperature data, thereby quantifying their variations. Our study provides a comprehensive analysis of these fronts, elucidating their seasonal variability, intra-tidal dynamics, and the influence of winds on the fronts. By capturing the temporal and spatial dynamics of these fronts, our understanding of the complex oceanographic processes within this region is enhanced.
Edwin Goh, Alice Yepremyan, Jinbo Wang, and Brian Wilson
Ocean Sci., 20, 1309–1323, https://doi.org/10.5194/os-20-1309-2024, https://doi.org/10.5194/os-20-1309-2024, 2024
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An AI model was used to fill in missing parts of sea temperature (SST) maps caused by cloud cover. We found masked autoencoders can recreate missing SSTs with less than 0.2 °C error, even when 80 % are missing. This is 5000 times faster than conventional methods tested on a single central processing unit. This can enhance our ability in monitoring global small-scale ocean fronts that affect heat, carbon, and nutrient exchange in the ocean. The method is promising for future research.
Claudia Fanelli, Daniele Ciani, Andrea Pisano, and Bruno Buongiorno Nardelli
Ocean Sci., 20, 1035–1050, https://doi.org/10.5194/os-20-1035-2024, https://doi.org/10.5194/os-20-1035-2024, 2024
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Sea surface temperature (SST) is an essential variable to understanding the Earth's climate system, and its accurate monitoring from space is essential. Since satellite measurements are hindered by cloudy/rainy conditions, data gaps are present even in merged multi-sensor products. Since optimal interpolation techniques tend to smooth out small-scale features, we developed a deep learning model to enhance the effective resolution of gap-free SST images over the Mediterranean Sea to address this.
Yingjie Liu and Xiaofeng Li
Ocean Sci., 19, 1579–1593, https://doi.org/10.5194/os-19-1579-2023, https://doi.org/10.5194/os-19-1579-2023, 2023
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The study developed a deep learning model that effectively distinguishes between surface- and subsurface-intensified eddies in the northern Indian Ocean by integrating sea surface height and temperature data. The accurate distinction between these types of eddies provides valuable insights into their dynamics and their impact on marine ecosystems in the northern Indian Ocean and contributes to understanding the complex interactions between eddy dynamics and biogeochemical processes in the ocean.
Florian Le Guillou, Lucile Gaultier, Maxime Ballarotta, Sammy Metref, Clément Ubelmann, Emmanuel Cosme, and Marie-Helène Rio
Ocean Sci., 19, 1517–1527, https://doi.org/10.5194/os-19-1517-2023, https://doi.org/10.5194/os-19-1517-2023, 2023
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Altimetry provides sea surface height (SSH) data along one-dimensional tracks. For many applications, the tracks are interpolated in space and time to provide gridded SSH maps. The operational SSH gridded products filter out the small-scale signals measured on the tracks. This paper evaluates the performances of a recently implemented dynamical method to retrieve the small-scale signals from real SSH data. We show a net improvement in the quality of SSH maps when compared to independent data.
Elisa Carli, Rosemary Morrow, Oscar Vergara, Robin Chevrier, and Lionel Renault
Ocean Sci., 19, 1413–1435, https://doi.org/10.5194/os-19-1413-2023, https://doi.org/10.5194/os-19-1413-2023, 2023
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Oceanic eddies are the structures carrying most of the energy in our oceans. They are key to climate regulation and nutrient transport. We prepare for the Surface Water and Ocean Topography mission, studying eddy dynamics in the region south of Africa, where the Indian and Atlantic oceans meet, using models and simulated satellite data. SWOT will provide insights into the structures smaller than what is currently observable, which appear to greatly contribute to eddy kinetic energy and strain.
Cited articles
Archambault, T., Filoche, A., Charantonnis, A., and Béréziat, D.: Multimodal unsupervised spatio-temporal interpolation of satellite ocean altimetry maps, InVISAPP, 2023.
Archambault, T., Filoche, A., Charantonis, A., Béréziat, D., and Thiria, S.: Learning Sea Surface Height Interpolation From Multi-Variate Simulated Satellite Observations, JAMES, 16, https://doi.org/10.1029/2023MS004047, 2024.
Archer, M. R., Li, Z., Wang, J., and Fu, L.: Reconstructing fine-scale ocean variability via data assimilation of the SWOT pre-launch in situ observing system, J. Geophys. Res.-Oceans, 127, e2021JC017362, https://doi.org/10.1029/2021JC017362, 2022.
Aviso+: Timeline of modern radar altimetry missions, version 2024/01, https://doi.org/10.24400/527896/A02-2022.001, 2022.
AVISO/DUACS: SWOT Level-3 KaRIn Low Rate SSH Expert (v1.0), CNES [data set], https://doi.org/10.24400/527896/A01-2023.018, 2024a.
AVISO/DUACS: SALTO/DUACS Mutlimission Experimental Level-4 maps, computed with SWOT Level-3 products (using both KaRIn and nadir instruments) (v1.0), CNES [data set], https://doi.org/10.24400/527896/a01-2004.007, 2024b.
Ballarotta, M. and Metref, S.: ocean-data-challenges/2024_DC_SSH_mapping_SWOT_OSE: Material for SWOT SSH mapping data challenge, Zenodo [code], https://doi.org/10.5281/zenodo.14187279, 2024.
Ballarotta, M., Ubelmann, C., Pujol, M.-I., Taburet, G., Fournier, F., Legeais, J.-F., Faugère, Y., Delepoulle, A., Chelton, D., Dibarboure, G., and Picot, N.: On the resolutions of ocean altimetry maps, Ocean Sci., 15, 1091–1109, https://doi.org/10.5194/os-15-1091-2019, 2019.
Ballarotta, M., Ubelmann, C., Rogé, M., Fournier, F., Faugère, Y., Dibarboure, G., Morrow, R., and Picot, N: Dynamic Mapping of Along-Track Ocean Altimetry: Performance from Real Observations, J. Atmos. Ocean. Tech., 37, 1593–1601, https://doi.org/10.1175/JTECH-D-20-0030.1, 2020a.
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Ballarotta, M., Ubelmann, C., Veillard, P., Prandi, P., Etienne, H., Mulet, S., Faugère, Y., Dibarboure, G., Morrow, R., and Picot, N.: Improved global sea surface height and current maps from remote sensing and in situ observations, Earth Syst. Sci. Data, 15, 295–315, https://doi.org/10.5194/essd-15-295-2023, 2023.
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Ducet, N., Le Traon, P. Y., and Reverdin, G.: Global high-resolution mapping of ocean circulation from the combination of T/P and ERS-1/2, J. Geophys. Res., 105, 19477–19498, https://doi.org/10.1029/2000JC900063, 2000.
Dufau, C., Orsztynowicz, M., Dibarboure, G., Morrow, R., and Le Traon, P.-Y.: Mesoscale resolution capability of altimetry: present and future, J. Geophys. Res.-Oceans, 121, 4910–4927, https://doi.org/10.1002/2015JC010904, 2016.
King, R. R., Martin, M. J., Gaultier, L., Waters, J., Ubelmann, C., and Donlon, C.: Assessing the impact of future altimeter constellations in the Met Office global ocean forecasting system, Ocean Sci., 20, 1657–1676, https://doi.org/10.5194/os-20-1657-2024, 2024.
Fablet, R., Beauchamp, M., Drumetz, L., and Rousseau, F.: Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields, Front. Appl. Math. Stat., 7, 655224, https://doi.org/10.3389/fams.2021.655224, 2021.
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Gaultier, L., Ubelmann, C., and Fu, L.-L.: The challenge of using future SWOT data for oceanic field reconstruction, J. Atmos. Ocean. Tech., 33, 119–126, https://doi.org/10.1175/JTECH-D-15-0160.1, 2016.
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Le Guillou, F., Metref, S., Cosme, E., Ubelmann, C., Ballarotta, M., Le Sommer, J., and Verron, J.: Mapping Altimetry in the Forthcoming SWOT Era by Back-and-Forth Nudging a One-Layer Quasigeostrophic Model, J. Atmos. Ocean. Tech., 38, 697–710, https://doi.org/10.1175/JTECH-D-20-0104.1, 2021a.
Le Guillou, F., Lahaye, N., Ubelmann, C., Metref, S., Cosme, E., Ponte, A., Le Sommer, J., Blayo, E., and Vidard, A.: Joint estimation of balanced motions and internal tides from future wideswath altimetry, J. Adv. Model. Earth Sy., 13, e2021MS002613, https://doi.org/10.1029/2021MS002613, 2021b.
Le Guillou, F., Gaultier, L., Ballarotta, M., Metref, S., Ubelmann, C., Cosme, E., and Rio, M.-H.: Regional mapping of energetic short mesoscale ocean dynamics from altimetry: performances from real observations, Ocean Sci., 19, 1517–1527, https://doi.org/10.5194/os-19-1517-2023, 2023.
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Co-editor-in-chief
The paper demonstrates how oceanographic parameters, like Sea Level Anomaly, surface currents and vorticity, from remote sensing are improved when SWOT (Surface Water Ocean Topography) data are included.
The paper demonstrates how oceanographic parameters, like Sea Level Anomaly, surface currents...
Short summary
The Surface Water and Ocean Topography (SWOT) mission provides unprecedented swath altimetry data. This study examines SWOT's impact on mapping systems, showing a moderate effect with the current nadir altimetry constellation and a stronger impact with a reduced one. Integrating SWOT with dynamic mapping techniques improves the resolution of satellite-derived products, offering promising solutions for studying and monitoring sea-level variability at finer scales.
The Surface Water and Ocean Topography (SWOT) mission provides unprecedented swath altimetry...
