Articles | Volume 21, issue 1
https://doi.org/10.5194/os-21-199-2025
© Author(s) 2025. This work is distributed under
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the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-21-199-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jan 2025
Research article |
| 27 Jan 2025
| 27 Jan 2025
Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms
Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 00133, Rome, Italy
Claudia Fanelli
Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 80133, Naples, Italy
Bruno Buongiorno Nardelli
Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR), 80133, Naples, Italy
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Sarah Asdar, Daniele Ciani, and Bruno Buongiorno Nardelli
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Estimating 3D currents is crucial for the understanding of ocean dynamics, and a precise knowledge of ocean circulation is essential to ensure a sustainable ocean. In this context, a new high-resolution (1 / 10°) data-driven dataset of 3D ocean currents has been developed within the European Space Agency World Ocean Circulation project, providing 10 years (2010–2019) of horizontal and vertical quasi-geostrophic currents at daily resolution over the North Atlantic Ocean, down to 1500 m depth.
Andrea Pisano, Daniele Ciani, Salvatore Marullo, Rosalia Santoleri, and Bruno Buongiorno Nardelli
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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.
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Short summary
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.
Ocean surface currents are routinely derived from satellite observations of the sea level,...
