Ocean Science
Ocean Science
OS
Articles | Volume 21, issue 1
Articles | Volume 21, issue 1
https://doi.org/10.5194/os-21-199-2025
© Author(s) 2025. This work is distributed under
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.
Articles | Volume 21, issue 1
Research article
 | 
27 Jan 2025
Research article |  | 27 Jan 2025

Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms

Daniele Ciani, Claudia Fanelli, and Bruno Buongiorno Nardelli

Viewed

Total article views: 1,109 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
685 252 172 1,109 34 32
  • HTML: 685
  • PDF: 252
  • XML: 172
  • Total: 1,109
  • BibTeX: 34
  • EndNote: 32
Views and downloads (calculated since 19 Apr 2024)
Cumulative views and downloads (calculated since 19 Apr 2024)

Viewed (geographical distribution)

Total article views: 1,109 (including HTML, PDF, and XML) Thereof 1,100 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Apr 2025
Download
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. 
Read more
Share
Special issue
Special Issue for the 54th International Liège Colloquium...