Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 8
Articles | Volume 16, issue 8
https://doi.org/10.5194/gmd-16-2119-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2119-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 8
Development and technical paper
 | 
19 Apr 2023
Development and technical paper |  | 19 Apr 2023

4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry

Maxime Beauchamp, Quentin Febvre, Hugo Georgenthum, and Ronan Fablet

Viewed

Total article views: 1,913 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,299 561 53 1,913 54 43
  • HTML: 1,299
  • PDF: 561
  • XML: 53
  • Total: 1,913
  • BibTeX: 54
  • EndNote: 43
Views and downloads (calculated since 15 Nov 2022)
Cumulative views and downloads (calculated since 15 Nov 2022)

Viewed (geographical distribution)

Total article views: 1,913 (including HTML, PDF, and XML) Thereof 1,855 with geography defined and 58 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 May 2024
Download
Short summary
4DVarNet is a learning-based method based on traditional data assimilation (DA). This new class of algorithms can be used to provide efficient reconstructions of a dynamical system based on single observations. We provide a 4DVarNet application to sea surface height reconstructions based on nadir and future Surface Water and Ocean and Topography data. It outperforms other methods, from optimal interpolation to sophisticated DA algorithms. This work is part of on-going AI Chair Oceanix projects.
Read more