Ocean Science
Ocean Science
OS
Articles | Volume 20, issue 3
Articles | Volume 20, issue 3
https://doi.org/10.5194/os-20-689-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-20-689-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 3
Research article
 | 
27 May 2024
Research article |  | 27 May 2024

Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model

Carolina Amadio, Anna Teruzzi, Gloria Pietropolli, Luca Manzoni, Gianluca Coidessa, and Gianpiero Cossarini

Viewed

Total article views: 5,673 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,055 1,451 167 5,673 200 277
  • HTML: 4,055
  • PDF: 1,451
  • XML: 167
  • Total: 5,673
  • BibTeX: 200
  • EndNote: 277
Views and downloads (calculated since 19 Jul 2023)
Cumulative views and downloads (calculated since 19 Jul 2023)

Viewed (geographical distribution)

Total article views: 5,673 (including HTML, PDF, and XML) Thereof 5,553 with geography defined and 120 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 May 2026
Download
Short summary
Forecasting of marine biogeochemistry can be improved via the assimilation of observations. Floating buoys provide multivariate information about the status of the ocean interior. Information on the ocean interior can be expanded/augmented by machine learning. In this work, we show the enhanced impact of assimilating new in situ variables (oxygen) and reconstructed variables (nitrate) in the operational forecast system (MedBFM) model of the Mediterranean Sea.
Read more
Share
Special issue
Special Issue for the 54th International Liège Colloquium...