Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 9
Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3831-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3831-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 9
Development and technical paper
 | Highlight paper
 | 
12 May 2022
Development and technical paper | Highlight paper |  | 12 May 2022

Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO – v.1

Francine Schevenhoven and Alberto Carrassi

Viewed

Total article views: 3,849 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,253 518 78 3,849 51 44
  • HTML: 3,253
  • PDF: 518
  • XML: 78
  • Total: 3,849
  • BibTeX: 51
  • EndNote: 44
Views and downloads (calculated since 17 Aug 2021)
Cumulative views and downloads (calculated since 17 Aug 2021)

Viewed (geographical distribution)

Total article views: 3,849 (including HTML, PDF, and XML) Thereof 3,562 with geography defined and 287 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
In this study, we present a novel formulation to build a dynamical combination of models, the so-called supermodel, which needs to be trained based on data. Previously, we assumed complete and noise-free observations. Here, we move towards a realistic scenario and develop adaptations to the training methods in order to cope with sparse and noisy observations. The results are very promising and shed light on how to apply the method with state of the art general circulation models.
Read more