Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 11, issue 2
Articles | Volume 11, issue 2
https://doi.org/10.5194/ascmo-11-159-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-11-159-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 2
08 Sep 2025
 | 08 Sep 2025

Interpretable seasonal multisite hidden Markov model for stochastic rain generation in France

Emmanuel Gobet, David Métivier, and Sylvie Parey

Viewed

Total article views: 46 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
38 6 2 46 1 0
  • HTML: 38
  • PDF: 6
  • XML: 2
  • Total: 46
  • BibTeX: 1
  • EndNote: 0
Views and downloads (calculated since 08 Sep 2025)
Cumulative views and downloads (calculated since 08 Sep 2025)

Viewed (geographical distribution)

Total article views: 46 (including HTML, PDF, and XML) Thereof 46 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 10 Sep 2025
Download
Short summary
Stochastic weather generators (SWGs) are statistical models used to study climate variability. We design an interpretable multisite SWG for precipitation, capable of learning large-scale weather regimes solely from French observational data. The model reproduces extreme events like droughts and heavy rain and is applied to climate models under historical and Representative Concentration Pathway (RCP) scenarios. This type of model aims to assess large-scale weather risks, such as those impacting energy systems and agriculture.
Read more
Share