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.
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.
the Creative Commons Attribution 4.0 License.
08 Sep 2025
| 08 Sep 2025
Interpretable seasonal multisite hidden Markov model for stochastic rain generation in France
Emmanuel Gobet
CMAP, CNRS, École Polytechnique, Institut Polytechnique de Paris, Route de Saclay, Palaiseau, France
CMAP, CNRS, École Polytechnique, Institut Polytechnique de Paris, Route de Saclay, Palaiseau, France
MISTEA, Université de Montpellier, INRAE, Institut Agro, Montpellier, France
Sylvie Parey
EDF R&D, 6 quai Watier, 78401 Chatou CEDEX, France
Related authors
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Alex C. Ruane, Charlotte L. Pascoe, Claas Teichmann, David J. Brayshaw, Carlo Buontempo, Ibrahima Diouf, Jesus Fernandez, Paula L. M. Gonzalez, Birgit Hassler, Vanessa Hernaman, Ulas Im, Doroteaciro Iovino, Martin Juckes, Iréne L. Lake, Timothy Lam, Xiaomao Lin, Jiafu Mao, Negin Nazarian, Sylvie Parey, Indrani Roy, Wan-Ling Tseng, Briony Turner, Andrew Wiebe, Lei Zhao, and Damaris Zurell
EGUsphere, https://doi.org/10.5194/egusphere-2025-3408, https://doi.org/10.5194/egusphere-2025-3408, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This paper describes how the Coupled Model Intercomparison Project organized its 7th phase (CMIP7) to encourage the production of Earth system model outputs relevant for impacts and adaptation. Community engagement identified 13 opportunities for application across human and natural systems, 60 variable groups and 539 unique variables. We also show how simulations can more efficiently meet applications needs by targeting appropriate resolution, time slices, experiments and variable groups.
Yasser Hamdi, Ivan D. Haigh, Sylvie Parey, and Thomas Wahl
Nat. Hazards Earth Syst. Sci., 21, 1461–1465, https://doi.org/10.5194/nhess-21-1461-2021, https://doi.org/10.5194/nhess-21-1461-2021, 2021
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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.
Stochastic weather generators (SWGs) are statistical models used to study climate...
