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-11-203-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-11-203-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 2
09 Oct 2025
 | 09 Oct 2025

A spatio-temporal weather generator for the temperature over France

Caroline Cognot, Liliane Bel, David Métivier, and Sylvie Parey

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Cited articles

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Allard, D., Emery, X., Lacaux, C., and Lantuéjoul, C.: Simulating space-time random fields with nonseparable Gneiting-type covariance functions, Statistics and Computing, 30, 1479–1495, https://doi.org/10.1007/s11222-020-09956-4, 2020. a, b
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Weather generators efficiently create realistic weather data based on historical records. This study introduces a daily temperature generator for large regions, separating deterministic factors (trends, seasonality) from random variations modeled using space-time interactions. Validated on French weather station data, it replicates observed patterns, including heatwaves. It offers a practical solution for generating realistic weather data, for applications such as climate impact assessments.
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