STOCHASTIC WEATHER GENERATION
A computationally light tool to simulate temperatures for worse case heat extremes, at city up to country level.
Description
Stochastic weather generators (SWG) are emulators of climate/meteorological variables (like temperature or precipitation). They replace the equations of physics by a random process (usually a Markov chain with latent states) with suitable statistical and physical properties. IPSL has devised a SWG based on analogs of circulation (circulation is the latent state) to simulate temperature or precipitation. This SWG can be run in an Importance Sampling setting for hot or cold temperatures. It has been used to simulate worst cases heatwaves in Paris, or worst case cold spells in France. The main advantage of such a tool is its computing light weight, compared to running a full climate model.
Potential Users: Urban planning, adaptation to climate change.
Guide: An overview and application of the tool is provided in the paper:
Cadiou, C. and Yiou, P.: Simulating record-shattering cold winters of the beginning of the 21st century in France, Weather Clim. Dynam., 6, 1–15, https://doi.org/10.5194/wcd-6-1-2025, 2025.
Availability: Code available on github: https://doi.org/10.5281/zenodo.10726791
Use Cases
- Paris summer heatwave before 2050 (https://doi.org/10.1038/s41612-023-00500-5)
- European extreme cold spells (https://doi.org/10.5194/wcd-5-943-2024, https://wcd.copernicus.org/articles/6/1/2025)
Reference: Cadiou, C. and Yiou, P.: Simulating record-shattering cold winters of the beginning of the 21st century in France, Weather Clim. Dynam., 6, 1–15, https://doi.org/10.5194/wcd-6-1-2025, 2025.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003469.
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Stochastic Weather Generation is a computationally light tool to simulate temperatures for worse case heat extremes, at city up to country level.
Importance sampling is a way to preferentially select out of a range of model simulations the ones that will lead to extremes of the metric of interest in an early stage of simulation, thus increasing computing efficiency for the cases of interest.