IMPORTANCE SAMPLING
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.
Description
Importance sampling is a method of statistical physics that nudges a system (like climate) towards extremes of a given observable (like high temperatures over a region). The idea is to make ensembles of simulations of the system for a short time, delete the simulations that do not increase the chosen observable and replicate those which do increase the observable. This is similar to particular filtering, with weights on temperature.
This procedure has been applied to the IPSL climate model to simulate extremes of temperatures over France.
This type of method is a broad case of “ensemble boosting”. It can be used to design “worst case scenarios”. In the present version, this method is designed for a global climate model (GCM) and requires supercomputing facilities.
Potential Users:
Long-term investment in energy sector: French RTE.
Agriculture & agronomy: French INRAE
Collaborations with research institutes with access to supercomputing facilities is necessary.
Guide:
PhD thesis of Robin Noyelle (https://cnrs.hal.science/tel-04632646/).
Availability:
Code is available upon request once the paper (submitted to J. Clim) is accepted. Such codes are meant to run on supercomputing facilities.
Use Cases
Robin Noyelle. Statistical and dynamical aspects of extreme heatwaves in the mid-latitudes. Ocean, Atmosphere. Université Paris-Saclay, 2024. English. ⟨NNT : 2024UPASJ013⟩. ⟨tel-04632646v2⟩
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003469.
Tools
In the aftermath of an extreme weather event, the event is studied more and more often by an operational attribution service. Two examples of such operational attribution services are used within XAIDA. These operational services generally answer slightly different questions and are complementary.
ClimaMeter is an international consortium designed to