David Métivier
I am permanent researcher at INRAE Montpellier since 2023. I work in Applied Mathematics and Physics. My latest research interests focus on statistical models e.g. Hidden Markov Models, Deep Learning to tackle environmental, and climate (change) problems. I also work on practical Robust statistics. Previously, I did a PhD and half a postdoc in theoretical physics on mean field dynamics of particles systems, looking at as bifurcations, synchronization, instabilities, partial differential equations, etc. I am a Julia enthusiast, and I am working on a few packages (see my GitHub profile.).
Intervention
Climate change challenges demand assessing the resilience of key sectors, such as energy and agriculture, to future weather conditions.
Stochastic Weather Generators (SWGs) are essential tools for this purpose, enabling efficient sampling of climate statistics—especially extreme events.
Combined with impact models like crop models, SWGs can be used to stress-test and measure system sensitivity to climate variability, particularly under climate change. For example, estimating the frequency of long dry spells is crucial.
The StochasticWeatherGenerators.jl package implements several SWG models, including those based on Hidden Markov Models and GeoStatistics functions.
Its goal is to provide a range of models and tools to facilitate comparison and experimentation.
Thanks to Julia’s powerful ecosystem and capabilities, defining, fitting, and using stochastic weather generators has never been easier!