New ways for dynamical prediction of extreme heat waves: rare event simulations and machine learning with deep neural networks
Freddy Bouchet ENS Lyon
Séminaire dans le cadre de la nouvelle saison des séminaires « AI4Climate »
En ligne et en présentiel
We cope with this lack of data issue using rare event simulations. Using some of the best climate models, we oversample extremely rare events and obtain several hundreds more events than with usual climate runs, at a fixed numerical cost. Coupled with deep neural networks this approach improves drastically the prediction of extreme heat waves.
This shed new light on the fluid mechanics processes which lead to extreme heat waves. We will describe quasi-stationary patterns of turbulent Rossby waves that lead to global teleconnection patterns in connection with heat waves and analyze their dynamics. We stress the relevance of these patterns for recently observed extreme heat waves and the prediction potential of our approach.