Machine learning and artificial intelligence have been revolutionizing weather and climate modeling and data analysis over the past few years. However, it remains unclear how much understanding has been gained from those models, even though they are reaching unprecedented accuracy. Through the study and analysis of carefully chosen latent spaces, I will demonstrate how we can get new understanding on the terrestrial water and carbon cycle as well as on atmospheric processes, specifically on convection. Those latent spaces can also be used to better characterize complex stochastic processes such as turbulence and combined with data assimilation in order to achieve improved performance in those models

These seminars are being made possible through the support of the CFM-ENS Chair « Modèles et Sciences des Données ».

The organizers: Giulio Biroli, Alex Cayco Gajic, Bruno Loureiro, Stéphane Mallat, Gabriel Peyré.