Publications (4)
A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models
Max Defez, Filippo Quarenghi, Mathieu Vrac +2
Deep-learning video super-resolution has progressed rapidly, but climate applications typically super-resolve (increase resolution) either space or time, and joint spatiotemporal m…
A Temporal Stochastic Bias Correction using a Machine Learning Attention model
Omer Nivron, Damon J. Wischik, Mathieu Vrac +2
Climate models are biased with respect to real-world observations. They usually need to be adjusted before being used in impact studies. The suite of statistical methods that enabl…
Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?
Mikel N. Legasa, Antoine Doury, Achille Gellens +4
Emulators provide a cost-effective alternative to regional climate models (RCMs) by capturing their dynamical downscaling function. They link large-scale predictors simulated by gl…
Detecting spatial patterns with the cumulant function. Part II: An application to El Nino
Alberto Bernacchia, Philippe Naveau, Mathieu Vrac +1
The spatial coherence of a measured variable (e.g. temperature or pressure) is often studied to determine the regions where this variable varies the most or to find teleconnections…