TAUDiff: Improving statistical downscaling for extreme-event simulation using generative diffusion models (Papers Track) Spotlight
Rahul Sundar (Verisk, India); Nishant Parashar (Verisk); Antoine Blanchard (Verisk); Boyko Dodov (Verisk)
Abstract
Deterministic regression-based downscaling models for climate variables often suffer from spectral bias, which can be mitigated by generative models like diffusion models. To enable efficient and reliable simulation of extreme weather events, it is crucial to achieve rapid turnaround, dynamical consistency, and accurate spatio-temporal spectral recovery. We propose an efficient correction diffusion model TAUDiff that combines a deterministic spatio-temporal model for mean field downscaling with a smaller generative diffusion model for recovering the fine-scale stochastic features. This approach can not only ensure quicker simulation of extreme events but also reduce overall carbon footprint due to low inference times.