Generative Diffusion-based Downscaling for Climate

arXiv:2404.17752v1 Announce Type: cross
Abstract: Downscaling, or super-resolution, provides decision-makers with detailed, high-resolution information about the potential risks and impacts of climate change, based on climate model output. Machine learning algorithms are proving themselves to be efficient and accurate approaches to downscaling. Here, we show how a generative, diffusion-based approach to downscaling gives accurate downscaled results. We focus on an idealised setting where we recover ERA5 at $0.25\degree$~resolution from coarse grained version at $2\degree$~resolution. The diffusion-based method provides superior accuracy …

abstract algorithms arxiv change climate climate change cs.lg decision diffusion generative impacts information machine machine learning machine learning algorithms makers physics.ao-ph resolution risks show type