TAUDiff: Highly efficient kilometer-scale downscaling using generative diffusion models

TL;DR

TAUDiff is a novel diffusion-based downscaling method that efficiently produces high-resolution atmospheric wind fields from coarse climate models, enabling rapid and reliable simulation of extreme weather events.

Contribution

It introduces TAUDiff, combining a deterministic model with a generative diffusion model for efficient, high-fidelity climate variable downscaling at kilometer scales.

Findings

Effective recovery of fine-scale wind features.

Significantly reduced inference times.

Enhanced simulation of extreme weather events.

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. We demonstrate the efficacy of this approach on downscaling atmospheric wind velocity fields obtained from coarse GCM simulations. We then extend TAUDiff for computationally efficient kilometer-scale downscaling of atmospheric wind velocity fields. Owing to low inference times, our approach can ensure quicker simulation of extreme events necessary for estimating associated risks and economic losses.