TAUDiff: Highly efficient kilometer-scale downscaling using generative diffusion models
This work addresses the need for rapid and accurate simulation of extreme weather events to estimate risks and economic losses, representing an incremental improvement in generative downscaling methods.
The paper tackled the problem of spectral bias in deterministic regression-based downscaling models for climate variables by proposing TAUDiff, an efficient correction diffusion model that combines deterministic and generative components, achieving low inference times for kilometer-scale downscaling of atmospheric wind velocity fields.
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.