TAUDiff: Improving statistical downscaling for extreme weather events using generative diffusion models

• URL: http://arxiv.org/abs/2412.13627v1
• Date: Wed, 18 Dec 2024 09:05:19 GMT
• Title: TAUDiff: Improving statistical downscaling for extreme weather events using generative diffusion models
• Authors: Rahul Sundar, Nishant Parashar, Antoine Blanchard, Boyko Dodov,
• Abstract summary: It is crucial to achieve rapid turnaround, dynamical consistency, and accurate-temporal recovery. We propose an efficient diffusion model TiffAUD, that combines a deterministic model mean downscaling with a smaller generative diffusion model for recovering the fine-scale features. Our approach can not only ensure quicker simulation of extreme events but also reduce overall carbon footprint due to low inference times.
• Score: 0.0
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• 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. Our approach can not only ensure quicker simulation of extreme events but also reduce overall carbon footprint due to low inference times.

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