用生成模型高效实现千米级风速场降尺度，提升极端天气模拟速度与精度。
TAUDiff: Highly efficient kilometer-scale downscaling using generative diffusion models
- 先用确定性模型处理整体趋势，再用小规模生成模型恢复细节特征。
- 在粗分辨率气候模型基础上实现千米级风速降尺度，推理速度快。
- 适合需要快速模拟极端天气风险的气象与气候评估场景。
基于确定性回归的降尺度模型常受谱偏差影响，而生成模型如扩散模型可缓解此问题。为实现极端天气事件的高效可靠模拟，需兼顾快速响应、动力一致性及时空谱的准确恢复。本文提出高效修正型扩散模型TAUDiff，结合确定性时空模型进行均值场降尺度，辅以小型生成扩散模型恢复细粒度随机特征。该方法在粗分辨率全球气候模型（GCM）输出的风速场降尺度任务中表现优异，并扩展至计算高效的千米级风速场降尺度。得益于低推理时间，本方法可显著加快极端事件模拟速度，有助于风险与经济损失的及时评估。
原文摘要 · Abstract (English)
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.
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