TAUDiff：使用生成式扩散模型实现高效公里尺度降尺度
TAUDiff: Highly efficient kilometer-scale downscaling using generative diffusion models
AI总结：
本文提出 TAUDiff，通过确定性时空模型结合轻量扩散校正恢复精细随机特征，实现高效且动力学一致的大气风速场公里尺度降尺度。
摘要：
基于确定性回归的气候变量降尺度模型常受到谱偏差影响，而扩散模型等生成式模型可缓解该问题。为实现极端天气事件的高效、可靠模拟，获得快速周转、动力学一致性以及准确的时空谱恢复至关重要。我们提出一种高效校正扩散模型 TAUDiff，它将用于平均场降尺度的确定性时空模型与用于恢复精细尺度随机特征的较小生成式扩散模型相结合。我们在对粗分辨率 GCM 模拟得到的大气风速场进行降尺度时验证了该方法的有效性。随后，我们扩展 TAUDiff，以计算高效的方式实现大气风速场的公里尺度降尺度。由于推理时间较短，我们的方法能够更快模拟极端事件，从而为评估相关风险和经济损失提供必要支持。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
