Abstract
Various weather modelling problems (e.g., weather forecasting, optimizing turbine placements, etc.) require ample access to high-resolution, highly accurate wind data. Acquiring such high-resolution wind data, however, remains a challenging and expensive endeavour. Traditional reconstruction approaches are typically either cost-effective or accurate, but not both. Deep learning methods, including diffusion models, have been proposed to resolve this trade-off by leveraging advances in natural image super-resolution. Wind data, however, is distinct from natural images, and wind super-resolvers often use upwards of 10 input channels, significantly more than the usual 3-channel RGB inputs in natural images. To better leverage a large number of conditioning variables in diffusion models, we present a generalization of classifier-free guidance (CFG) to multiple conditioning inputs. Our novel composite classifier-free guidance (CCFG) can be dropped into any pre-trained diffusion model trained with standard CFG dropout. We demonstrate that CCFG outputs are higher-fidelity than those from CFG on wind super-resolution tasks. We present WindDM, a diffusion model trained for industrial-scale wind dynamics reconstruction and leveraging CCFG. WindDM achieves state-of-the-art reconstruction quality among deep learning models and costs up to $1000\times$ less than classical methods.
Abstract (translated)
各种天气建模问题（如天气预报、优化涡轮机布局等）需要大量高分辨率和高度准确的风数据。然而，获取这种高质量的风数据仍然是一个既具挑战性又昂贵的过程。传统的方法通常要么成本效益高但不够准确，要么非常准确但成本高昂。深度学习方法，包括扩散模型（diffusion models），已被提出以利用自然图像超分辨领域的进步来解决这一问题。然而，风数据与自然图像存在本质区别，风数据的超级分辨率通常需要多达10个输入通道，远超过普通3通道RGB自然图像输入。 为了更好地在扩散模型中使用大量的条件变量，我们提出了一个针对多条件输入的无分类器指导（classifier-free guidance, CFG）方法的一般化版本。我们的新型复合无分类器指导（composite classifier-free guidance, CCFG）可以无缝集成到任何预先训练的标准CFG丢弃策略下的扩散模型中。我们在风数据超分辨率任务上展示了，与标准CFG相比，CCFG生成的结果具有更高的保真度。 我们介绍了WindDM，这是一个专门用于工业规模风动态重建的扩散模型，并采用了我们的复合无分类器指导技术。在深度学习模型中，WindDM实现了目前最先进的重建质量，并且其成本相较于传统方法低至千分之一。
URL
https://arxiv.org/abs/2512.13729
