用小波扩散模型将降水图分辨率提升10倍，速度更快更真实。
Efficient Kilometer-Scale Precipitation Downscaling with Conditional Wavelet Diffusion
- 在小波域建模降水结构，聚焦高频系数生成细节。
- 实现1公里分辨率，推理速度比像素模型快9倍。
- 适合需要高精度与高效计算的气象与水文研究者。
精准的水文建模与极端天气分析需要千米级分辨率的降水数据，远高于标准全球产品（如IMERG）提供的10公里尺度。为此，我们提出小波扩散模型（WDM），一种生成式框架，可实现10倍空间超分辨率（下采样至1公里），并相比基于像素的扩散模型获得9倍的推理加速。WDM是一种条件扩散模型，直接在小波域中学习来自MRMS雷达数据的降水复杂结构。通过聚焦高频小波系数，该模型生成高度逼真且细节丰富的1公里降水场。这种小波基方法在视觉效果上优于像素空间模型，且具有更少伪影和更高的采样效率。结果表明，WDM为地球科学超分辨率面临的准确性与速度双重挑战提供了稳健解决方案，推动了更可靠的水文预报发展。
原文摘要 · Abstract (English)
Effective hydrological modeling and extreme weather analysis demand precipitation data at a kilometer-scale resolution, which is significantly finer than the 10 km scale offered by standard global products like IMERG. To address this, we propose the Wavelet Diffusion Model (WDM), a generative framework that achieves 10x spatial super-resolution (downscaling to 1 km) and delivers a 9x inference speedup over pixel-based diffusion models. WDM is a conditional diffusion model that learns the learns the complex structure of precipitation from MRMS radar data directly in the wavelet domain. By focusing on high-frequency wavelet coefficients, it generates exceptionally realistic and detailed 1-km precipitation fields. This wavelet-based approach produces visually superior results with fewer artifacts than pixel-space models, and delivers a significant gains in sampling efficiency. Our results demonstrate that WDM provides a robust solution to the dual challenges of accuracy and speed in geoscience super-resolution, paving the way for more reliable hydrological forecasts.
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