用扩散模型提升中短期风速预测精度，支持任意分辨率下采样和误差修正。
DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts using Diffusion Models
- 基于无分类器引导扩散模型，直接生成不同分辨率与预报时效的风速数据。
- 在第3周预报上优于基线模型，实现高精度连续下采样与偏差校正。
- 无需重新训练即可适配不同网格和预报时长，适合能源领域实时应用。
可再生能源高度依赖局地与大尺度天气状况。精准的次季节至季节（S2S）预报——超过两周、最长可达两个月——能为能源行业带来显著的社会经济效益。本研究提出DiffScale，一种基于扩散模型并采用无分类器引导的风速预报下采样方法，用于提升地表风速的次季节预报精度。该模型通过超分辨率技术对欧洲中期天气预报中心（ECMWF）的粗分辨率风速预测进行连续下采样，目标是逼近ERA5再分析数据的细分辨率。借助气象先验作为生成过程的引导，模型以条件概率视角直接估计不同空间分辨率和预报时效下的目标预测分布，无需自回归或序列建模，具备高效灵活的优势。实验表明，该方法在第3周预报上显著优于基线模型。其创新之处在于可泛化至任意缩放因子，无需重训即可适应多种网格分辨率与预报时效，同时纠正模型偏差，是提升S2S风速预测的通用工具。
原文摘要 · Abstract (English)
Renewable resources are strongly dependent on local and large-scale weather situations. Skillful subseasonal to seasonal (S2S) forecasts -- beyond two weeks and up to two months -- can offer significant socioeconomic advantages to the energy sector. This study aims to enhance wind speed predictions using a diffusion model with classifier-free guidance to downscale S2S forecasts of surface wind speed. We propose DiffScale, a diffusion model that super-resolves spatial information for continuous downscaling factors and lead times. Leveraging weather priors as guidance for the generative process of diffusion models, we adopt the perspective of conditional probabilities on sampling super-resolved S2S forecasts. We aim to directly estimate the density associated with the target S2S forecasts at different spatial resolutions and lead times without auto-regression or sequence prediction, resulting in an efficient and flexible model. Synthetic experiments were designed to super-resolve wind speed S2S forecasts from the European Center for Medium-Range Weather Forecast (ECMWF) from a coarse resolution to a finer resolution of ERA5 reanalysis data, which serves as a high-resolution target. The innovative aspect of DiffScale lies in its flexibility to downscale arbitrary scaling factors, enabling it to generalize across various grid resolutions and lead times -without retraining the model- while correcting model errors, making it a versatile tool for improving S2S wind speed forecasts. We achieve a significant improvement in prediction quality, outperforming baselines up to week 3.
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