DiffScale：使用扩散模型对次季节风速预测进行连续降尺度和偏差校正
DiffScale: Continuous Downscaling and Bias Correction of Subseasonal Wind Speed Forecasts using Diffusion Models
AI总结：
提出DiffScale扩散模型，利用天气先验和无分类器引导对次季节风速预测进行连续降尺度与偏差校正，无需重训即可泛化多分辨率并显著提升至第3周的预测质量。
摘要：
可再生资源强烈依赖于局地和大尺度天气状况。精准的次季节到季节（S2S）预测——超过两周并长达两个月——能为能源行业带来显著的社会经济效益。本研究旨在利用具有无分类器引导的扩散模型对地表风速的S2S预测进行降尺度，从而增强风速预测。我们提出了DiffScale，这是一种能够为连续降尺度因子和预报时效超分辨率空间信息的扩散模型。利用天气先验作为扩散模型生成过程的引导，我们采用条件概率的视角来对超分辨率的S2S预测进行采样。我们旨在直接估计不同空间分辨率和预报时效下与目标S2S预测相关的密度，而无需自回归或序列预测，从而得到一个高效且灵活的模型。我们设计了合成实验，将来自欧洲中期天气预报中心（ECMWF）的风速S2S预测从粗分辨率超分辨率至ERA5再分析数据的较细分辨率，后者作为高分辨率目标。DiffScale的创新之处在于其能够降尺度任意缩放因子的灵活性，使其能够在无需重新训练模型的情况下，泛化到各种网格分辨率和预报时效，同时校正模型误差，这使其成为改进S2S风速预测的通用工具。我们在预测质量上取得了显著提升，在第3周之前的表现均优于基线。
英文摘要：
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.
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。
