用扩散模型生成高精度风暴演化视频，提升气象预测数据质量
Geospatial Diffusion-based Evolution Synthesis (GeoDES) for Storm-Centered Weather Augmentation
- 基于地理空间扩散模型，专注生成风暴动态演化过程
- 风暴涡度误差降低52%，异常相关系数提高8%（北大西洋测试集）
- 适合气象建模、灾害模拟与数据增强研究者使用
尽管基于机器学习的气象模型前景广阔，但在预测大尺度天气系统（如气旋风暴）的精细结构方面仍存在困难。区域模型受限于固定地理边界内的历史数据量，而全球模型计算成本高且分辨率不足，难以捕捉细粒度风暴动态。为此，我们提出地理空间扩散演化合成模型（GeoDES），一种专为图像到视频生成设计的定制化扩散模型。通过聚焦于风暴结构的动态演化，GeoDES可生成物理一致、高保真的天气事件，适用于压力测试预报模型并扩展气象数据集。评估显示，相比现有方法，GeoDES在关键指标上表现更优：在北大西洋测试集中，峰值涡度误差降低52%，异常相关系数提升8%。
原文摘要 · Abstract (English)
While machine learning-based weather models hold significant promise, they struggle to predict the detailed structure of large-scale weather systems such as cyclonic storms. Regional models are constrained by limited historical records within fixed geographic boundaries, while global models are computationally expensive and often operate at resolutions too coarse to capture fine-grained storm dynamics. To bridge this gap, we introduce the Geospatial Diffusion-based Evolution Synthesis (GeoDES) model, a custom image-to-video diffusion model. By focusing generation strictly on the evolving storm structure, GeoDES synthesizes physically consistent, high-fidelity weather events suitable for stress-testing forecast models and expanding meteorological datasets. Evaluations demonstrate that GeoDES outperforms prior methods on key metrics, achieving $52\%$ lower Peak Vorticity Error and $8\%$ higher Anomaly Correlation Coefficient than the next strongest methods on the North Atlantic test set.
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