AI 中文总结
研究针对机器学习天气模型预测风暴结构难题，提出基于地理空间扩散的演化合成（GeoDES）模型，该模型能合成高保真天气事件，经评估在关键指标上优于先前方法，可用于测试预测模型和扩展气象数据集。
AI 中文摘要
虽然基于机器学习的天气模型前景广阔，但在预测气旋风暴等大规模天气系统的详细结构时面临困难。区域模型受限于固定地理边界内有限的历史记录，全球模型计算成本高且分辨率粗糙。为此，我们引入了基于地理空间扩散的演化合成（GeoDES）模型，这是一个定制的图像到视频扩散模型。通过严格聚焦于不断演变的风暴结构，GeoDES合成了适合压力测试预测模型和扩展气象数据集的物理上一致、高保真的天气事件。评估表明，在关键指标上，GeoDES优于先前方法，在北大西洋测试集上，其峰值涡度误差比次优方法低52%，异常相关系数高8%。
英文摘要
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.
Comments31 pages, 11 figures
