首个跨区域多变量气象数据合成框架，解决生成结果过平滑问题
SynWeather: Weather Observation Data Synthesis across Multiple Regions and Variables via a General Diffusion Transformer
- 基于扩散Transformer构建概率化合成模型，支持多变量跨区域统一生成
- 在四大区域12类气象变量上实现高分辨率数据生成，覆盖雷达反射率等关键参数
- 适用于气象模拟、极端天气研究及数据增强，尤其适合需要真实分布的下游任务
随着气象仪器的进步，海量观测数据已可获取。现有方法多聚焦单变量、单区域任务，依赖确定性建模，限制了跨变量与跨区域的统一合成，忽略变量间互补性，常导致结果过度平滑。为此，我们提出SynWeather，首个面向多区域多变量气象观测数据统一合成的基准数据集。涵盖美国大陆、欧洲、东亚及热带气旋区四大典型区域，提供复合雷达反射率、小时降水、可见光和微波亮温等关键气象变量的高分辨率观测。同时，我们构建SynWeatherDiff，一种基于扩散Transformer的通用概率化合成模型，以缓解过平滑问题。在SynWeather数据集上的实验表明，该模型在性能上优于各类专用与通用基线模型。
原文摘要 · Abstract (English)
With the advancement of meteorological instruments, abundant data has become available. Current approaches are typically focus on single-variable, single-region tasks and primarily rely on deterministic modeling. This limits unified synthesis across variables and regions, overlooks cross-variable complementarity and often leads to over-smoothed results. To address above challenges, we introduce SynWeather, the first dataset designed for Unified Multi-region and Multi-variable Weather Observation Data Synthesis. SynWeather covers four representative regions: the Continental United States, Europe, East Asia, and Tropical Cyclone regions, as well as provides high-resolution observations of key weather variables, including Composite Radar Reflectivity, Hourly Precipitation, Visible Light, and Microwave Brightness Temperature. In addition, we introduce SynWeatherDiff, a general and probabilistic weather synthesis model built upon the Diffusion Transformer framework to address the over-smoothed problem. Experiments on the SynWeather dataset demonstrate the effectiveness of our network compared with both task-specific and general models.
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