高分辨率雷达数据集，助力机器学习精准预测强对流风暴
A Storm-Centric 250 m NEXRAD Level-II Dataset for High-Resolution ML Nowcasting
- 以风暴为中心裁剪250米分辨率雷达数据，保留原始极坐标结构
- 包含数千个美国本土风暴事件，提供连续时序的雷达回波序列
- 适合气象模型训练，尤其擅长捕捉小尺度风暴结构演变
基于机器学习的降水短临预报依赖于高保真雷达反射率序列来模拟对流风暴的短期演变。然而，现有公开雷达数据集（如SEVIR、HKO-7、GridRad-Severe）分辨率较低（1-2公里），模糊了精确预报所必需的细粒度结构，制约了极端天气预测模型的发展。为此，我们提出Storm250-L2，一个从NEXRAD Level-II和GridRad-Severe数据中提取的风暴中心雷达数据集。通过算法在GridRad-Severe风暴轨迹周围裁剪固定250米分辨率窗口，保持原始极坐标几何结构，并提供每仰角扫描序列及伪复合反射率产品。数据集涵盖美国本土数千个风暴事件，以HDF5张量形式存储，附带丰富上下文元数据和可复现的清单文件。
原文摘要 · Abstract (English)
Machine learning-based precipitation nowcasting relies on high-fidelity radar reflectivity sequences to model the short-term evolution of convective storms. However, the development of models capable of predicting extreme weather has been constrained by the coarse resolution (1-2 km) of existing public radar datasets, such as SEVIR, HKO-7, and GridRad-Severe, which smooth the fine-scale structures essential for accurate forecasting. To address this gap, we introduce Storm250-L2, a storm-centric radar dataset derived from NEXRAD Level-II and GridRad-Severe data. We algorithmically crop a fixed, high-resolution (250 m) window around GridRad-Severe storm tracks, preserve the native polar geometry, and provide temporally consistent sequences of both per-tilt sweeps and a pseudo-composite reflectivity product. The dataset comprises thousands of storm events across the continental United States, packaged in HDF5 tensors with rich context metadata and reproducible manifests.
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