{"ID":2855451,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16031","arxiv_id":"2510.16031","title":"A Storm-Centric 250 m NEXRAD Level-II Dataset for High-Resolution ML Nowcasting","abstract":"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.","short_abstract":"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,...","url_abs":"https://arxiv.org/abs/2510.16031","url_pdf":"https://arxiv.org/pdf/2510.16031v1","authors":"[\"Andy Shi\"]","published":"2025-10-15T23:11:00Z","proceeding":"physics.ao-ph","tasks":"[\"physics.ao-ph\",\"cs.LG\"]","methods":"[]","has_code":false}