Tornet

The Tornado Network (TorNet) dataset is a large, high-resolution benchmark dataset developed to support machine learning research in tornado detection and prediction. It comprises over 200,000 radar samples derived from 9 years of full-resolution, polarimetric WSR-88D (NEXRAD) level-II and level-III radar data. Each sample, called a "chip," includes multiple radar variables—such as reflectivity, radial velocity, spectrum width, differential reflectivity, correlation coefficient, and specific differential phase—captured across two elevation angles and four time steps spaced five minutes apart. Rather than converting radar data to Cartesian coordinates, TorNet retains its native polar format, preserving spatial fidelity near the radar site. This level of detail enables the dataset to support a wide range of machine learning techniques, including deep learning models that can learn directly from raw radar imagery.