SEVIR: AStormEventImageryDatasetforDeep LearningApplicationsinRadarandSatellite Meteorology

Modern deep learning approaches haveshown promising results inmeteorological applications like precipitation nowcasting, synthetic radar generation, front detection and several others. Inorder toeffectively train and validate these complex algorithms, large and diverse datasets containing high-resolution imagery are required. Petabytes of weather data, such as from the Geostationary Environmental SatelliteSystem(GOES)andtheNext-Generation Radar(NEXRAD) system, are available to the public; however, the size and complexity of these datasets isahindrance todeveloping and training deep models.