Deep learning on three-dimensional multiscale data for next-hour tornado prediction

Predictors are a storm-centered radar image and a proximity sounding from the Rapid Refresh model. Radar images come from the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) and Gridded NEXRAD WSR-88D Radar dataset (GridRad), both of which are multiradar composites.

We train separate CNNs on MYRORSS and GridRad data, present an experiment to optimize the CNN settings, and evaluate the chosen CNNs on independent testing data. Both models achieve an area under the receiver-operating-characteristic curve (AUC) well above 0.9, which is considered to be excellent performance. The GridRad model achieves a critical success index (CSI) of 0.31, and the MYRORSS model achieves a CSI of 0.17.