In this study, a new database of full-resolution, polarimetric WSR-88D data spanning 2013-2022 is combined with the National Centers for Environmental Information (NCEI) tornado database, National Weather Service (NWS) warnings, and a plethora of metadata to train and evaluate a deep neural network (DNN) for tornado detection. The approach of using full-resolution Level-II polarimetric data is novel in this research space, and results have shown significant promise for improvement over previous methods. A baseline of performance is first evaluated with the improved dataset by comparing non-DL methods such as regression, random forests, and support vector machines, showing similar results to previous techniques. The dataset is then used to train a DNN using 12 modalities, including the lowest two tilts of horizontal reflectivity factor, radial velocity, spectrum width, differential reflectivity, co-polar correlation coefficient, and specific differential phase. Comparisons to the non-DL baselines are made and discussed, as well as the critical applicability to future tornado prediction methods using the same dataset. A specific focus is given to applications of eXplainable AI (XAI) in order to determine the specific areas of interest to the DNN. Using this approach, we can determine exactly which signatures across the 12 modalities are of most importance to the model, resulting in the ability to verify performance. Finally, the applicability of XAI to a future tornado prediction algorithm and discovery of tornadic precursors combined across multiple modalities is discussed.

