Tornado Detection using Deep Neural Networks and Full-Resolution Polarimetric Weather Radar Data

Weather radar is the primary tool used by forecasters to detect and warn for tornadoes in near-real time. While forecasters historically provide excellent warning performance, false alarm rates for tornado warnings remain relatively high. Particularly in cases of widespread convection, and especially with the potential influx of additional data with future phased-array radar systems, “data overload” can become an issue, leading to the desire to alert forecasters to potential storms of interest. This has led to multiple previous tornado detection algorithms in the literature. While these approaches have continuously improved tornado detection capabilities, applications of image-based deep learning (DL) are yet to be extensively explored for the tornado detection problem. In this study, a database of full-resolution, polarimetric, Level-II WSR-88D data 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. A baseline evaluation of the dataset is first made by comparing non-DL methods, showing similar results to previous techniques with their own independent datasets. 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. Given the size of the dataset and its ability to be expanded in the temporal dimension, a preview of upcoming tornado prediction efforts is presented.