A New Machine Learning-Based Tornado Detection Algorithm for the WSR-88D Network

Using single-radar products from the WSR-88D network, the New Tornado Detection Algorithm (NTDA) utilizes a random forest machine learning technique for automatically identifying potential tornadoes to improve the performance of the current operational Tornado Detection Algorithm (TDA). The random forest is trained on tornadic and non-tornadic events (non-tornadic includes severe wind and hail greater than 58 mph and 1 inch in diameter, respectively) from 2013 to 2016, totaling almost 10,000 individual points. The radar products include both base and derived products, where both dual-pol and velocity-derived products were generated using the linear least-squares derivative (LLSD) method (Smith and Elmore 2004, Mahalik et al. 2019). These include the total gradients of radial velocity, velocity spectrum width, differential phase, and correlation coefficient, as well as azimuthal and radial (divergent) shear of radial velocity.