  Weather radar is the primary tool used by forecasters to detect and warn for
tornadoes in near-real time. In order to assist forecasters in warning the
public, several algorithms have been developed to automatically detect tornadic
signatures in weather radar observations. Recently, Machine Learning (ML)
algorithms, which learn directly from large amounts of labeled data, have been
shown to be highly effective for this purpose. Since tornadoes are extremely
rare events within the corpus of all available radar observations, the
selection and design of training datasets for ML applications is critical for
the performance, robustness, and ultimate acceptance of ML algorithms. This
study introduces a new benchmark dataset, TorNet to support development of ML
algorithms in tornado detection and prediction. TorNet contains
full-resolution, polarimetric, Level-II WSR-88D data sampled from 10 years of
reported storm events. A number of ML baselines for tornado detection are
developed and compared, including a novel deep learning (DL) architecture
capable of processing raw radar imagery without the need for manual feature
extraction required for existing ML algorithms. Despite not benefiting from
manual feature engineering or other preprocessing, the DL model shows increased
detection performance compared to non-DL and operational baselines. The TorNet
dataset, as well as source code and model weights of the DL baseline trained in
this work, are made freely available.
