We demonstrate that machine learning (ML) can skillfully classify thunderstorms into three categories: supercell, part of a quasi-linear convective system, or disorganized. These classifications are based on radar data and environmental information obtained through a proximity sounding. We compare the performance of five ML algorithms: logistic regression with the elastic-net penalty, random forests, gradient-boosted forests, and support-vector machines with both a linear and nonlinear kernel. The gradient-boosted forest performs best, with an accuracy of 0.77 ± 0.02 and a Peirce score of 0.58 ± 0.04. The linear support-vector machine performs second best, with values of 0.70 ± 0.02 and 0.55 ± 0.05, respectively. We use two interpretation methods, permutation importance and sequential forward selection, to determine the most important predictors for the ML models. We also use partial-dependence plots to determine how these predictors influence the outcome. A main conclusion is that shape predictors, based on the outline of the storm, appear to be highly important across ML models. The training data, a storm-centered radar scan and modeled proximity sounding, are similar to real-time data. Thus, the models could be used operationally to aid human decision-making by reducing the cognitive load involved in manual storm-mode identification. Also, they could be run on historical data to perform climatological analyses, which could be valuable to both the research and operational communities.
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