Coffer, BriceKubacki, MichaelaWen, YixinZhang, TingBarajas, Carlos A.Gobbert, Matthias K.2020-07-162020-07-162020-06-25Brice Coffer et al., Machine Learning with Feature Importance Analysis for Tornado Prediction from Environmental Sounding Data, Proceedings in Applied Mathematics and Mechanics (2020), http://hpcf-files.umbc.edu/research/papers/S22_Barajas_v1.pdfhttp://hdl.handle.net/11603/19167UMBC High Performance Computing FacilityTornadoes pose a forecast challenge to National Weather Service forecasters because of their quick development and potential for life-threatening damage. The use of machine learning in severe weather forecasting has recently garnered interest, with current efforts mainly utilizing ground weather radar observations. In this study, we investigate machine learning techniques to discriminate between nontornadic and tornadic storms solely relying on the Rapid Update Cycle (RUC) sounding data that represent the pre-storm atmospheric conditions. This approach aims to provide for early warnings of tornadic storms, before they form and are detectable by weather radar observations. Feature analysis of a Random Forest machine learning model uncovers that the pressure variable has little impact on the classification process, which is consistent with known key physical attributes of tornado formation, demonstrating the ability of machine learning techniques to provide insight solely based on the data2 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.UMBC High Performance Computing Facility (HPCF)Text