Machine Learning with Feature Importance Analysis for Tornado Prediction from Environmental Sounding Data
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Date
2020-06-25
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Citation of Original Publication
Brice 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.pdf
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Abstract
Tornadoes 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 data