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Machine Learning with Feature Importance Analysis for Tornado Prediction from Environmental Sounding Data
Author(s) -
Coffer Brice,
Kubacki Michaela,
Wen Yixin,
Zhang Ting,
Barajas Carlos A.,
Gobbert Matthias K.
Publication year - 2021
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.202000112
Subject(s) - tornado , depth sounding , storm , machine learning , severe weather , radar , computer science , meteorology , weather forecasting , feature (linguistics) , artificial intelligence , random forest , feature engineering , environmental science , geography , deep learning , cartography , linguistics , philosophy , telecommunications
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.