Machine Learning with Feature Importance Analysis for Tornado Prediction from Environmental Sounding Data

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dc.contributor.authorCoffer, Brice
dc.contributor.authorKubacki, Michaela
dc.contributor.authorWen, Yixin
dc.contributor.authorZhang, Ting
dc.contributor.authorBarajas, Carlos A.
dc.contributor.authorGobbert, Matthias K.
dc.date.accessioned2020-07-16T16:32:49Z
dc.date.available2020-07-16T16:32:49Z
dc.date.issued2020-06-25
dc.descriptionUMBC High Performance Computing Facilityen_US
dc.description.abstractTornadoes 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 dataen_US
dc.description.sponsorshipThis work is supported in part by the U.S. National Science Foundation under the CyberTraining (OAC–1730250) and MRI (OAC–1726023) programs. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). Co-author Carlos A. Barajas was supported as HPCF RA.en_US
dc.description.urihttp://hpcf-files.umbc.edu/research/papers/S22_Barajas_v1.pdfen_US
dc.format.extent2 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/m286bn-ybzj
dc.identifier.citationBrice 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.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/19167
dc.language.isoen_USen_US
dc.publisherUMBCen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis 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.
dc.subjectUMBC High Performance Computing Facility (HPCF)
dc.titleMachine Learning with Feature Importance Analysis for Tornado Prediction from Environmental Sounding Dataen_US
dc.typeTexten_US

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