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dc.date.accessioned2022-11-09T02:29:13Z
dc.date.available2022-11-09T02:29:13Z
dc.date.issued2022-11-09
dc.identifier.urihttps://hdl.handle.net/1721.1/146228
dc.description.abstractThe most common type of tornado in the Southeast region of the United States, known as a quasi-linear convective system tornado, is historically difficult to warn for, with lead times hovering under 7 minutes and a false alarm rate of over 75 percent. The Intelligent Tornado Prediction Engine (ITORPE) combines meteorological and machine learning expertise from MIT Lincoln Laboratory researches and the Lincoln Laboratory Supercomputing Center to perform extremely large-scale data fusion spanning several years’ worth of radar, satellite, model, and in situ observation platforms, to provide enhanced situational awareness to forecasters using a graphical interface to focus forecasters’ attention on the storms of highest importance.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesThe Bulletin;
dc.rightsAttribution-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nd/3.0/us/*
dc.subjectLLSCen_US
dc.subjectMIT Lincoln Laboratoryen_US
dc.subjectDeep Learningen_US
dc.titleIntelligent Tornado Prediction Engineen_US
dc.typeArticleen_US


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  • LLSC in the News
    News articles about the LLSC and programs that are supported by the LLSC

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