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Ryan Lagerquist, Amy McGovern, Cameron R. Homeyer, David John Gagne II, and Travis Smith

et al. (2014 , 2018) developed an operational algorithm called ProbSevere, which uses naïve Bayes to forecast any severe weather for a given storm. Their predictors are derived from radar, satellite, and lightning data, as well as NSE variables from the Rapid Refresh model. ProbSevere has run in the HWT for several years, receiving favorable feedback from forecasters. It has improved upon the median lead time of NWS tornado and severe-thunderstorm warnings but at the cost of a decrease in CSI

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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

machine learning has had a long and fruitful application to meteorology (see Haupt et al. 2008 ; McGovern et al. 2017 and references therein), the state of the science of DL applied to meteorology is limited but rapidly growing. A large portion of work in this field to date applies to short-term forecasting for renewable energy ( Diaz et al. 2015 ; Wan et al. 2016 ; Sogabe et al. 2016 ; Hu et al. 2016 ). Other major research includes feature identification for long-term climate analysis ( Kurth

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