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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

, DL performs best with at least tens of thousands of training samples, and model performance scales logarithmically with the training sample size ( Sun et al. 2017 ). Thus we have sought out the largest available dataset of TC observations in the 37- and 89-GHz bands. This is available in the Microwave Imagery from NRL TC (MINT) collection, which covers global conical scanner observations from 1987 to 2012. As described in Cossuth et al. (2013) , the dataset includes brightness temperatures from

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

last decade ( Brooks and Correia 2018 ). During this time, the amount of data available to forecasters has exploded—including dual-polarization radar observations, high-resolution satellite observations, and forecasts from convection-allowing models (CAM). However, none of these datasets explicitly resolves tornadoes, so they must still be translated into useful information by forecasters, which can lead to cognitive overload ( Wilson et al. 2017 ). This problem can be alleviated by explicit

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