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layer downsamples feature maps to half resolution, thus halving the spatial dimensions. Other convolutional and pooling layers perform similar operations. Feature maps from the last pooling layer are flattened into a length-6,400 vector (5 × 5 × 256 = 6,400), which is transformed by the three dense layers into vectors of length 404, then 20, and then 1. The sigmoid activation function of the final dense layer forces the output (tornadogenesis probability) to the range [0, 1]. During training
layer downsamples feature maps to half resolution, thus halving the spatial dimensions. Other convolutional and pooling layers perform similar operations. Feature maps from the last pooling layer are flattened into a length-6,400 vector (5 × 5 × 256 = 6,400), which is transformed by the three dense layers into vectors of length 404, then 20, and then 1. The sigmoid activation function of the final dense layer forces the output (tornadogenesis probability) to the range [0, 1]. During training
lead times. CAMs typically have 1–4-km horizontal grid spacing, which allows them to explicitly resolve some thunderstorms but not individual hazards such as tornadoes. 1 However, CAMs do resolve midlevel and sometimes low-level mesocyclones, which are necessary precursors for supercell tornadogenesis ( Davies-Jones et al. 2001 ; Markowski and Richardson 2009 , 2014 ). Yussouf et al. (2015) and Wheatley et al. (2015) ran 3-km CAM ensembles for several tornado outbreaks, at lead times up to 1
lead times. CAMs typically have 1–4-km horizontal grid spacing, which allows them to explicitly resolve some thunderstorms but not individual hazards such as tornadoes. 1 However, CAMs do resolve midlevel and sometimes low-level mesocyclones, which are necessary precursors for supercell tornadogenesis ( Davies-Jones et al. 2001 ; Markowski and Richardson 2009 , 2014 ). Yussouf et al. (2015) and Wheatley et al. (2015) ran 3-km CAM ensembles for several tornado outbreaks, at lead times up to 1
