  The problem of nowcasting extreme weather events can be addressed by applying
either numerical methods for the solution of dynamic model equations or
data-driven artificial intelligence algorithms. Within this latter framework,
the present paper illustrates how a deep learning method, exploiting videos of
radar reflectivity frames as input, can be used to realize a warning machine
able to sound timely alarms of possible severe thunderstorm events. From a
technical viewpoint, the computational core of this approach is the use of a
value-weighted skill score for both transforming the probabilistic outcomes of
the deep neural network into binary classification and assessing the
forecasting performances. The warning machine has been validated against
weather radar data recorded in the Liguria region, in Italy,
