  We propose the use of a stochastic variational frame prediction deep neural
network with a learned prior distribution trained on two-dimensional rain radar
reflectivity maps for precipitation nowcasting with lead times of up to 2 1/2
hours. We present a comparison to a standard convolutional LSTM network and
assess the evolution of the structural similarity index for both methods. Case
studies are presented that illustrate that the novel methodology can yield
meaningful forecasts without excessive blur for the time horizons of interest.
