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

Precipitation nowcasting using a stochastic variational frame predictor with learned prior distribution