  Precipitation nowcasting, which aims to precisely predict the short-term
rainfall intensity of a local region, is gaining increasing attention in the
artificial intelligence community. Existing deep learning-based algorithms use
a single network to process various rainfall intensities together, compromising
the predictive accuracy. Therefore, this paper proposes a novel recurrent
neural network (RNN) based star-bridge network (StarBriNet) for precipitation
nowcasting. The novelty of this work lies in the following three aspects.
First, the proposed network comprises multiple sub-networks to deal with
different rainfall intensities and duration separately, which can significantly
improve the model performance. Second, we propose a star-shaped information
bridge to enhance the information flow across RNN layers. Third, we introduce a
multi-sigmoid loss function to take the precipitation nowcasting criterion into
account. Experimental results demonstrate superior performance for
precipitation nowcasting over existing algorithms, including the
state-of-the-art one, on a natural radar echo dataset.
