STUNNER: Radar Echo Extrapolation Model Based on Spatiotemporal Fusion Neural Network
Wei Fang, Lin Pang, Victor S. Sheng, Qiguang Wang
Abstract
Radar echo extrapolation based on deep learning is an important method for conducting precipitation nowcasting. Radar echo sequence data have spatio-temporal correlations and non-rigid movements of the radar echo. According to the characteristics of radar data, this study proposes a new spatio-temporal fusion neural network called STUNNER. STUNNER implements a two-stream spatio-temporal fusion strategy to extract and fuse spatial and temporal signals. Specifically, it uses a novel cross-network embedding method to achieve efficient spatio-temporal fusion; the fusion integrates a temporal differencing network and a spatio-temporal trajectory network. The temporal differencing network models the high-order non-stationarity of the radar sequence to learn the motion trend. The spatio-temporal trajectory network optimizes the convolution operator in a spatio-temporal long short-term memory model to extract the transient variations from the radar images. We compare STUNNER with other five models on two public datasets. On the radar echo extrapolation task, STUNNER achieves the best performance.