A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars

In recent years, the number of weather-related disasters significantly increases across the world. As a typical example, short-range extreme precipitation can cause severe flooding and other secondary disasters, which therefore requires accurate prediction of extent and intensity of precipitation in a relatively short period of time. Based on the echo extrapolation of networked weather radars (i.e., the Internet of Radars), different solutions have been presented ranging from traditional optical-flow methods to recent deep neural networks. However, these existing networks focus on local features of echo variations to model the dynamics of holistic radar echo motion, so it often suffers from inaccurate extrapolation of the radar echo motion trend, trajectory, and intensity. To address the problem, this paper introduces the self-attention mechanism and an extra memory that saves global spatiotemporal feature into the original Spatiotemporal LSTM (ST-LSTM) to form a self-attention Integrated ST-LSTM recurrent unit (SAST-LSTM), capturing both spatial and temporal global features of radar echo motion. And several these units are stacked to build the radar echo extrapolation network SAST-Net. Comparative experiments show that the proposed model has better performance on different real world radar echo datasets over other recent methods