Research on short-term and impending precipitation forecasting based on deep spatiotemporal network

Abstract

Precipitation forecasting has always been a challenging problem. Currently, meteorological radar echo data are used widely in the field of precipitation forecasting. From the radar echo images, the current precipitation situation can be obtained. Compared with the actual image, however, the radar echo extrapolation image has the disadvantages of echo loss and inaccurate echo tracks, which leads to low accuracy of the precipitation forecast. A more effective network based on the New-RainNet module for precipitation forecasting is proposed. The network is constructed by stacking New-RainNet modules that are established by adding a convolutional block attention module and a flexible switch from Adam to SGD optimization algorithm to the original convolutional long short-term memory network. The short-term and impending precipitation forecast in the next 0 to 60 min was successfully realized on the radar echo dataset of the Hong Kong Observatory and achieves satisfactory results. Experiments show that the network surpasses other methods in both network convergence and prediction accuracy. In addition, the radar echo image generated by the network prediction is also better than other methods in visual quality as it retains the image details and is closer to the real radar echo image than other methods.