A Method of Weather Radar Echo Extrapolation Based on Convolutional Neural Networks

摘要

Weather radar echo extrapolation techniques possess wide application prospects in short-term forecasting (i.e., nowcasting). Traditional methods of radar echo extrapolation have difficulty obtaining long limitation period data and lack the utilization rate of radar. To solve this problem, this paper proposes a method of weather radar echo extrapolation based on convolutional neural networks (CNNs). To create a strong correlation among contiguous weather radar echo images from traditional CNNs, this method present a new CNN model: Recurrent Dynamic CNNs (RDCNN). RDCNN consists of a recurrent dynamic sub-network and a probability prediction layer, which constructs a cyclic structure in the convolution layer, improving the ability of RDCNN to process time-related images. Nanjing, Hangzhuo and Xiamen experimented with radar data, and compared with traditional methods, our method achieved higher accuracy of extrapolation and extended the limitation period effectively, meeting the requirements for application.