An Energy-Based Generative Adversarial Forecaster for Radar Echo Map Extrapolation
Pengfei Xie, Xutao Li, Xiyang Ji, Xunlai Chen, Yuanzhao Chen, Jia Liu, Yunming Ye
Abstract
Precipitation nowcasting is an important task in weather forecast. The key challenge of the task lies at radar echo map extrapolation. Recent studies show that a convolutional recurrent neural network (ConvRNN) is a promising direction to solve the problem. However, the extrapolation results of the existing ConvRNN methods tend to be blurring and unrealistic. Recent studies show that generative adversarial network (GAN) is a promising tool to address the drawback, while it suffers from the instability for training. In this letter, we build a novel ConvRNN model based on the energy-based GAN for radar echo map extrapolation. The method can alleviate the blurring and unrealistic issues and is more stable. We have conducted experiments on a real-world data set, and the results show that the proposed method outperforms several existing models, including optical flow, convolution gated recurrent unit (ConvGRU), and generative adversarial ConvGRU (GA-ConvGRU).