GAN-rcLSTM: A Deep Learning Model for Radar Echo Extrapolation

Abstract

The target of radar echo extrapolation is to predict the motion and development of radar echo in the future based on historical radar observation data. For such spatiotemporal prediction problems, a deep learning method based on Long Short-Term Memory (LSTM) networks has been widely used in recent years, although such models generally suffer from weak and blurry prediction. This paper proposes two models called Residual Convolution LSTM (rcLSTM) and Generative Adversarial Networks-rcLSTM (GAN-rcLSTM): The former introduces the residual module, and the latter introduces the discriminator. We use the historical data of 2017 and 2018 in the Jiangsu region as training and test sets. Experiments show that in long sequence forecasts, our model can provide more stable and clear images, while achieving higher CSI scores.

Keywords

• radar extrapolation
• deep learning
• LSTM
• generative adversarial network