PPNet: A more effective method of precipitation prediction

Abstract

Abstract Precipitation nowcasting plays an important role in the early warning of disasters and many other aspects of people's lives. In this study, we address the problem of radar reflectivity image extrapolation, which has great significance for precipitation near‐range forecasting. In recent years, the related achievements of nowcasting indicate that deep learning‐based methods have been far ahead of traditional ones. However, most deep learning methods focus on spatial appearance but cannot characterize the motion information well. To solve this complex problem, in addition to completing pixel‐level predictions by McNet, we introduce FlowNet and optical flow loss into generative adversarial networks (GAN) to express motion information more effectively. The reflectance image changes significantly due to complex meteorological conditions. Only by adding motion features can the law of change be determined. Thus, we can retain more details in the image. To the best of our knowledge, this is the first time FlowNet has been combined with GAN to fulfil the task of precipitation prediction. Extensive experiments on the dataset provided by the Shenzhen Meteorological Bureau demonstrate that our network performs favourably against other state‐of‐the‐art methods, which presents great guiding significance for precipitation nowcasting and possesses broad application prospects.

Keywords

• deep learning methods
• motion information
• precipitation nowcasting
• spatial information