Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow

Abstract

Precipitation nowcasting is critical for preventing damage to human life and the economy. Radar echo tracking methods such as optical flow algorithms have been widely employed for precipitation nowcasting because they can track precipitation motions well. Thus, this method, including the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE), was implemented for operational precipitation nowcasting. However, advection-based methods struggle to predict the nonlinear motions of precipitation fields and dynamic processes, such as the growth and decay of precipitation. This study proposes an enhanced optical flow model using a multi-temporal optical flow field and a conditional generative adversarial network (cGAN). We trained the proposed model using a 3-year radar dataset provided by the Korean Meteorological Administration and performed forecast skill evaluations using both qualitative and quantitative methods. In particular, the model featuring multi-temporal optical flow enhances prediction accuracy for the nonlinear motion of precipitation fields, and the model’s accuracy can be further improved through the use of the cGAN structure. We have verified that these improvements hold for 0–3 h lead times. Based on this performance enhancement, we conclude that the multi-temporal optical flow model with cGAN has a potential role in operational precipitation nowcasting.

Keywords

• precipitation nowcasting
• deep learning approach
• optical flow
• generative adversarial network
• weather radar