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.

Application of Advection-Diffusion Equation for Nonlinearly Evolving Precipitation Field

“…Due to the challenges in understanding the nonlinear evolutionary patterns of precipitation, models based on Deep Learning methods, which are free from relying on physics, have recently been developed by training data from weather radar, satellites, and surface observations (e.g., Shi et al 2015Shi et al , 2017Agrawal et al 2019;Ayzel et al 2020;Choi et al 2021;Ha & Lee 2023a;Ha & Lee 2023b;Kim & Hong 2022;Ko et al 2022;Ravuri et al 2021;Sønderby et al 2020). The forecasting performance 60 of these data-driven models is better than that of models based on advection and diffusion.…”