A study on fine scale precipitation nowcasting in Beijing-Tianjin-Hebei region based on deep learning

Precipitation nowcasting on fine scale is of great significance to improve the ability of early warning of flood and waterlogging disasters in modern cities. As a new method, deep learning has more advantages in mining the internal characteristics and physical laws of data. In recent years, the application of deep learning in the field of meteorological radar image has achieved preliminary results. In order to improve the effectiveness of nowcasting on fine scale, a deep convolutional neural network-RainNet is used to propose two ways of rolling approach for precipitation nowcasting. Experiments and comparative analysis are carried out in Beijing-Tianjin-Hebei region on 1 km resolution. Compared with the traditional extrapolation based on Tracking Radar Echoes by Correlation (TREC), the results show that the mean absolute error and correlation coefficient of 1 h nowcasting can be improved. The prediction in 10—50 min in thresholds of 1.04 mm/(10 min) and below is better than that of traditional prediction. Temporal and spatial evolution of precipitation extinction process is better described by deep learning compared with that by traditional extrapolation. The rolling approach with two RainNet models combined outperforms one single model in precipitation nowcasting.