Improvements in deep learning-based precipitation nowcasting using major atmospheric factors with radar rain rate

Abstract

Recently, deep learning-based precipitation nowcasting has been investigated and its usefulness has been recognized. However, existing approaches have treated precipitation nowcasting as a spatiotemporal sequence prediction problem and have mainly used only radar images. Radar images show the distribution of water or ice droplets, but are limited in providing information about the dynamic or thermodynamic processes involved in the development of the rainfall system. In this study, we introduced a deep neural network that can utilize atmospheric factors that play major roles in the development of rainfall systems over the Korean Peninsula, including divergence at 925 hPa and total column water vapor, in addition to radar images. We also proposed a loss function based on mean categorical scores (e.g., critical success index and false alarm ratio) to minimize the performance error. In a quantitative evaluation of 1- to 6-hour forecast results, our deep learning models outperformed the conventional approach based on radar echo extrapolation and produced higher equitable threat scores (ETSs) for moderate to severe rain (i.e., 5, 10, and 20 mm h−1 thresholds). By applying the proposed loss function and using the divergence at 925 hPa as an additional input, the deep-learning model not only obtained the highest ETS values, but also revealed its potential to predict new developments of some heavy rainfall systems. The relationship between the presented deep learning-based precipitation nowcasting and the distribution features of the additional atmospheric factors is discussed further in the case study.