Improved Precipitation Nowcasting Through a Deep Learning Model Based on Three-Dimensional Cloud Structures

Precipitation nowcasting pertains to the localized forecasting of rainfall over a brief time horizon, characterized by precise estimates of both coverage and intensity. This capability holds particular significance in various societal applications, including agriculture, aviation safety, and transportation. However, since traditional methods mainly by extrapolating radar echo in 2-D space, cannot accurately and sufficiently represent the spatiotemporal state of clouds in the vertical direction, the accuracy of precipitation nowcasting using weather radar has reached a bottleneck. A new deep learning precipitation nowcasting model called 3dCloudNet is designed and evaluated in this study. The 3dCloudNet incorporates historical 3-D radar echo sequences obtained from weather radar data to improve the accuracy and reliability of precipitation nowcasting. By capturing both horizontal and vertical motion patterns of clouds at various altitude levels, this model demonstrates an enhanced capability in detecting and distinguishing regions prone to severe convective weather events. The experimental results show that the model better captures the cloud’s motion patterns and trends, and therefore has a noteworthy ability to detect and distinguish areas that may lead to severe convective weather. This study provides a step toward further improving the accuracy of precipitation nowcasting.