Advances in Deep-Learning-based Precipitation Nowcasting Techniques  

作　　者：ZHENG Qun LIU Qi LAO Ping LU Zhen-ci 郑群;刘奇;劳坪;卢振赐(School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing,University of Science and Technology of China,Hefei 230026 China)

机构地区：[1]School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing,University of Science and Technology of China,Hefei 230026 China

出　　处：《Journal of Tropical Meteorology》2024年第3期337-350,共14页热带气象学报（英文版）

基　　金：National Natural Science Foundation of China(42075075);National Key R&D Program of China(2023YFC3007700);Pre-Research Fund of USTC(YZ2082300006)。

摘　　要：Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than numerical weather models.The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision techniques.Recently,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation nowcasting.Notable progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost.This paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field emerge.Classic models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the second.In particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints.The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.

关 键 词：precipitation nowcasting deep learning neural network classic model large model 

分 类 号：P456.1[天文地球—大气科学及气象学]