Abstract:

[Objective] This paper aims to construct a short-term and imminent precipitation forecasting model based on deep learning and explores the application of domestic accelerators in the field of meteorology, with the goal of improving the accuracy of meteorological disaster prevention and mitigation, and providing more reliable meteorological support for urban development and transportation. [Literature Scope] This paper focuses on the development of short-term precipitation forecasting technology, especially the application of deep learning-based radar echo extrapolation methods in the field of meteorology. [Context] Traditional numerical weather prediction has uncertainty in precipitation forecasting within the first two hours, and radar echo extrapolation methods based on physical models, such as optical flow and cross-correlation methods, have limited prediction accuracy and stability under complex meteorological conditions. [Methods] In this study, we employ specialized datasets for short-term intense precipitation of an AI application developed by China Meteorological Administration. Based on TrajGRU, a deep learning network architecture, we establish the short-term and imminent precipitation forecasting model TrajCast. Employing data parallelism and mixed-precision training techniques, the model training is implemented on domestic accelerators. [Results] Experiment results demonstrate that the model outperforms traditional optical flow methods in precipitation forecasting, achieving a 4.1-fold acceleration ratio in model training on a domestic accelerator. [Limitations] The model has certain requirements for data quality and computing resources in practical applications, and its performance under extreme weather conditions still needs further verification. [Conclusions] This study provides new methods and technical support for short-term precipitation forecasting, promotes the application of domestic accelerators in the field of meteorology. The model has been demonstrated and applied in meteorological departments in Hunan, Zhejiang, and Hubei provinces, providing intelligent algorithm support for high-resolution and high-frequency meteorological intelligent forecasting products.

Key words: short-term and imminent precipitation forecast, deep learning, domestic accelerator, radar echo extrapolation, mixed-precision training