中国气象局城市气象研究所杨璐及其团队成员，提出了京津冀地区预测雷暴阵风的深度学习方法。2024年1月6日出版的《大气科学进展》杂志发表了这项成果。

本文基于中国城市气象研究所(IUM)的多源网格化产品数据，提出了一种雷暴阵风TransU-net(TG-TransUnet)的深度学习方法，来预测华北地区的雷暴天气，预测时间为1-6小时。为了确定雷暴天气的具体范围，研究人员结合了三个气象变量：雷达反射率因子、闪电位置和自动气象站(AWSs)的1 小时最大瞬时风速，并得到了雷暴天气的合理地面真实值。

在基于卷积神经网络和变压器的TG-TransUnet架构下，研究人员将预测问题转化为深度学习中的图像—图像问题。2021-2023年期间，丰富的多时段网格综合预测系统的分析和预测数据随后被用作训练、验证和测试数据集。最后，研究人员将TG-TransUnet的性能与其他方法进行了比较。结果表明，TG-TransUnet在1-6小时的预报效果最好。目前，中国城市气象研究所正在使用该模式来对华北地区的雷暴天气预报提供支持。

据了解，雷暴天气是华北暖季常见的强对流天气形式，正确预报雷暴天气具有重要意义。目前，雷暴天气的预报主要基于传统的主观方法，无法实现基于多观测点的高分辨率、高频率网格化预报。

附：英文原文

Title: A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

Author: Yunqing LIU, Lu Yang, Chen Mingxuan, Linye Song, Lei Han, Jingfeng XU

Issue&Volume: 2024-01-06

Abstract: Thunderstorm gusts are a common form of severe convective weather in the warm season in North China, and it is of great importance to correctly forecast them. At present, the forecasting of thunderstorm gusts is mainly based on traditional subjective methods, which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources. In this paper, we propose a deep learning method called Thunderstorm Gusts TransU-net (TG-TransUnet) to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology (IUM) with a lead time of 1 to 6 h. To determine the specific range of thunderstorm gusts, we combine three meteorological variables: radar reflectivity factor, lightning location, and 1-h maximum instantaneous wind speed from automatic weather stations (AWSs), and obtain a reasonable ground truth of thunderstorm gusts. Then, we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture, which is based on convolutional neural networks and a transformer. The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–2023 are then used as training, validation, and testing datasets. Finally, the performance of TG-TransUnet is compared with other methods. The results show that TG-TransUnet has the best prediction results at 1–6 h. The IUM is currently using this model to support thunderstorm gusts forecasting in North China.

DOI: 10.1007/s00376-023-3255-7

Source: http://www.iapjournals.ac.cn/aas/en/article/doi/10.1007/s00376-023-3255-7viewType=HTML