Convective storms are one of the severe weather hazards found during the warm season. Doppler weather radar is the only operational instrument that can frequently sample the detailed structure of convective storm which has a small spatial scale and short lifetime. For the challenging task of short-term convective storm forecasting, 3-D radar images contain information about the processes in convective storm. However, effectively extracting such information from multisource raw data has been problematic due to a lack of methodology and computation limitations. Recent advancements in deep learning techniques and graphics processing units now make it possible. This article investigates the feasibility and performance of an end-to-end deep learning nowcasting method. The nowcasting problem was transformed into a classification problem first, and then, a deep learning method that uses a convolutional neural network was presented to make predictions. On the first layer of CNN, a cross-channel 3D convolution was proposed to fuse 3D raw data. The CNN method eliminates the handcrafted feature engineering, i.e., the process of using domain knowledge of the data to manually design features. Operationally produced historical data of the Beijing-Tianjin-Hebei region in China was used to train the nowcasting system and evaluate its performance; 3737332 samples were collected in the training data set. The experimental results show that the deep learning method improves nowcasting skills compared with traditional machine learning methods.
翻译:多普勒气象雷达是经常抽样调查空间规模小、寿命短的对流风暴的详细结构的唯一操作工具。对于短期对流风暴预报这一具有挑战性的任务,3D雷达图像含有关于对流风暴过程的信息。然而,由于缺乏方法和计算限制,从多源原始数据中有效提取此类信息有问题。最近深层次学习技术和图形处理器的进步使得现在成为可能。本文章调查了目前深层研究方法的可行性和性能。现在,对流的对流风暴问题首先演变成分类问题,然后,提出了使用革命性神经网络的深度学习方法,以作出预测。在CNN的第一层,建议从多源原始数据中进行跨渠道的3D演进,以整合3D原始数据。CNN方法现在消除了手工艺的特征工程,即利用域数据知识来手动设计特征。现在,将现在的对深层学习方法进行实地研究的问题转换成一个分类问题,然后,提出了使用革命性神经网络的深度学习方法来进行预测。现在,用北京37号的实验性学习方法对中国的学习方法进行了实地评估,然后用北京的学习方法对北京的学习结果进行了评估。