Deep learning-based severe convection extrapolation method and system under multiple scales

The invention provides a multi-scale severe convection extrapolation method and system based on deep learning, and the method comprises the following steps: receiving radar image data, and extracting the implicit state features of the radar image data; carrying out convolution on the implicit state features, inputting a convolution result into a TrajGRU network, and carrying out severe convection extrapolation to obtain a radar map; carrying out second convolution on the radar map and carrying out batch regularization at the same time to obtain extrapolation image data. According to the method, radar image data is trained to obtain an extrapolation image, and the extrapolation image is used for forecasting severe convection weather such as rainstorm, thunderstorm and hail. 本发明提供一种基于深度学习在多尺度下的强对流外推方法与系统，方法包括以下步骤：接收雷达图像数据，提取所述雷达图像数据的隐含状态特征；对所述隐含状态特征进行卷积，并将卷积结果输入TrajGRU网络中，进行强对流外推得到雷达图；将所述雷达图进行第二次卷积并同时进行批正则化获得外推图像数据。该方法对雷达图像数据训练得到外推图像，用于强对流天气如暴雨、雷暴、冰雹等极端天气的预报。