基于深度神经网络的强对流天气识别算法

短时强降水、大风等强对流天气危害巨大,对其进行自动识别存在相当大的技术困难.提出一种基于深度神经网络的强对流天气智能识别模型,以雷达回波图像和表征回波移动路径的光流图像作为输入,通过神经网络的自学习,寻求雷达图像与是否发生强对流天气之间的函数映射关系;并运用数据集增强、代价函数优化和模型泛化性能优化等技术,解决了训...

A Strong Convective Weather Recognition Algorithm based on Deep Neural Network

Severe convective weather, such as short-time heavy precipitation and strong wind, has great harm, and there are considerable technical difficulties in its automatic identification. An intelligent recognition model of severe convective weather based on deep neural network is proposed, the model takes radar echo image and optical flow image representing echo movement path as input. Through self-learning of neural network, the functional mapping relationship between radar image and whether severe convection weather occurs is sought. The techniques of data set enhancement, cost function optimization and model generalization performance optimization are used to solve the problem of unbalanced training samples and avoid the problem of model training falling into local extremum. Experimental results show that the accuracy of this method is 96%, and the false alarm rate is less than 60%. This method is also suitable for automatic identification of severe weather such as downburst.