Abstract
The local thunderstorms and gale weather occurring frequently has brought huge losses to the agriculture and transportation industries. This paper presents a method of forecasting the local thunderstorms and gale weather, in which a multisource convolution neural network is constructed to extract the features of weather-related data with multiple types from Doppler radar. To improve the discriminative power of features, Center-Loss and Softmax were jointly used as loss function in the training, and then the features obtained are combined with SVM for classification. Furthermore, a comparative experiment of multisource convolution neural network based on CNN-4, ResNet30, ResNet50, and VGG16 is designed, in which the ResNet30 achieves the highest accuracy. The experimental results show that the multisource convolution neural network avoids the limitation of using one type of data and improves the accuracy of forecasting local thunderstorms and gale.
Highlights
Severe convective weather, such as heavy precipitation, hail, thunderstorms and gale, is characterized by small space-time scale, rapid change, and complicated occurrence and development mechanism
Deep learning overcomes the difficulty of handcrafted feature extraction in statistical machine learning methods, and shows prominent advantages in image recognition
These methods based on deep learning only extract the features of single source data, which efficiently utilizes all kinds of data from Doppler radar
Summary
Severe convective weather, such as heavy precipitation, hail, thunderstorms and gale, is characterized by small space-time scale, rapid change, and complicated occurrence and development mechanism. These methods based on deep learning only extract the features of single source data, which efficiently utilizes all kinds of data from Doppler radar. Volution neural network is proposed to forecast local thunderstorms and gale weather, in which the features of some kinds of weather-related data are extracted.
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