Application Of Convolution Neural Network In Weather Nowcasting

In recent years,with the acceleration of society informatization,the demand for weather forecast has been gradually improved.Strong convective weather,due to its sudden strong destructive,has been paid more attention by the meteorological department.Short-term and imminent prediction,as a means to prevent strong convective weather,has important research significance.However,the short-term and imminent prediction is mostly based on the optical flow method of the radar echo,and the optical flow method has some limitations that optical flow estimation step is separated from the radar echo extrapolation step,the determination of the parameters becomes more difficult.With the rapid development of deep learning,there is more and more application of deep learning in various fields.In this paper,the depth learning method is used to study the precipitation aspect of short-term and impending forecast.Short term prediction of precipitation is essentially a prediction of future radar echo from a series of radar echo sequence,can be seen as a spatiotemporal sequence forecasting problem.This paper studies and summarizes the commonly used neural network based on the reference of Conv LSTM(Convolutional LSTM)structure is proposed which combines convolution neural network(Convolution Neural Network,CNN)and GRU(Gated Recurrent Unit)Conv GRU model(Convolutional GRU).This model has faster training speed and smaller memory requirement than Conv LSTM structure,because the structure of GRU is simpler than LSTM,but it has little difference in effect.Another work of this paper is to improve the convolution layer based on VGGNet(Visual Geometry Group Net).Instead of large convolution kernel,multiple convolution kernel combination is used to reduce the number of parameters and improve the ability of feature extraction.The model fully exerts the advantages of convolutional neural network and GRU,that is,the spatial feature extraction ability of convolution structure and GRU's ability to memorizing time sequence problems.Finally,the experimental results of the model and the optical flow method are compared to verify the applicability of the model in the precipitation nowcasting.