Turbulence warning based on convolutional neural network by lidar

In order to realize automatic turbulence warning, a novel turbulence warning algorithm based on convolution neural network(CNN) by lidar was proposed. Firstly, the velocity structure function was constructed from the wind speed data obtained by lidar; Then, the eddy dissipation rate was fitted, and then the eddy dissipation rate was constructed as a pixel data set. The data set was input into the CNN model composed of two convolution layers, two fully connected layers, one softmax layer and several activation functions for turbulence identification. The learning rate decreasing method is used to adjust the parameters of the model to train the network. After the network converges, the loss is as low as 3%. The comparative experiment shows that the accuracy of the network reaches 85%. Based on the flight crew report of Zhongchuan airport in 2016, the results show that the hit rate of this method for atmospheric turbulence warning is 80%, the false alarm rate is 13.3%, and the distort alarm is 6.7%. Compared with the Hog-SVM classification method, the hit rate of this method is significantly improved, which proves that the convolution network model has strong generalization ability in turbulence warning, and improves the warning efficiency significantly. It can provide a judgment basis for relevant weather forecasters.