Automated multi-classifier recognition of atmospheric turbulent structures obtained by Doppler lidar

We present algorithms and results of automated processing of LiDAR measurements obtained during VEGILOT measuring campaign in Paris in autumn 2014 in order to study horizontal turbulent atmospheric regimes on urban scales. To process images obtained by horizontal atmospheric scanning using Doppler LiDAR, the method is proposed based on texture analysis and classification using supervised machine learning algorithms. The results of the parallel classification by various classifiers were combined using the majority voting strategy. The obtained estimates of accuracy demonstrate the efficiency of the proposed method for solving the problem of remote sensing of regional-scale turbulent patterns in the atmosphere.