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. While it is relatively straightforward to automate the processing of lidar signals, it is moredifficult to choose periods of “good” measurements to process. Groups use various ad hocprocedures involving either very simple (e.g. signal-to-noise ratio) or more complex procedures(e.g. Wing et al., 2018) to perform a task that is easy to train humans to perform but is time-consuming. Here, we use machine learning techniques to train the machine to sort the measurementsbefore processing. The presented method is generic and can be applied to most lidars. We test thetechniques using measurements from the Purple Crow Lidar (PCL) system located in London,Canada. The PCL has over 200 000 raw profiles in Rayleigh and Raman channels available forclassification. We classify raw (level-0) lidar measurements as “clear” sky profiles with stronglidar returns, “bad” profiles, and profiles which are significantly influenced by clouds oraerosol loads. We examined different supervised machine learning algorithms including the randomforest, the support vector machine, and the gradient boosting trees, all of which can successfullyclassify profiles. The algorithms were trained using about 1500 profiles for each PCL channel,selected randomly from different nights of measurements in different years. The success rate of identification for all the channels is above 95 %. We also used the t-distributed stochastic embedding (t-SNE) method, which is an unsupervised algorithm, to cluster our lidar profiles. Because the t-SNE is a data-driven method in which no labelling of the training set is needed, it is an attractive algorithm to find anomalies in lidar profiles. The method has been tested on several nights of measurements from the PCL measurements. The t-SNE can successfullycluster the PCL data profiles into meaningful categories. To demonstrate the use of the technique,we have used the algorithm to identify stratospheric aerosol layers due to wildfires.

Classification of lidar measurements using supervised and unsupervised machine learning methods