###### Abstract  
Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports. lidar; Iceland; machine learning; aerosols +
Footnote †: journal: Remote Sensing  
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be large, and the noise exceeded the threshold in some cases, or non-noise signals could not reach the thresholds, especially in the free troposphere, where the concentration of scatterers is in general low. Therefore, the lidar needs to be calibrated, which is complex and time-consuming. Foremost, the CNR threshold needs to be optimized and validated to identify the noise. Thus, it is ideal to find a way to identify and filter the background noise observed by a lidar, other than the conventional CNR filtering approach, which could perform better, especially when the intensity of the scatterers is low. After the background noise is identified, lidar observations of different target classes, such as aerosols and clouds need to be interpreted. Frequently, lookup tables (LUT) are used to classify lidar measurements [10]. LUTs typically contain a combination of variables measured by the lidar, e.g., the lidar ratio, the backscatter coefficients, the color ratio, the temperature and the relative humidity, as well as their respective thresholds [6; 11; 12; 13]. Some lidar manufacturers, i.e., Leosphere Inc (Vaisala Group), can provide a built-in automatic algorithm to classify the lidar signals [14]. However, based on our experience of using the WindCube lidar from Leosphere, the accuracy of this built-in classification function is rather low. Considering the environmental conditions are varied at different locations, validating this built-in algorithm is also time-consuming and requires high-skill. However, an automatic algorithm that classifies the lidar measurements in real-time, and applies to data from different instruments, is valuable to lidar end users. Machine learning is a widely used method to classify data [15] and has been used on lidar measurements. [PERSON] et al. [16] applied a k-means clustering method to classify cloud and aerosols from CALIPSO satellite-based lidar. [PERSON] et al. [17] used the Random Forest (RF) and Support Vector Machine (SVM) algorithm to discriminate CATS-ISS measured aerosols from clouds. [PERSON] et al. [18] applied both supervised and unsupervised machine learning methods to classify lidar measurements over the UK and identify the anomalies, which were considered as biomass burning aerosols, from other measurements. The machine learning method is considered capable of handling high-dimensional data and map classes with complex characteristics [19].