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.

中文翻译：

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使用机器学习方法从冰岛的多普勒激光雷达测量中识别粒子类型

多普勒激光雷达在全球范围内用于风监测，最近还用于检测气溶胶。对从激光雷达测量中检索到的激光雷达信号进行分类的自动算法对用户非常有用。在这项研究中，我们探索了机器学习在使用冰岛数据对来自多普勒激光雷达的反向散射信号进行分类方面的价值。我们将有监督和无监督的机器学习算法与传统的激光雷达数据处理方法相结合，训练了两个模型来过滤噪声信号，并将多普勒激光雷达观测分为不同的类别，包括云、气溶胶和雨。结果表明，噪声识别和气溶胶和云分类具有很高的准确性。然而，降水检测被低估了。该方法在不同天气条件下（包括 2019 年夏季的三场沙尘暴）的两个仪器的数据集上进行了测试。我们的结果表明，该方法可以对激光雷达测量进行高效、准确和实时的分类。因此，我们得出结论，机器学习可以为激光雷达数据终端用户（例如航空安全运营商）提供新的机会，以监测机场附近的灰尘。