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.
Highlights
Two Leosphere WindCube 200S Doppler lidars are in use in Iceland; both were equipped with a depolarization module during this study period
Did the models apply a similar filtering but with a more precise how did the models achieve that? Did the models apply a similar carrier to noise ratio (CNR) filtering but with threshold than the conventional CNR filtering? Table 3 shows the statistics of the data a more precise threshold than the conventional CNR filtering? Table 3 shows the statistics points that are classified as noise by the models from different data sets
We applied machine learning methods to classify the signals retrieved from lidar measurements in Iceland
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The light detection and ranging system (lidar) is an active remote sensing instrument that is widely used for various purposes: from automatic driving [1] and forestry [2] to aviation safety [3,4]. It is mainly used for wind measurements [3,5] and aerosol detection [6]. While conventional meteorological measurements are either continuous in time but at a single height (e.g., measurements in meteorological masts) or give a profile at a certain time only a few times a day (e.g., radiosondes), a lidar provides a continuous profile measurement with a high temporal and spatial resolution at the same time
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