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Article

Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

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School of Technology, Reykjavik University, 102 Reykjavik, Iceland
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Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, China
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Icelandic Meteorological Office, 105 Reykjavik, Iceland
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Sustainability Institute and Forum (SIF), Reykjavik University, 102 Reykjavik, Iceland
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Energy Institute, Johannes Kepler University, 4040 Linz, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Simone Lolli
Remote Sens. 2021, 13(13), 2433; https://doi.org/10.3390/rs13132433
Received: 31 May 2021 / Revised: 19 June 2021 / Accepted: 20 June 2021 / Published: 22 June 2021
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. View Full-Text
Keywords: lidar; Iceland; machine learning; aerosols lidar; Iceland; machine learning; aerosols
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MDPI and ACS Style

Yang, S.; Peng, F.; von Löwis, S.; Petersen, G.N.; Finger, D.C. Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland. Remote Sens. 2021, 13, 2433. https://doi.org/10.3390/rs13132433

AMA Style

Yang S, Peng F, von Löwis S, Petersen GN, Finger DC. Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland. Remote Sensing. 2021; 13(13):2433. https://doi.org/10.3390/rs13132433

Chicago/Turabian Style

Yang, Shu, Fengchao Peng, Sibylle von Löwis, Guðrún N. Petersen, and David C. Finger 2021. "Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland" Remote Sensing 13, no. 13: 2433. https://doi.org/10.3390/rs13132433

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