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

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 backscatter...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Shu Yang, Fengchao Peng, Sibylle von Löwis, Guðrún Nína Petersen, David Christian Finger
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2021
Subjects:
Q
Online Access:https://doi.org/10.3390/rs13132433
https://doaj.org/article/8bc38cc027f94b7db06951c2a4458881
Description
Summary: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.