Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 1
Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-391-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-391-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 1
Research article
 | 
18 Jan 2021
Research article |  | 18 Jan 2021

Classification of lidar measurements using supervised and unsupervised machine learning methods

Ghazal Farhani, Robert J. Sica, and Mark Joseph Daley

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Cited articles

Bishop, C. M.: Pattern recognition and machine learning, Springer-Verlag, New York, 2006. a, b, c
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, 2002. a
Burges, C. J.: A tutorial on support vector machines for pattern recognition, Data Mining Knowledge Discovery, 2, 121–167, 1998. a
Christian, K., Wang, J., Ge, C., Peterson, D., Hyer, E., Yorks, J., and McGill, M.: Radiative Forcing and Stratospheric Warming of Pyrocumulonimbus Smoke Aerosols: First Modeling Results With Multisensor (EPIC, CALIPSO, and CATS) Views from Space, Geophys. Res. Lett., 46, 10061–10071, 2019. a
Doucet, P. J.: First aerosol measurements with the Purple Crow Lidar: lofted particulate matter straddling the stratospheric boundary, Master's thesis, The University of Western Ontario, London, ON, Canada, 2009. a, b
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While it is relatively straightforward to automate the processing of lidar signals, it is difficult to automatically preprocess the measurements to distinguish between good and bad scans. It is easy to train humans to perform the task; however, considering the growing number of measurements, it is a time-consuming, on-going process. We have tested some machine learning algorithms for lidar signal classification and had success with both supervised and unsupervised methods.
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