Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-391-2021
https://doi.org/10.5194/amt-14-391-2021
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

Related authors

Optimal estimation method retrievals of stratospheric ozone profiles from a DIAL
Ghazal Farhani, Robert J. Sica, Sophie Godin-Beekmann, and Alexander Haefele
Atmos. Meas. Tech., 12, 2097–2111, https://doi.org/10.5194/amt-12-2097-2019,https://doi.org/10.5194/amt-12-2097-2019, 2019
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Observations of tall-building wakes using a scanning Doppler lidar
Natalie E. Theeuwes, Janet F. Barlow, Antti Mannisenaho, Denise Hertwig, Ewan O'Connor, and Alan Robins
Atmos. Meas. Tech., 18, 1355–1371, https://doi.org/10.5194/amt-18-1355-2025,https://doi.org/10.5194/amt-18-1355-2025, 2025
Short summary
Mid-Atlantic nocturnal low-level jet characteristics: a machine learning analysis of radar wind profiles
Maurice Roots, John T. Sullivan, and Belay Demoz
Atmos. Meas. Tech., 18, 1269–1282, https://doi.org/10.5194/amt-18-1269-2025,https://doi.org/10.5194/amt-18-1269-2025, 2025
Short summary
Mitigating radome-induced bias in X-band weather radar polarimetric moments using an adaptive discrete Fourier transform algorithm
Padmanabhan Thiruvengadam, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen
Atmos. Meas. Tech., 18, 1185–1191, https://doi.org/10.5194/amt-18-1185-2025,https://doi.org/10.5194/amt-18-1185-2025, 2025
Short summary
GNSS-RO residual ionospheric error (RIE): a new method and assessment
Dong L. Wu, Valery A. Yudin, Kyu-Myong Kim, Mohar Chattopadhyay, Lawrence Coy, Ruth S. Lieberman, C. C. Jude H. Salinas, Jae N. Lee, Jie Gong, and Guiping Liu
Atmos. Meas. Tech., 18, 843–863, https://doi.org/10.5194/amt-18-843-2025,https://doi.org/10.5194/amt-18-843-2025, 2025
Short summary
Benchmarking KDP in rainfall: a quantitative assessment of estimation algorithms using C-band weather radar observations
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025,https://doi.org/10.5194/amt-18-793-2025, 2025
Short summary

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
Download
Short summary
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.
Share