Articles | Volume 18, issue 24
https://doi.org/10.5194/amt-18-7735-2025
https://doi.org/10.5194/amt-18-7735-2025
Research article
 | 
19 Dec 2025
Research article |  | 19 Dec 2025

Particulate matter concentrations derived from airborne high spectral resolution lidar measurements using machine learning regression

Richard Ferrare, Johnathan Hair, Taylor Shingler, Chris Hostetler, Amin Nehrir, Marta Fenn, Amy Jo Scarino, Sharon Burton, Marian Clayton, James Collins, Laura Judd, James Crawford, Katherine Travis, Travis Toth, Pablo Saide, Jose Luis Jimenez, Pedro Campuzano-Jost, Guy Symonds, Richard Moore, Luke Ziemba, Michael Shook, Glenn Diskin, Joshua P. DiGangi, Ryan Bennett, Chia-Hsiang Ho, Lim-Seok Chang, Adisak Aiampisanuvong, and Ittipol Pawarmart

Data sets

STAQS JSC GV High Spectral Resolution Lidar-2 Data NASA/LARC/SD/ASDC https://doi.org/10.5067/ASDC/SUBORBITAL/STAQS/DATA001/GV/AircraftRemoteSensing/HSRL2_1

ASIA-AQ LaRC G-III High Spectral Resolution Lidar-2 Data NASA/LARC/SD/ASDC https://doi.org/10.5067/ASDC/SUBORBITAL/ASIA-AQ/DATA001/G3/AircraftRemoteSensing/HSRL2_1

ASIA-AQ Analysis Data NASA/LARC/SD/ASDC https://doi.org/10.5067/SUBORBITAL/ASIA-AQ/DATA001/DC8/Analysis_1

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Short summary
We present a new method to retrieve atmospheric particulate matter concentrations using only airborne High Spectral Resolution Lidar measurements in machine learning algorithms. Retrieved concentrations agree well with surface measurements. These concentrations and our estimates of the particle mass extinction efficiency are also consistent with those retrieved from airborne in situ measurements. This methodology can also be applied to the Atmosphere Lidar on the EarthCARE satellite.
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