Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4755-2021
https://doi.org/10.5194/amt-14-4755-2021
Research article
 | 
01 Jul 2021
Research article |  | 01 Jul 2021

Retrieval of aerosol microphysical properties from atmospheric lidar sounding: an investigation using synthetic measurements and data from the ACEPOL campaign

William G. K. McLean, Guangliang Fu, Sharon P. Burton, and Otto P. Hasekamp

Related authors

Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach
Zihao Yuan, Guangliang Fu, Bastiaan van Diedenhoven, Hai Xiang Lin, Jan Willem Erisman, and Otto P. Hasekamp
Atmos. Meas. Tech., 17, 2595–2610, https://doi.org/10.5194/amt-17-2595-2024,https://doi.org/10.5194/amt-17-2595-2024, 2024
Short summary
Retrieving UV–Vis spectral single-scattering albedo of absorbing aerosols above clouds from synergy of ORACLES airborne and A-train sensors
Hiren T. Jethva, Omar Torres, Richard A. Ferrare, Sharon P. Burton, Anthony L. Cook, David B. Harper, Chris A. Hostetler, Jens Redemann, Vinay Kayetha, Samuel LeBlanc, Kristina Pistone, Logan Mitchell, and Connor J. Flynn
Atmos. Meas. Tech., 17, 2335–2366, https://doi.org/10.5194/amt-17-2335-2024,https://doi.org/10.5194/amt-17-2335-2024, 2024
Short summary
Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols
Otto Hasekamp, Pavel Litvinov, Guangliang Fu, Cheng Chen, and Oleg Dubovik
Atmos. Meas. Tech., 17, 1497–1525, https://doi.org/10.5194/amt-17-1497-2024,https://doi.org/10.5194/amt-17-1497-2024, 2024
Short summary
Aerosol retrieval over snow using the RemoTAP algorithm
Zihan Zhang, Guangliang Fu, and Otto Hasekamp
Atmos. Meas. Tech., 16, 6051–6063, https://doi.org/10.5194/amt-16-6051-2023,https://doi.org/10.5194/amt-16-6051-2023, 2023
Short summary
Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models
Meng Gao, Bryan A. Franz, Peng-Wang Zhai, Kirk Knobelspiesse, Andrew M. Sayer, Xiaoguang Xu, J. Vanderlei Martins, Brian Cairns, Patricia Castellanos, Guangliang Fu, Neranga Hannadige, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Frederick Patt, Anin Puthukkudy, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 5863–5881, https://doi.org/10.5194/amt-16-5863-2023,https://doi.org/10.5194/amt-16-5863-2023, 2023
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach
Zihao Yuan, Guangliang Fu, Bastiaan van Diedenhoven, Hai Xiang Lin, Jan Willem Erisman, and Otto P. Hasekamp
Atmos. Meas. Tech., 17, 2595–2610, https://doi.org/10.5194/amt-17-2595-2024,https://doi.org/10.5194/amt-17-2595-2024, 2024
Short summary
Retrieving UV–Vis spectral single-scattering albedo of absorbing aerosols above clouds from synergy of ORACLES airborne and A-train sensors
Hiren T. Jethva, Omar Torres, Richard A. Ferrare, Sharon P. Burton, Anthony L. Cook, David B. Harper, Chris A. Hostetler, Jens Redemann, Vinay Kayetha, Samuel LeBlanc, Kristina Pistone, Logan Mitchell, and Connor J. Flynn
Atmos. Meas. Tech., 17, 2335–2366, https://doi.org/10.5194/amt-17-2335-2024,https://doi.org/10.5194/amt-17-2335-2024, 2024
Short summary
Characterization of stratospheric particle size distribution uncertainties using SAGE II and SAGE III/ISS extinction spectra
Travis N. Knepp, Mahesh Kovilakam, Larry Thomason, and Stephen J. Miller
Atmos. Meas. Tech., 17, 2025–2054, https://doi.org/10.5194/amt-17-2025-2024,https://doi.org/10.5194/amt-17-2025-2024, 2024
Short summary
Parameterizing spectral surface reflectance relationships for the Dark Target aerosol algorithm applied to a geostationary imager
Mijin Kim, Robert C. Levy, Lorraine A. Remer, Shana Mattoo, and Pawan Gupta
Atmos. Meas. Tech., 17, 1913–1939, https://doi.org/10.5194/amt-17-1913-2024,https://doi.org/10.5194/amt-17-1913-2024, 2024
Short summary
Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1
Guangyao Dai, Songhua Wu, Wenrui Long, Jiqiao Liu, Yuan Xie, Kangwen Sun, Fanqian Meng, Xiaoquan Song, Zhongwei Huang, and Weibiao Chen
Atmos. Meas. Tech., 17, 1879–1890, https://doi.org/10.5194/amt-17-1879-2024,https://doi.org/10.5194/amt-17-1879-2024, 2024
Short summary

Cited articles

Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Andreae, M. O., Jones, C. D., and Cox, P. M.: Strong present-day aerosol cooling implies a hot future, Nature, 435, 1187–1190, https://doi.org/10.1038/nature03671, 2005. a
Böckmann, C.: Hybrid regularization method for the ill-posed inversion of multiwavelength lidar data in the retrieval of aerosol size distributions, Appl. Optics, 40, 1329–1342, https://doi.org/10.1364/AO.40.001329, 2001. a
Böckmann, C., Mironova, I., Müller, D., Schneidenbach, L., and Nessler, R.: Microphysical aerosol parameters from multiwavelength lidar, J. Opt. Soc. Am. A, 22, 518–528, https://doi.org/10.1364/JOSAA.22.000518, 2005. a
Burton, S. P., Ferrare, R. A., Hostetler, C. A., Hair, J. W., Rogers, R. R., Obland, M. D., Butler, C. F., Cook, A. L., Harper, D. B., and Froyd, K. D.: Aerosol classification using airborne High Spectral Resolution Lidar measurements – methodology and examples, Atmos. Meas. Tech., 5, 73–98, https://doi.org/10.5194/amt-5-73-2012, 2012. a, b, c
Download
Short summary
In this study, we present results from aerosol retrievals using both synthetic and real lidar datasets, including measurements from the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, a combined initiative between NASA and SRON (the Netherlands Institute for Space Research). Aerosol microphysical retrievals were performed using the High Spectral Resolution Lidar-2 (HSRL-2) setup, alongside several others, with the ACEPOL retrievals also compared to polarimeter retrievals.