Articles | Volume 12, issue 12
https://doi.org/10.5194/amt-12-6319-2019
https://doi.org/10.5194/amt-12-6319-2019
Research article
 | 
02 Dec 2019
Research article |  | 02 Dec 2019

The role of aerosol layer height in quantifying aerosol absorption from ultraviolet satellite observations

Jiyunting Sun, Pepijn Veefkind, Swadhin Nanda, Peter van Velthoven, and Pieternel Levelt

Related authors

A first comparison of TROPOMI aerosol layer height (ALH) to CALIOP data
Swadhin Nanda, Martin de Graaf, J. Pepijn Veefkind, Maarten Sneep, Mark ter Linden, Jiyunting Sun, and Pieternel F. Levelt
Atmos. Meas. Tech., 13, 3043–3059, https://doi.org/10.5194/amt-13-3043-2020,https://doi.org/10.5194/amt-13-3043-2020, 2020
Short summary
Defining aerosol layer height for UVAI interpretation using aerosol vertical distributions characterized by MERRA-2
Jiyunting Sun, J. Pepijn Veefkind, Peter van Velthoven, L. Gijsbert Tilstra, Julien Chimot, Swadhin Nanda, and Pieternel F. Levelt
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-39,https://doi.org/10.5194/acp-2020-39, 2020
Revised manuscript not accepted
Short summary
Quantifying the single-scattering albedo for the January 2017 Chile wildfires from simulations of the OMI absorbing aerosol index
Jiyunting Sun, J. Pepijn Veefkind, Peter van Velthoven, and Pieternel F. Levelt
Atmos. Meas. Tech., 11, 5261–5277, https://doi.org/10.5194/amt-11-5261-2018,https://doi.org/10.5194/amt-11-5261-2018, 2018
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Retrieval algorithm for aerosol effective height from the Geostationary Environment Monitoring Spectrometer (GEMS)
Sang Seo Park, Jhoon Kim, Yeseul Cho, Hanlim Lee, Junsung Park, Dong-Won Lee, Won-Jin Lee, and Deok-Rae Kim
Atmos. Meas. Tech., 18, 2241–2259, https://doi.org/10.5194/amt-18-2241-2025,https://doi.org/10.5194/amt-18-2241-2025, 2025
Short summary
ACDL/DQ-1 calibration algorithms – Part 1: Nighttime 532 nm polarization and the high-spectral-resolution channel
Fanqian Meng, Junwu Tang, Guangyao Dai, Wenrui Long, Kangwen Sun, Zhiyu Zhang, Xiaoquan Song, Jiqiao Liu, Weibiao Chen, and Songhua Wu
Atmos. Meas. Tech., 18, 2021–2039, https://doi.org/10.5194/amt-18-2021-2025,https://doi.org/10.5194/amt-18-2021-2025, 2025
Short summary
Aerosol composition retrieval from a combination of three different spaceborne instruments: information content analysis
Ulrike Stöffelmair, Thomas Popp, Marco Vountas, and Hartmut Bösch
Atmos. Meas. Tech., 18, 2005–2020, https://doi.org/10.5194/amt-18-2005-2025,https://doi.org/10.5194/amt-18-2005-2025, 2025
Short summary
Compact dual-wavelength depolarization lidar for aerosol characterization over the subtropical North Atlantic
Yenny González, María F. Sánchez-Barrero, Ioana Popovici, África Barreto, Stephane Victori, Ellsworth J. Welton, Rosa D. García, Pablo G. Sicilia, Fernando A. Almansa, Carlos Torres, and Philippe Goloub
Atmos. Meas. Tech., 18, 1885–1908, https://doi.org/10.5194/amt-18-1885-2025,https://doi.org/10.5194/amt-18-1885-2025, 2025
Short summary
Towards gridded nighttime aerosol optical thickness retrievals using VIIRS day–night band observations over regions with artificial light sources
Jianglong Zhang, Jeffrey S. Reid, Blake T. Sorenson, Steven D. Miller, Miguel O. Román, Zhuosen Wang, Robert J. D. Spurr, Shawn Jaker, Thomas F. Eck, and Juli I. Rubin
Atmos. Meas. Tech., 18, 1787–1810, https://doi.org/10.5194/amt-18-1787-2025,https://doi.org/10.5194/amt-18-1787-2025, 2025
Short summary

Cited articles

Ahn, C., Torres, O., and Jethva, H.: Assessment of OMI near-UV aerosol optical depth over land, J. Geophys. Res.-Atmospheres, 119, 2457–2473, 2014. 
Apituley, A., Pedergnana, M., Sneep, M., Veefkind, J. P., Loyola, D. and Wang, P.: Level 2 Product User Manual KNMI level 2 support products, KNMI, the Netherlands, 118 pp., 2017. 
Bergstrom, R. W., Pilewskie, P., Russell, P. B., Redemann, J., Bond, T. C., Quinn, P. K., and Sierau, B.: Spectral absorption properties of atmospheric aerosols, Atmos. Chem. Phys., 7, 5937–5943, https://doi.org/10.5194/acp-7-5937-2007, 2007. 
Buchard, V., Randles, C. A., da Silva, A. M., Darmenov, A., Colarco, P. R., Govindaraju, R., Ferrare, R., Hair, J., Beyersdorf, A. J., Ziemba, L. D., and Yu, H.: The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies, J. Climate, 30, 6851–6872, https://doi.org/10.1175/JCLI-D-16-0613.1, 2017. 
Cherkassky, V. and Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks, 17, 113–126, https://doi.org/10.1016/S0893-6080(03)00169-2, 2004. 
Download
Short summary
Single scattering albedo (SSA) is critical for reducing uncertainties in radiative forcing assessment. This paper presents two methods to retrieve SSA from satellite observations of the near-UV absorbing aerosol index (UVAI). The first is physically based radiative transfer simulations; the second is a statistically based machine learning algorithm. The result of the latter is encouraging. Both methods show that the ALH is necessary to quantitatively interpret aerosol absorption from UVAI.
Share