Articles | Volume 12, issue 12
Atmos. Meas. Tech., 12, 6319–6340, 2019

Special issue: TROPOMI on Sentinel-5 Precursor: first year in operation (AMT/ACP...

Atmos. Meas. Tech., 12, 6319–6340, 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 et al.

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,,, 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.,,, 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,,, 2018
Short summary

Related subject area

Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
A Dark Target research aerosol algorithm for MODIS observations over eastern China: increasing coverage while maintaining accuracy at high aerosol loading
Yingxi R. Shi, Robert C. Levy, Leiku Yang, Lorraine A. Remer, Shana Mattoo, and Oleg Dubovik
Atmos. Meas. Tech., 14, 3449–3468,,, 2021
Short summary
Optimal use of the Prede POM sky radiometer for aerosol, water vapor, and ozone retrievals
Rei Kudo, Henri Diémoz, Victor Estellés, Monica Campanelli, Masahiro Momoi, Franco Marenco, Claire L. Ryder, Osamu Ijima, Akihiro Uchiyama, Kouichi Nakashima, Akihiro Yamazaki, Ryoji Nagasawa, Nozomu Ohkawara, and Haruma Ishida
Atmos. Meas. Tech., 14, 3395–3426,,, 2021
Short summary
Analysis of simultaneous aerosol and ocean glint retrieval using multi-angle observations
Kirk Knobelspiesse, Amir Ibrahim, Bryan Franz, Sean Bailey, Robert Levy, Ziauddin Ahmad, Joel Gales, Meng Gao, Michael Garay, Samuel Anderson, and Olga Kalashnikova
Atmos. Meas. Tech., 14, 3233–3252,,, 2021
Short summary
Model-enforced post-process correction of satellite aerosol retrievals
Antti Lipponen, Ville Kolehmainen, Pekka Kolmonen, Antti Kukkurainen, Tero Mielonen, Neus Sabater, Larisa Sogacheva, Timo H. Virtanen, and Antti Arola
Atmos. Meas. Tech., 14, 2981–2992,,, 2021
Short summary
Explicit and consistent aerosol correction for visible wavelength satellite cloud and nitrogen dioxide retrievals based on optical properties from a global aerosol analysis
Alexander Vasilkov, Nickolay Krotkov, Eun-Su Yang, Lok Lamsal, Joanna Joiner, Patricia Castellanos, Zachary Fasnacht, and Robert Spurr
Atmos. Meas. Tech., 14, 2857–2871,,, 2021
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,, 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,, 2017. 
Cherkassky, V. and Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression, Neural Networks, 17, 113–126,, 2004. 
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.