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

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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. 
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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.