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AMT | Articles | Volume 12, issue 12
Atmos. Meas. Tech., 12, 6319–6340, 2019
https://doi.org/10.5194/amt-12-6319-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Atmos. Meas. Tech., 12, 6319–6340, 2019
https://doi.org/10.5194/amt-12-6319-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

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.
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Manuscript under review for AMT
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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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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.
Single scattering albedo (SSA) is critical for reducing uncertainties in radiative forcing...
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