Articles | Volume 16, issue 13
Atmos. Chem. Phys., 16, 8181–8191, 2016
https://doi.org/10.5194/acp-16-8181-2016
Atmos. Chem. Phys., 16, 8181–8191, 2016
https://doi.org/10.5194/acp-16-8181-2016

Research article 07 Jul 2016

Research article | 07 Jul 2016

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

Jani Huttunen et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Jani Huttunen on behalf of the Authors (02 May 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (19 May 2016) by Bernhard Mayer
RR by Anonymous Referee #1 (20 May 2016)
ED: Reconsider after minor revisions (Editor review) (01 Jun 2016) by Bernhard Mayer
AR by Jani Huttunen on behalf of the Authors (09 Jun 2016)  Author's response    Manuscript
ED: Publish as is (14 Jun 2016) by Bernhard Mayer
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Short summary
For a good estimate of the current forcing by anthropogenic aerosols, knowledge in past is needed. One option to lengthen time series is to retrieve aerosol optical depth from solar radiation measurements. We have evaluated several methods for this task. Most of the methods produce aerosol optical depth estimates with a good accuracy. However, machine learning methods seem to be the most applicable not to produce any systematic biases, since they do not need constrain the aerosol properties.
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