Articles | Volume 16, issue 13
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, Harri Kokkola, Tero Mielonen, Mika Esa Juhani Mononen, Antti Lipponen, Juha Reunanen, Anders Vilhelm Lindfors, Santtu Mikkonen, Kari Erkki Juhani Lehtinen, Natalia Kouremeti, Alkiviadis Bais, Harri Niska, and Antti Arola


Interactive discussion

Status: closed
Status: closed
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
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.
Final-revised paper