Articles | Volume 16, issue 13
https://doi.org/10.5194/acp-16-8181-2016
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, 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

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Subject: Radiation | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
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Cited articles

Ahmad, I., Mielonen, T., Grosvenor, D., Portin, H., Arola, A., Mikkonen, S., Kühn, T., Leskinen, A., Joutsensaari, J., Komppula, M., Lehtinen, K., Laaksonen, A., and Romakkaniemi, S.: Long-term measurements of cloud droplet concentrations and aerosol-cloud interactions in continental boundary layer clouds, Tellus B, 65, 20138, https://doi.org/10.3402/tellusb.v65i0.20138, 2013.
Andreae, M. O. and Rosenfeld, D.: Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth Sci. Rev., 89, 13–41, 2008.
Bais, A. F., Drosoglou, Th., Meleti, C., Tourpali, K., and Kouremeti, N.: Changes in surface shortwave solar irradiance from 1993 to 2011 at Thessaloniki (Greece), Int. J. Climatol., 33, 2871–2876, https://doi.org/10.1002/joc.3636, 2013.
Bates, D. M. and Watts, D. G.: Nonlinear Regression Analysis and Its Applications, Wiley, New York, 1988.
Bishop C. M.: Neural Networks for Pattern Recognition, Oxford University Press, Inc. New York, NY, USA, ISBN:0198538642, 1995.
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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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