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

Related authors

Effect of water vapor on the determination of aerosol direct radiative effect based on the AERONET fluxes
J. Huttunen, A. Arola, G. Myhre, A. V. Lindfors, T. Mielonen, S. Mikkonen, J. S. Schafer, S. N. Tripathi, M. Wild, M. Komppula, and K. E. J. Lehtinen
Atmos. Chem. Phys., 14, 6103–6110,,, 2014
Influence of observed diurnal cycles of aerosol optical depth on aerosol direct radiative effect
A. Arola, T. F. Eck, J. Huttunen, K. E. J. Lehtinen, A. V. Lindfors, G. Myhre, A. Smirnov, S. N. Tripathi, and H. Yu
Atmos. Chem. Phys., 13, 7895–7901,,, 2013
Intercomparison of shortwave radiative transfer schemes in global aerosol modeling: results from the AeroCom Radiative Transfer Experiment
C. A. Randles, S. Kinne, G. Myhre, M. Schulz, P. Stier, J. Fischer, L. Doppler, E. Highwood, C. Ryder, B. Harris, J. Huttunen, Y. Ma, R. T. Pinker, B. Mayer, D. Neubauer, R. Hitzenberger, L. Oreopoulos, D. Lee, G. Pitari, G. Di Genova, J. Quaas, F. G. Rose, S. Kato, S. T. Rumbold, I. Vardavas, N. Hatzianastassiou, C. Matsoukas, H. Yu, F. Zhang, H. Zhang, and P. Lu
Atmos. Chem. Phys., 13, 2347–2379,,, 2013

Related subject area

Subject: Radiation | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Record-breaking statistics detect islands of cooling in a sea of warming
Elisa T. Sena, Ilan Koren, Orit Altaratz, and Alexander B. Kostinski
Atmos. Chem. Phys., 22, 16111–16122,,, 2022
Short summary
Radiative closure and cloud effects on the radiation budget based on satellite and shipborne observations during the Arctic summer research cruise, PS106
Carola Barrientos-Velasco, Hartwig Deneke, Anja Hünerbein, Hannes J. Griesche, Patric Seifert, and Andreas Macke
Atmos. Chem. Phys., 22, 9313–9348,,, 2022
Short summary
Impacts of active satellite sensors' low-level cloud detection limitations on cloud radiative forcing in the Arctic
Yinghui Liu
Atmos. Chem. Phys., 22, 8151–8173,,, 2022
Short summary
Longwave radiative effect of the cloud–aerosol transition zone based on CERES observations
Babak Jahani, Hendrik Andersen, Josep Calbó, Josep-Abel González, and Jan Cermak
Atmos. Chem. Phys., 22, 1483–1494,,, 2022
Short summary
Ice and mixed-phase cloud statistics on the Antarctic Plateau
William Cossich, Tiziano Maestri, Davide Magurno, Michele Martinazzo, Gianluca Di Natale, Luca Palchetti, Giovanni Bianchini, and Massimo Del Guasta
Atmos. Chem. Phys., 21, 13811–13833,,, 2021
Short summary

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,, 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,, 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.
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