Articles | Volume 8, issue 1
https://doi.org/10.5194/amt-8-281-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-8-281-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
14 Jan 2015
Research article |
| 14 Jan 2015
Use of neural networks in ground-based aerosol retrievals from multi-angle spectropolarimetric observations
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, the Netherlands
O. P. Hasekamp
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, the Netherlands
G. van Harten
Leiden Observatory, Leiden University, Niels Bohrweg 2, 2333 CA Leiden, the Netherlands
J. H. H. Rietjens
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, the Netherlands
J. M. Smit
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA Utrecht, the Netherlands
F. Snik
Leiden Observatory, Leiden University, Niels Bohrweg 2, 2333 CA Leiden, the Netherlands
J. S. Henzing
Netherlands Organisation for Applied Research (TNO), Princetonlaan 6, 3584 CB Utrecht, the Netherlands
J. de Boer
Leiden Observatory, Leiden University, Niels Bohrweg 2, 2333 CA Leiden, the Netherlands
C. U. Keller
Leiden Observatory, Leiden University, Niels Bohrweg 2, 2333 CA Leiden, the Netherlands
H. Volten
National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, the Netherlands
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Short summary
A neural network algorithm has been developed to retrieve aerosol microphysical parameters from ground-based measurements of skylight intensity and polarization. The neural network is capable of producing accurate estimates of aerosol optical thicknesses, effective radii and refractive index. In addition, it is shown that the use of the neural retrievals as initial guess for an iterative retrieval algorithm results in improved convergence and retrieval accuracy.
A neural network algorithm has been developed to retrieve aerosol microphysical parameters from...
