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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 9
Atmos. Meas. Tech., 7, 3151–3175, 2014
https://doi.org/10.5194/amt-7-3151-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 7, 3151–3175, 2014
https://doi.org/10.5194/amt-7-3151-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 26 Sep 2014

Research article | 26 Sep 2014

Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peak

M. Taylor et al.
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Abdi, H. and Williams, L. J.: Principal component analysis, Wiley Interdisciplinary Reviews, Comput. Stat., 2, 433–459, https://doi.org/10.1002/wics.101, 2010.
AERONET: Level 2.0 Version 2 daily averaged almucantar inversion products, available at:f http://aeronet.gsfc.nasa.gov/cgi-bin/combined_data_access_inv, last access: 7 April 2012.
Albayrak, A., Wei, J., Petrenko, M., Lynnes, C., and Levy, R. C.: Global bias adjustment for MODIS aerosol optical thickness using neural network, J. Appl. Remote Sens., 7, 073514, 1–16, 2013.
Bishop, C. M.: Neural Networks for Pattern Recognition, Oxford University Press, New York, NY, USA, 1995.
Chin, M., Rood, R. B., Lin, S. J., Müller, J. F., and Thompson, A. M.: Atmospheric sulfur cycle simulated in the global model GOCART: model description and global properties, J. Geophys. Res., 105, 24671–24687, 2000.
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