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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 17 | Copyright
Atmos. Chem. Phys., 9, 6417-6427, 2009
https://doi.org/10.5194/acp-9-6417-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  08 Sep 2009

08 Sep 2009

Technical Note: Determination of aerosol optical properties by a calibrated sky imager

A. Cazorla1,2, J. E. Shields3, M. E. Karr3, F. J. Olmo3, A. Burden1,2, and L. Alados-Arboledas1,2 A. Cazorla et al.
  • 1Departamento de Física Aplicada, Facultad de Ciencias, Universidad de Granada, Fuentenueva s/n. 18071, Granada, Spain
  • 2Centro Andaluz de Medio Ambiente (CEAMA), Junta de Andalucía-Universidad de Granada, Avda. del Mediterraneo s/n. 18071, Granada, Spain
  • 3Marine Physical Lab, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Dr., La Jolla, 92093-0701 CA, USA

Abstract. The calibrated ground-based sky imager developed in the Marine Physical Laboratory, the Whole Sky Imager (WSI), has been tested with data from the Atmospheric Radiation Measurement Program (ARM) at the Southern Great Plain site (SGP) to determine optical properties of the atmospheric aerosol. Different neural network-based models calculate the aerosol optical depth (AOD) for three wavelengths using the radiance extracted from the principal plane of sky images from the WSI as input parameters. The models use data from a CIMEL CE318 photometer for training and validation and the wavelengths used correspond to the closest wavelengths in both instruments. The spectral dependency of the AOD, characterized by the Ångström exponent α in the interval 440–870 nm, is also derived using the standard AERONET procedure and also with a neural network-based model using the values obtained with a CIMEL CE318. The deviations between the WSI derived AOD and the AOD retrieved by AERONET are within the nominal uncertainty assigned to the AERONET AOD calculation (±0.01), in 80% of the cases. The explanation of data variance by the model is over 92% in all cases. In the case of α, the deviation is within the uncertainty assigned to the AERONET α (±0.1) in 50% of the cases for the standard method and 84% for the neural network-based model. The explanation of data variance by the model is 63% for the standard method and 77% for the neural network-based model.

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