Atmos. Chem. Phys. Discuss., 8, 19989-20018, 2008
www.atmos-chem-phys-discuss.net/8/19989/2008/
doi:10.5194/acpd-8-19989-2008
© Author(s) 2008. This work is distributed
under the Creative Commons Attribution 3.0 License.
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This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
Calibrated sky imager for aerosol optical properties determination
A. Cazorla1,2, J. E. Shields3, M. E. Karr3, A. Burden3, F. J. Olmo1,2, and L. Alados-Arboledas1,2
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 CA 92093-0701, USA

Abstract. The calibrated ground-based sky imager developed in the Marine Physical Laboratory, the Whole Sky Imager (WSI), has been tested 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, 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% 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.

Citation: Cazorla, A., Shields, J. E., Karr, M. E., Burden, A., Olmo, F. J., and Alados-Arboledas, L.: Calibrated sky imager for aerosol optical properties determination, Atmos. Chem. Phys. Discuss., 8, 19989-20018, doi:10.5194/acpd-8-19989-2008, 2008.
 
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