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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 5, issue 11 | Copyright
Atmos. Meas. Tech., 5, 2881-2892, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Nov 2012

Research article | 27 Nov 2012

A method for cloud detection and opacity classification based on ground based sky imagery

M. S. Ghonima1, B. Urquhart1, C. W. Chow1, J. E. Shields2, A. Cazorla3, and J. Kleissl1 M. S. Ghonima et al.
  • 1Department of Mechanical and Aerospace Engineering, University of California, San Diego, USA
  • 2Marine Physical Laboratory, Scripps Institution of Oceanography, University of California, San Diego, USA
  • 3Department of Chemistry and Biochemistry, University of California, San Diego, USA

Abstract. Digital images of the sky obtained using a total sky imager (TSI) are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR) to the RBR of a clear sky library (CSL) generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD) measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF) to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

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