Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.400 IF 3.400
  • IF 5-year value: 3.841 IF 5-year
    3.841
  • CiteScore value: 3.71 CiteScore
    3.71
  • SNIP value: 1.472 SNIP 1.472
  • IPP value: 3.57 IPP 3.57
  • SJR value: 1.770 SJR 1.770
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 70 Scimago H
    index 70
  • h5-index value: 49 h5-index 49
Volume 9, issue 2 | Copyright
Atmos. Meas. Tech., 9, 587-597, 2016
https://doi.org/10.5194/amt-9-587-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 23 Feb 2016

Research article | 23 Feb 2016

A total sky cloud detection method using real clear sky background

Jun Yang1,2, Qilong Min2,3, Weitao Lu1, Ying Ma1, Wen Yao1, Tianshu Lu1, Juan Du3, and Guangyi Liu4 Jun Yang et al.
  • 1State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 2Atmospheric Sciences Research Center, State University of New York, Albany, NY 12203, USA
  • 3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 4Smart Grid Operation Research Center, China Electric Power Research Institute, Beijing 100192, China

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods, including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show that the cloud detection results are satisfactory when the two images have the same solar positions. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

Publications Copernicus
Download
Citation
Share