Abstract
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.
Highlights
Clouds are an essential part of the atmospheric energy and water cycle, and their coverage state is crucial for radiative transfer models and climate simulations
9 the results of green channel background subtraction adaptive threshold (GBSAT), and (e) is the results of the proposed clear sky background differencing (CSBD) method. this comparison, we evaluate the precision of differ- algorithm obtains satisfactory results, both in the circumsolar ent cloud detection methods by visual examination
Yang et al (2015) suggested using the 1-D green channel of the RGB image instead of the 2-D R/B and the 3-D RGB methods for cloud detection methods by analyzing the imaging principle of the color camera; so, in this paper, the proposed CSBD algorithm was based on the green channel of the images
Summary
Clouds are an essential part of the atmospheric energy and water cycle, and their coverage state is crucial for radiative transfer models and climate simulations. Yang et al (2012) put forward the background subtraction adaptive threshold algorithm to segment clouds in the same (R − B)/(R + B) color space In addition to these 2-D red and blue channel algorithms, several methods have been developed to detect clouds based on the 3-D red-green-blue (RGB) color space. Most existing cloud identification algorithms encounter great uncertainties for cloud detection in the circumsolar and near-horizon regions To face this challenge, Long et al (2006) established a clear-sky function to identify clear, thin, and opaque clouds for the TSI images, and Long (2010) proposed a statistical analysis method to correct the cloud identification errors in those regions. Chauvin et al (2015) simulated clear sky background using least squares fitting in the normalized red/blue ratio space All of these algorithms assume the RGB information of the images is correct and reliable.
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