Abstract
Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.
Highlights
Clouds play an important role in the global water cycle and the Earth’s radiation budget (Kokhanovsky, 2006), and their sky coverage and movements are strongly influenced by weather phenomena
The imaging theory of cameras shows that the color images we acquire from cameras are not really true color images, which are obtained by interpolation from raw color filter array (CFA) data
The difficulty of detecting clouds lies in the uneven illumination in the total-sky images, which means a single threshold is inapplicable for this condition
Summary
Clouds play an important role in the global water cycle and the Earth’s radiation budget (Kokhanovsky, 2006), and their sky coverage and movements are strongly influenced by weather phenomena. Several automatic ground-based sky imaging devices were manufactured and implemented in order to retrieve cloud coverage and type from the visible portion of the electromagnetic spectrum These instruments include the Total-Sky Imager (TSI) (Long and DeLuisi, 1998), the Whole-Sky Imager (WSI) (Johnson et al, 1989; Shields et al.,1993), the Whole-Sky Camera (WSC) (Long et al, 2006; Calbó and Sabburg, 2008), the All-Sky Imager (Cazorla et al, 2008), the All-Sky Imager system (ASIs) (Huo and Lu, 2009), the Total-Sky Cloud Imager (TCI) (Yang et al, 2012) and the Solmirus All-Sky Infrared Visible Analyzer (ASIVA) (Morris and Klebe, 2010; Klebe et al, 2014).
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