Daylight visible/NIR whole-sky imagers for cloud and radiance monitoring in support of UV research programs

Shields, Janet E.; Johnson, Richard W.; Karr, Monette E.; Burden, Art R.; Baker, Justin G.

doi:10.1117/12.509062

Cited by 29 publications

(28 citation statements)

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“…A number of papers (Long et al 2006;SouzaEcher et al 2006;Kassianov et al 2005;Lu et al 2004;Pfister et al 2003;Shields et al 2003;Martins et al 2003;Crawford et al 2003;Sabburg and Wong 1999) demonstrate the increased number of ground-based sky imagers being developed and used in several countries. This development is partly due to the dramatic improvements in technology in recent years, with respect to both the hardware [e.g., charge-coupled devices (CCDs)] and digital image processing (DIP) techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Among the most well-known instruments of this kind is the family of whole sky imagers (WSIs), developed by the Scripps Institute of Oceanography at the University of California, San Diego. They are designed to measure radiances at distinct wavelength bands across the hemisphere (Shields et al 2003). The WSI, besides being used for many other interesting scientific capabilities, can estimate fractional sky cover (Tooman 2003;Johnson et al 1989).…”

Section: Introductionmentioning

confidence: 99%

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Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

Calbó

Sabburg

2008

Journal of Atmospheric and Oceanic Technology

195

121

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Several features that can be extracted from digital images of the sky and that can be useful for cloud-type classification of such images are presented. Some features are statistical measurements of image texture, some are based on the Fourier transform of the image and, finally, others are computed from the image where cloudy pixels are distinguished from clear-sky pixels. The use of the most suitable features in an automatic classification algorithm is also shown and discussed. Both the features and the classifier are developed over images taken by two different camera devices, namely, a total sky imager (TSI) and a whole sky imager (WSC), which are placed in two different areas of the world (Toowoomba, Australia; and Girona, Spain, respectively). The performance of the classifier is assessed by comparing its image classification with an a priori classification carried out by visual inspection of more than 200 images from each camera. The index of agreement is 76% when five different sky conditions are considered: clear, low cumuliform clouds, stratiform clouds (overcast), cirriform clouds, and mottled clouds (altocumulus, cirrocumulus). Discussion on the future directions of this research is also presented, regarding both the use of other features and the use of other classification techniques.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

Calbó

Sabburg

2008

Journal of Atmospheric and Oceanic Technology

195

121

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“…One of the best known commercial manufacturer of such instruments is the Scripps Institute of Oceanography at the University of California San Diego. Their Whole Sky Imagers are constructed to measure sky radiance at diverse wavelength bands (visible spectrum and near infrared) across the whole hemisphere (Shields et al, 1998(Shields et al, , 2003. Due to the high-quality components involved, these imagers are often too expensive for small research groups.…”

Section: Introductionmentioning

confidence: 99%

Automatic cloud classification of whole sky images

2010

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Abstract.The recently increasing development of whole sky imagers enables temporal and spatial high-resolution sky observations. One application already performed in most cases is the estimation of fractional sky cover. A distinction between different cloud types, however, is still in progress. Here, an automatic cloud classification algorithm is presented, based on a set of mainly statistical features describing the color as well as the texture of an image. The k-nearestneighbour classifier is used due to its high performance in solving complex issues, simplicity of implementation and low computational complexity. Seven different sky conditions are distinguished: high thin clouds (cirrus and cirrostratus), high patched cumuliform clouds (cirrocumulus and altocumulus), stratocumulus clouds, low cumuliform clouds, thick clouds (cumulonimbus and nimbostratus), stratiform clouds and clear sky. Based on the Leave-One-Out CrossValidation the algorithm achieves an accuracy of about 97%. In addition, a test run of random images is presented, still outperforming previous algorithms by yielding a success rate of about 75%, or up to 88% if only "serious" errors with respect to radiation impact are considered. Reasons for the decrement in accuracy are discussed, and ideas to further improve the classification results, especially in problematic cases, are investigated.

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“…Most of these imagers have been purposely constructed with a specific application in mind, for example, the first integrated sun-centered sky camera (SCSC) for solar UV research (Sabburg and Wong 1999), while other imagers have the general purpose of measuring cloud macroscopic characteristics (Pagès et al 2002;Lu et al 2004;Long and DeLuisi 1998). A family of Whole Sky Imagers (WSIs), developed by the Scripps Institution of Oceanography at the University of California, San Diego, are designed to measure radiances at distinct wavelength bands across the hemisphere (Johnson et al 1989;Shields et al 2003). The various models of the WSI include a highquality temperature-controlled CDD, high-quality optics including spectral filtering, detailed mapping of the sky dome to CCD element, and careful calibration of dark current and stray light influences needed to make the scientific-quality spectral radiance measurements.…”

Section: Introductionmentioning

confidence: 99%

Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images

Long

Sabburg

Calbó

et al. 2006

Journal of Atmospheric and Oceanic Technology

374

306

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A discussion is presented of daytime sky imaging and techniques that may be applied to the analysis of full-color sky images to infer cloud macrophysical properties. Descriptions of two different types of skyimaging systems developed by the authors are presented, one of which has been developed into a commercially available instrument. Retrievals of fractional sky cover from automated processing methods are compared to human retrievals, both from direct observations and visual analyses of sky images. Although some uncertainty exists in fractional sky cover retrievals from sky images, this uncertainty is no greater than that attached to human observations for the commercially available sky-imager retrievals. Thus, the application of automatic digital image processing techniques on sky images is a useful method to complement, or even replace, traditional human observations of sky cover and, potentially, cloud type. Additionally, the possibilities for inferring other cloud parameters such as cloud brokenness and solar obstruction further enhance the usefulness of sky imagers.

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Daylight visible/NIR whole-sky imagers for cloud and radiance monitoring in support of UV research programs

Cited by 29 publications

References 0 publications

Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

Automatic cloud classification of whole sky images

Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images

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