Abstract
Abstract. In the context of a network of sky cameras installed on atmospheric multi-instrumented sites, we present an algorithm named ELIFAN, which aims to estimate the cloud cover amount from full-sky visible daytime images with a common principle and procedure. ELIFAN was initially developed for a self-made full-sky image system presented in this article and adapted to a set of other systems in the network. It is based on red-to-blue ratio thresholding for the distinction of cloudy and cloud-free pixels of the image and on the use of a cloud-free sky library, without taking account of aerosol loading. Both an absolute (without the use of a cloud-free reference image) and a differential (based on a cloud-free reference image) red-to-blue ratio thresholding are used. An evaluation of the algorithm based on a 1-year-long series of images shows that the proposed algorithm is very convincing for most of the images, with about 97 % of relevance in the process, outside the sunrise and sunset transitions. During those latter periods, however, ELIFAN has large difficulties in appropriately processing the image due to a large difference in color composition and potential confusion between cloud-free and cloudy sky at that time. This issue also impacts the library of cloud-free images. Beside this, the library also reveals some limitations during daytime, with the possible presence of very small and/or thin clouds. However, the latter have only a small impact on the cloud cover estimate. The two thresholding methodologies, the absolute and the differential red-to-blue ratio thresholding processes, agree very well, with departure usually below 8 % except in sunrise–sunset periods and in some specific conditions. The use of the cloud-free image library gives generally better results than the absolute process. It particularly better detects thin cirrus clouds. But the absolute thresholding process turns out to be better sometimes, for example in some cases in which the sun is hidden by a cloud.
Highlights
Due to their crucial role in weather and climate, clouds are the focus of many observation systems all over the world
Several algorithms have been proposed that enable us to retrieve an estimation of cloud cover (e.g., Long and DeLuisi, 1998; Li et al, 2011; Ghonima et al, 2012; Martinis et al, 2013; Silva and Echer, 2013; Cazorla et al, 2015; Kim et al, 2016; Krinitskiy and Sinitsyn, 2016) or solar irradiance (Pfister et al, 2003; Chu et al, 2014; Chauvin et al, 2015; Kurtz and Kleissl, 2017), to classify the type of observed clouds (e.g., Heinle et al, 2010; Kazantzidis et al, 2012; Xia et al, 2015; Gan et al, 2017), or to track them (Peng et al, 2015; Cheng, 2017; Richardson et al, 2017)
A common sky imager algorithm has been developed, called ELIFAN, in order to retrieve in a similar way the cloud fraction from all the sky cameras of the different sites
Summary
Due to their crucial role in weather and climate, clouds are the focus of many observation systems all over the world. One can estimate cloud-base height (Allmen and Kegelmeyer, 1996; Kassianov et al, 2005; Nguyen and Kleissl, 2014) by using a pair of sky cameras Those systems are commonly deployed in the vicinity of solar farms for the intra-hour or now-casting of solar irradiance and during atmospheric field experiments or on permanent observatories for cloud cover and cloud type monitoring. Within the ACTRIS-FR1 French research infrastructure, several instrumented permanent sites have coordinated their actions for the observation of the atmosphere and attempt to homogenize their instrumental, data process, and data dissemination practices for wider and more consistent multiparameter data use by the international research community In this context, a common sky imager algorithm has been developed, called ELIFAN, in order to retrieve in a similar way the cloud fraction from all the sky cameras of the different sites. We make concluding remarks in the last section, with perspectives on the evolution of the algorithm and further discussion
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