All-sky photogrammetry techniques to georeference a cloud field

Crispel, Pierre; Roberts, Grégory

doi:10.5194/amt-11-593-2018

Cited by 13 publications

(14 citation statements)

References 16 publications

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“…Due to the financial and technical constraints of low cost forecasting systems, a direct retrieval of cloud height and tracking information from the sky images itself is mandatory. Stereoscopic approaches with two ASIs are frequently described in the literature (Allmen et al 1996, Kassianov et al 2005, Seiz et al 2007, Nguyen et al 2014, Beekmans et al 2016and Crispel et al 2017. Cloud heights are determined by matching segmented clouds from images taken simultaneously by two ASIs.…”

Section: State Of the Artmentioning

confidence: 99%

“…Most motion vector validation are done indirectly by comparing the achieved forecast score (Quesada-Ruiz et al 2014 andPeng et al 2015) or by comparing the previously forecasted cloud cover with the corresponding real cloud cover (Huang et al 2012, Chow et al 2015and Zaher et al 2017. Others estimate motion vector uncertainties (Crispel et al 2017 andSchmidt et al 2016).…”

Section: Cloud Motion Vector Validation Of the Three Systems Comparedmentioning

confidence: 99%

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Cloud height and tracking accuracy of three all sky imager systems for individual clouds

Nouri¹,

Kuhn²,

Wilbert³

et al. 2019

Solar Energy

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Solar irradiance nowcasts can be derived with sky images from all sky imagers (ASI) by detecting and analyzing transient clouds, which are the main contributor of intra-hour solar irradiance variability. The accuracy of ASI based solar irradiance nowcasting systems depends on various processing steps. Two vital steps are the cloud height detection and cloud tracking. This task is challenging, due to the atmospheric conditions that are often complex, including various cloud layers moving in different directions simultaneously. This challenge is addressed by detecting and tracking individual clouds. For this, we developed two distinct ASI nowcasting approaches with four or two cameras and a third hybridized approach. These three systems create individual 3-D cloud models with unique attributes 2 including height, position, size, optical properties and motion. This enables us to describe complex multi-layer conditions. In this paper, derived cloud height and motion vectors are compared with a reference ceilometer (height) and shadow camera system (motion) over a 30 day validation period. The validation data set includes a wide range of cloud heights, cloud motion patterns and atmospheric conditions. Furthermore, limitations of ASI based nowcasting systems due to image resolution and image perspective constrains are discussed. The most promising system is found to be the hybridized approach. This approach uses four ASIs and a voxel carving based cloud modeling combined with a cloud segmentation independent stereoscopic cloud height and tracking detection. We observed for this approach an overall mean absolute error of 648 m for the height, 1.3 m/s for the cloud speed and 16.2° for the motion direction.

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Section: State Of the Artmentioning

confidence: 99%

Section: Cloud Motion Vector Validation Of the Three Systems Comparedmentioning

confidence: 99%

Cloud height and tracking accuracy of three all sky imager systems for individual clouds

Nouri¹,

Kuhn²,

Wilbert³

et al. 2019

Solar Energy

View full text Add to dashboard Cite

show abstract

“…Ghonima et al (2012) and Shields et al (2013) give rich and interesting reviews of sky imager systems and their applications. Several algorithms have now 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). Sky imagers have also been specifically used for the detection of cirrus (Yang et al, 2012) or thin clouds (Li et al, 2012) and contrail studies (Schumann et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Reply to Referee #1

Lothon¹

2019

Preprint

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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.

show abstract

“…Ghonima et al (2012) and Shields et al (2013) give rich and interesting reviews of sky imager systems and their applications. Several algorithms have now 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). Sky imagers have also been specifically used for the detection of cirrus or thin clouds (Li et al, 2012) and contrail studies (Schumann et al, 2013).…”

mentioning

confidence: 99%

ELIFAN, an algorithm for the estimation of cloud cover from sky imagers

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

All-sky photogrammetry techniques to georeference a cloud field

Cited by 13 publications

References 16 publications

Cloud height and tracking accuracy of three all sky imager systems for individual clouds

Cloud height and tracking accuracy of three all sky imager systems for individual clouds

Reply to Referee #1

ELIFAN, an algorithm for the estimation of cloud cover from sky imagers

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