Long-term (1995–2018) aerosol optical depth derived using ground based AERONET and SKYNET measurements from aerosol aged-background sites

Ningombam, Shantikumar S.; Larson, E. J. L.; Dumka, U. C.; Estellés, V.; Campanelli, Monica; Colwell, Steve

doi:10.1016/j.apr.2018.10.008

Cited by 38 publications

(22 citation statements)

References 42 publications

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“…Mathematical tools like neural networks, K-nearest neighbor techniques, and support vector machines (SVMs) are also used for cloud detection based on RGB composition and luminosity (Blazek and Pata, 2015;Taravat et al, 2015;Cheng, 2017) and on the image texture (Cazorla et al, 2008;Kazantzidis et al, 2012;Liu et al, 2015;Peng et al, 2015). The combination of both allowed Kazantzidis et al (2012) to distinguish seven types of clouds, in addition to estimating cloud cover amount.…”

Section: Elifan Algorithmmentioning

confidence: 99%

“…Ceilometers and sky imagers are very complementary in this respect, as the ceilometer adds cloud-base height information above the sky camera system (Chu et al, 2014;Roman et al, 2017), which can allow for access to cloud speed motion . Pyranometers are also naturally considered in such instrumental synergy (Peng et al, 2015;.…”

Section: Elifan Algorithmmentioning

confidence: 99%

“…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).…”

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confidence: 99%

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

et al. 2019

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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.

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Section: Elifan Algorithmmentioning

confidence: 99%

Section: Elifan Algorithmmentioning

confidence: 99%

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confidence: 99%

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

et al. 2019

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“…As evidenced by Provençal et al [14], air quality on the European continent has significantly improved over the last decades, with reductions in anthropogenic emissions leading to negative AOD trends over several aged-background sites [16]. To verify this context, AOD trends were evaluated over a 30-year period in five of the most populated cities in Italy (Milan, Rome, Cagliari, Taranto, and Palermo).…”

Section: Introductionmentioning

confidence: 99%

Aerosol Optical Depth of the Main Aerosol Species over Italian Cities Based on the NASA/MERRA-2 Model Reanalysis

et al. 2019

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The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) provides data at 0.5° × 0.625° resolution covering a period from 1 January 1980 to the present. Natural and anthropogenic aerosols are simulated in MERRA-2, considering the Goddard chemistry, aerosol, radiation, and transport model. This model simulates the sources, sinks, and chemistry of mixed aerosol tracers: dust, sea salt, hydrophobic and hydrophilic black carbon and organic carbon, and sulfate. MERRA-2 aerosol reanalysis is a pioneering tool for investigating air quality issues, noteworthy for its global coverage and its distinction of aerosol speciation expressed in the form of aerosol optical depth (AOD). The aim of this work was to use the MERRA-2 reanalysis to study urban air pollution at a national scale by analyzing the AOD. AOD trends were evaluated for a 30-year period (1987–2017) over five Italian cities (Milan, Rome, Cagliari, Taranto, and Palermo) in order to investigate the impacts of urbanization, industrialization, air quality regulations, and regional transport on urban aerosol load. AOD evolution predicted by the MERRA-2 model in the period 2002–2017 showed a generalized decreasing trend over the selected cities. The anthropogenic signature on total AOD was between 50% and 80%, with the largest contribution deriving from sulfate.

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“…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%

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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.

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Long-term (1995–2018) aerosol optical depth derived using ground based AERONET and SKYNET measurements from aerosol aged-background sites

Cited by 38 publications

References 42 publications

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

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

Aerosol Optical Depth of the Main Aerosol Species over Italian Cities Based on the NASA/MERRA-2 Model Reanalysis

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