Cloud cover detection combining high dynamic range sky images and ceilometer measurements

Román, Roberto; Cazorla, A.; Toledano, Carlos; Olmo, F.J.; Cachorro, Victoria E.; Frutos, Ángel de; Alados-Arboledas, L.

doi:10.1016/j.atmosres.2017.06.006

Cited by 30 publications

(24 citation statements)

References 44 publications

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“…The GRASP versatility allows the retrieval of aerosol properties through the combination of measurements from different instruments both column-integrated and vertically resolved. In fact, GRASP was successfully utilized for the retrieval of the aerosol properties using different configurations and measurements, such as polar nephelometer data (Espinosa et al, 2017), satellite remote-sensing data (Kokhanovsky et al, 2015;Dubovik et al, 2019), aerosol optical depth (AOD) and sky radiances (including polarization) (Fedarenka et al, 2016), spectral AOD and sky camera images (Román et al, 2017a), only spectral AOD (Torres et al, 2017), and the combination of aerosol optical depth (AOD), sky radiances and elastic lidar (Lopatin et al, 2013; or ceilometer profiles (Román et al, 2018). The aerosol properties retrieved by GRASP aerosol profiles have been used as input to radiative transfer models (Granados-Muñoz et al, 2019), to evaluate dust forecast models (Tsekeri et al, 2017) or to be assimilated in global models (Chen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Different strategies to retrieve aerosol properties at night-time with the GRASP algorithm

et al. 2019

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Abstract. This study evaluates the potential of the GRASP algorithm (Generalized Retrieval of Aerosol and Surface Properties) to retrieve continuous day-to-night aerosol properties, both column-integrated and vertically resolved. The study is focused on the evaluation of GRASP retrievals during an intense Saharan dust event that occurred during the Sierra Nevada Lidar aerOsol Profiling Experiment I (SLOPE I) field campaign. For daytime aerosol retrievals, we combined the measurements of the ground-based lidar from EARLINET (European Aerosol Research Lidar Network) station and sun–sky photometer from AERONET (Aerosol Robotic Network), both instruments co-located in Granada (Spain). However, for night-time retrievals three different combinations of active and passive remote-sensing measurements are proposed. The first scheme (N0) uses lidar night-time measurements in combination with the interpolation of sun–sky daytime measurements. The other two schemes combine lidar night-time measurements with night-time aerosol optical depth obtained by lunar photometry either using intensive properties of the aerosol retrieved during sun–sky daytime measurements (N1) or using the Moon aureole radiance obtained by sky camera images (N2). Evaluations of the columnar aerosol properties retrieved by GRASP are done versus standard AERONET retrievals. The coherence of day-to-night evolutions of the different aerosol properties retrieved by GRASP is also studied. The extinction coefficient vertical profiles retrieved by GRASP are compared with the profiles calculated by the Raman technique at night-time with differences below 30 % for all schemes at 355, 532 and 1064 nm. Finally, the volume concentration and scattering coefficient retrieved by GRASP at 2500 m a.s.l. are evaluated by in situ measurements at this height at Sierra Nevada Station. The differences between GRASP and in situ measurements are similar for the different schemes, with differences below 30 % for both volume concentration and scattering coefficient. In general, for the scattering coefficient, the GRASP N0 and N1 show better results than the GRASP N2 schemes, while for volume concentration, GRASP N2 shows the lowest differences against in situ measurements (around 10 %) for high aerosol optical depth values.

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Section: Introductionmentioning

confidence: 99%

Different strategies to retrieve aerosol properties at night-time with the GRASP algorithm

et al. 2019

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“…With the development of hardware technologies such as charge-coupled devices and digital image processing, many ground-based full-sky cloud-measuring instruments have been successfully developed. Currently, the most representative instruments include the Whole Sky Imager (WSI; Johnson et al, 1989), Total Sky Imager (TSI; Long and Deluisi, 1998;Long et al, 2006), Infrared Cloud Imager (ICI; Shaw et al, 2005;Thurairajah and Shaw, 2005;Nugent et al, 2009Nugent et al, , 2013, All Sky Imager (ASI; Cazorla et al, 2008), Whole Sky Infrared Cloud Measuring System (WSIRCMS; Sun et al, 2008), Total Sky Cloud Imager (TCI; Yang et al, 2012), All-Sky Infrared Visible Analyzer (ASIVA; Klebe et al, 2014), Whole Sky Camera (WSC; Kuji et al, 2018), and All Sky Camera (ASC; Aebi et al, 2018). The above instruments provide hardware support for analyzing ground-based cloud images, making the automated observation of groundbased cloud images possible.…”

Section: Introductionmentioning

confidence: 99%

Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks

et al. 2019

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Abstract. Cloud segmentation plays a very important role in astronomical observatory site selection. At present, few researchers segment cloud in nocturnal all-sky imager (ASI) images. This paper proposes a new automatic cloud segmentation algorithm that utilizes the advantages of deep-learning fully convolutional networks (FCNs) to segment cloud pixels from diurnal and nocturnal ASI images; it is called the enhancement fully convolutional network (EFCN). Firstly, all the ASI images in the data set from the Key Laboratory of Optical Astronomy at the National Astronomical Observatories of Chinese Academy of Sciences (CAS) are converted from the red–green–blue (RGB) color space to hue saturation intensity (HSI) color space. Secondly, the I channel of the HSI color space is enhanced by histogram equalization. Thirdly, all the ASI images are converted from the HSI color space to RGB color space. Then after 100 000 iterative trainings based on the ASI images in the training set, the optimum associated parameters of the EFCN-8s model are obtained. Finally, we use the trained EFCN-8s to segment the cloud pixels of the ASI image in the test set. In the experiments our proposed EFCN-8s was compared with four other algorithms (OTSU, FCN-8s, EFCN-32s, and EFCN-16s) using four evaluation metrics. Experiments show that the EFCN-8s is much more accurate in cloud segmentation for diurnal and nocturnal ASI images than the other four algorithms.

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“…The combination of both allowed Kazantzidis et al (2012) to distinguish seven types of clouds, in addition to estimating cloud cover amount. Roman et al (2017) interestingly use the fact that an image of totally clear sky is symmetric.…”

Section: Elifan Algorithmmentioning

confidence: 99%

“…Finally, there is of course a gain in combining instruments. 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%

“…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). Furthermore, one can also estimate cloud-base height (Allmen and Kegelmeyer, 1996;Kassianov et al, 2005;Nguyen and Kleissl, 2014) by using a pair of sky cameras.…”

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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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Cloud cover detection combining high dynamic range sky images and ceilometer measurements

Cited by 30 publications

References 44 publications

Different strategies to retrieve aerosol properties at night-time with the GRASP algorithm

Different strategies to retrieve aerosol properties at night-time with the GRASP algorithm

Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks

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

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