Cloud fraction determined by thermal infrared and visible all-sky cameras

Aebi, Christine; Gröbner, Julian; Kämpfer, Niklaus

doi:10.5194/amt-11-5549-2018

Cited by 22 publications

(19 citation statements)

References 48 publications

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“…A further refinement is the use of an infrared sky camera which allows cloud cover to be determined during the night. A research prototype, the thermal infrared cloud camera (IRCCAM), has been continuously operating at DAV since September 2015 (Aebi et al, 2018). A comparison of IRCCAM with the visible sky cameras gave FCC values to within ±0.07 and to within ±0.05 for APCADA.…”

Section: Improvement Of Methodsmentioning

confidence: 99%

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Trends in surface radiation and cloud radiative effect at four Swiss sites for the 1996–2015 period

Nyeki¹,

Wacker²,

Aebi³

et al. 2019

Preprint

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The trends of meteorological parameters and surface downward shortwave and longwave radiation (DSR, DLR) were analyzed at four stations (between 370 and 3580 m asl) in Switzerland for the 1996 -2015 period. Ground temperature, specific humidity and atmospheric integrated water vapor (IWV) increased during all-sky and cloud-free conditions. All-sky DSR and DLR trends were in the ranges 0.6 -4.3 W m -2 /decade and 0.9 -4.3 W m -2 /decade, respectively, while 15 corresponding cloud-free trends were -2.9 -3.3 W m -2 /decade and 2.9 -5.4 W m -2 /decade. The cloud radiative effect (CRE) was determined using radiative transfer calculations for cloud-free DSR and an empirical scheme for cloud-free DLR. CRE decreased in magnitude by 0.9 -3.1 W m -2 /decade which implies a reduction in cloud cover and/or a change towards a different cloud type over the four Swiss sites. Between 10 and 70% of the increase in DLR is explained by factors other than ground temperature and IWV. Trends in aerosol optical depth at each station over the same period remained insignificant, 20 and thus their contribution to the observed changes in surface radiative fluxes was negligible. A more detailed, long-term quantification of cloud changes is crucial and will be possible in the future as cloud cameras have been measuring at three of the four stations since 2013.

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Section: Improvement Of Methodsmentioning

confidence: 99%

“…A comparison of IRCCAM with the visible sky cameras gave FCC values to within ±0.07 and to within ±0.05 for APCADA. Aebi et al (2018) concluded that the use of FCC from infrared sky cameras could increase the accuracy of cloud-free climatologies when FCC time series 25 of adequate length become available.…”

Section: Improvement Of Methodsmentioning

confidence: 99%

Trends in surface radiation and cloud radiative effect at four Swiss sites for the 1996–2015 period

Nyeki¹,

Wacker²,

Aebi³

et al. 2019

Preprint

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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%

“…Ghonima et al (2012) compared the pixel red-blue ratio (RBR) to the RBR of a clear-sky library (CSL) for more accurate cloud segmentation. Different from the various algorithms mentioned above, Calbo and Sabburg (2008) presented several features that are computed from the threshold image, extracted from statistical measurements of image texture, based on the Fourier transform of the image, and can be useful for cloud segmentation of all-sky images. Peng et al (2015) designed a classifier-based pipeline of identifying and tracking clouds in three-dimensional space to utilize all three totalsky imagers for multisource image correction to enhance the overall accuracy of cloud detection.…”

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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“…As reported in Aebi et al (2018), there are some uncertainties with the automatic detection of thin high-level clouds. Therefore, for the final data set of 206 measurements, only situations with a measured CBH of at least 5 km are considered.…”

Section: Case Selectionmentioning

confidence: 99%

Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne

Aebi¹,

Gröbner²,

Kazadzis³

et al. 2019

Preprint

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Abstract. We have used a method based on ground-based solar radiation measurements and radiative transfer models (RTM) in order to estimate the following cloud optical properties: cloud optical thickness (COT), cloud single scattering albedo (SSAc) and effective droplet radius (reff). The method is based on the minimisation of the difference between modelled and measured downward shortwave radiation (DSR). The optical properties are estimated for more than 3,000 stratus-altostratus (St-As) and 206 cirrus-cirrostratus (Ci-Cs) measurements during 2013–2017, at the Baseline Surface Radiation Network (BSRN) station in Payerne, Switzerland. The RTM libRadtran is used to simulate the total DSR, as well as its direct and diffuse components. The model inputs of additional atmospheric parameters are either ground- or satellite-based measurements. The cloud cases are identified by the use of an all-sky cloud camera. For the low- to mid-level cloud class St-As, 95 % of the estimated COT values from DSR measurements (COTDSR) are between 11.9 and 91.5 with a geometric mean and standard deviation of 33.81 and 1.67, respectively. The comparison of these COTDSR values with COTBarnard values retrieved from an independent empirical equation, results in a mean difference of −1.20 ± 2.73 and is thus within the method uncertainty. However, there is a larger mean difference of around 18 between COTDSR and COT values derived from MODIS level-2 (L2), Collection 6.1 (C6.1) data (COTMODIS). The estimated reff (from liquid water path (LWP) and COTDSR) for St-As are between 2.1 and 20.4 μm. For the high-level cloud class Ci-Cs, COTDSR is derived considering the direct radiation and 95 % of the values are between 0.32 and 1.40. For Ci-Cs, 95 % of the SSAc values are estimated to be between 0.84 and 0.99 using diffuse radiation measurements. The COT values for Ci-Cs are also estimated from data from precision filter radiometers (PFR) at various wavelengths. The herein presented method could be applied and validated at other stations with direct and diffuse radiation measurements.

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Cloud fraction determined by thermal infrared and visible all-sky cameras

Cited by 22 publications

References 48 publications

Trends in surface radiation and cloud radiative effect at four Swiss sites for the 1996–2015 period

Trends in surface radiation and cloud radiative effect at four Swiss sites for the 1996–2015 period

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

Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne

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