Comparison of two different techniques to determine the cloud cover from all-sky imagery

Catalán-Valdelomar, Pedro; Gómez-Amo, José Luís; Scarlatti, Francesco; Peris-Ferrús, Caterina; Utrillas, M. P.

doi:10.1117/12.2599517

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Thirdly, a cloud screening method has been performed [3] in order to separate the cloudy zone to the transition zone, based on a threshold method performed on the Blue to Red Ratio (BRR) of all the pixels in the image. This has been done because of the following observation.…”

Section: Instruments and Methodsmentioning

confidence: 99%

Exploring the aerosol-cloud transition zone from advanced all-sky camera observations

Scarlatti,

Tarín,

Gómez-Amo

et al. 2023

Remote Sensing of Clouds and the Atmosphere XXVIII

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Aerosols and clouds have been scientifically considered distinct from theoretical point of view. The two components share the same physical meaning, namely particles suspended in the air, even if the chemical or physical features can differentiate. Form the radiometric view it has been a challenging task to separate the two atmospheric components in an exact way. Recently scientists are also discussing about a continuum between aerosols and clouds. In this study we use calibrated images of an all-sky camera with the aim of exploring the features that can differentiate the two regions in terms of radiometric magnitudes. An intense smoothing is applied, and the spatial derivatives are performed on the red channel radiances. These derivatives are almost zero in the cloud-free area and sensibly different from zero in the rest of the image. Applying dynamic thresholds to Blue-to-Red ratio (BRR), we further determine the cloudy region of the sky. Then, we define aerosol-cloud transition zone as the non-cloudy sky zone with intense directional derivatives of the radiances in the red channel. This transition zone shows different radiometric characteristics with respect to cloud and cloud-free regions, for example in terms of BRR distribution of the pixels.

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Section: Instruments and Methodsmentioning

confidence: 99%

Exploring the aerosol-cloud transition zone from advanced all-sky camera observations

Scarlatti,

Tarín,

Gómez-Amo

et al. 2023

Remote Sensing of Clouds and the Atmosphere XXVIII

Self Cite

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show abstract

“…Additionally, we employed a SONA sky-imager, integrated by Sieltec SL, to capture high dynamic range (HDR) images, which have been geometrically and radiatively calibrated to provide the radiance from each portion of the sky with an angular resolution of 0.3-0.6º, and 1-min temporal resolution [7]. The cloud fraction (CF) is obtained using an adaptive threshold method applied to the Probability Density Function (PDF) of the blue to red ratio (BRR) [8].…”

Section: Measurements Site and Instrumentsmentioning

confidence: 99%

Machine learning clustering of cloud regimes using synergetic ground-based remote sensing observations

Julián-Izquierdo,

García-Pitarch,

Scarlatti

et al. 2023

Remote Sensing of Clouds and the Atmosphere XXVIII

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Clouds are essential in climate, especially to evaluate the radiative balance in the Earth atmosphere and, their contribution depends on the type of cloud. In addition, cloud classification plays an important role in the development of different research and technological fields such as solar photovoltaic energy. We use ground-based zenith observations of cloud optical depth (COD) and cloud base height (CBH), at 1-minute intervals, to develop a clustering algorithm. It is based on non-supervised machine learning using k-means function. Due to the intrinsic characteristics of the measuring instruments, high-altitude clouds with large COD are not accurately represented. For this reason, a classification into six categories is performed. Regarding to COD, our machine learning method detects three COD clusters separated at 3.2 and 24.5. On the other hand, the three CBH clusters well identify low-, mid-and high-clouds, with centroids around 1500 m, 5399-6240 m, and 9589 m, respectively. A slight increase in these CBH boundaries with COD is also observed. Our clustering method is consistent and robust since it does not present any sensitivity regarding to the temporal window used to perform the clustering. The resulting clusters are consistent and in line with the cloud classification established by the WMO.In addition, the cloud fraction (CF) measured with an all-sky camera has been analyzed in terms of the defined cloud types. Our results indicate that the CF increases with COD, up to 24.5, and stabilize for thicker clouds for which overcast conditions, with CF > 70 %, are mostly observed.

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Comparison of two different techniques to determine the cloud cover from all-sky imagery

Cited by 2 publications

References 14 publications

Exploring the aerosol-cloud transition zone from advanced all-sky camera observations

Exploring the aerosol-cloud transition zone from advanced all-sky camera observations

Machine learning clustering of cloud regimes using synergetic ground-based remote sensing observations

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