Cloud observations in Switzerland using hemispherical sky cameras

We present observations of total cloud cover and cloud type classification results from a sky camera network comprising four stations in Switzerland. In a comprehensive intercomparison study, records of total cloud cover from the sky camera, long‐wave radiation observations, Meteosat, ceilometer, an...

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Veröffentlicht in:Journal of geophysical research. Atmospheres 2015-01, Vol.120 (2), p.695-707
Hauptverfasser: Wacker, Stefan, Gröbner, Julian, Zysset, Christoph, Diener, Laurin, Tzoumanikas, Panagiotis, Kazantzidis, Andreas, Vuilleumier, Laurent, Stöckli, Reto, Nyeki, Stephan, Kämpfer, Niklaus
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container_end_page 707
container_issue 2
container_start_page 695
container_title Journal of geophysical research. Atmospheres
container_volume 120
creator Wacker, Stefan
Gröbner, Julian
Zysset, Christoph
Diener, Laurin
Tzoumanikas, Panagiotis
Kazantzidis, Andreas
Vuilleumier, Laurent
Stöckli, Reto
Nyeki, Stephan
Kämpfer, Niklaus
description We present observations of total cloud cover and cloud type classification results from a sky camera network comprising four stations in Switzerland. In a comprehensive intercomparison study, records of total cloud cover from the sky camera, long‐wave radiation observations, Meteosat, ceilometer, and visual observations were compared. Total cloud cover from the sky camera was in 65–85% of cases within ±1 okta with respect to the other methods. The sky camera overestimates cloudiness with respect to the other automatic techniques on average by up to 1.1 ± 2.8 oktas but underestimates it by 0.8 ± 1.9 oktas compared to the human observer. However, the bias depends on the cloudiness and therefore needs to be considered when records from various observational techniques are being homogenized. Cloud type classification was conducted using the k‐Nearest Neighbor classifier in combination with a set of color and textural features. In addition, a radiative feature was introduced which improved the discrimination by up to 10%. The performance of the algorithm mainly depends on the atmospheric conditions, site‐specific characteristics, the randomness of the selected images, and possible visual misclassifications: The mean success rate was 80–90% when the image only contained a single cloud class but dropped to 50–70% if the test images were completely randomly selected and multiple cloud classes occurred in the images. Key Points Total cloud cover from various methods is in 65–85% within ±1 okta Sky camera overestimates cloudiness with respect to other automatic methods Clouds can be correctly classified in 50–90% of cases using sky cameras
doi_str_mv 10.1002/2014JD022643
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In a comprehensive intercomparison study, records of total cloud cover from the sky camera, long‐wave radiation observations, Meteosat, ceilometer, and visual observations were compared. Total cloud cover from the sky camera was in 65–85% of cases within ±1 okta with respect to the other methods. The sky camera overestimates cloudiness with respect to the other automatic techniques on average by up to 1.1 ± 2.8 oktas but underestimates it by 0.8 ± 1.9 oktas compared to the human observer. However, the bias depends on the cloudiness and therefore needs to be considered when records from various observational techniques are being homogenized. Cloud type classification was conducted using the k‐Nearest Neighbor classifier in combination with a set of color and textural features. In addition, a radiative feature was introduced which improved the discrimination by up to 10%. The performance of the algorithm mainly depends on the atmospheric conditions, site‐specific characteristics, the randomness of the selected images, and possible visual misclassifications: The mean success rate was 80–90% when the image only contained a single cloud class but dropped to 50–70% if the test images were completely randomly selected and multiple cloud classes occurred in the images. 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Atmospheres</title><addtitle>J. Geophys. Res. Atmos</addtitle><description>We present observations of total cloud cover and cloud type classification results from a sky camera network comprising four stations in Switzerland. In a comprehensive intercomparison study, records of total cloud cover from the sky camera, long‐wave radiation observations, Meteosat, ceilometer, and visual observations were compared. Total cloud cover from the sky camera was in 65–85% of cases within ±1 okta with respect to the other methods. The sky camera overestimates cloudiness with respect to the other automatic techniques on average by up to 1.1 ± 2.8 oktas but underestimates it by 0.8 ± 1.9 oktas compared to the human observer. However, the bias depends on the cloudiness and therefore needs to be considered when records from various observational techniques are being homogenized. Cloud type classification was conducted using the k‐Nearest Neighbor classifier in combination with a set of color and textural features. In addition, a radiative feature was introduced which improved the discrimination by up to 10%. The performance of the algorithm mainly depends on the atmospheric conditions, site‐specific characteristics, the randomness of the selected images, and possible visual misclassifications: The mean success rate was 80–90% when the image only contained a single cloud class but dropped to 50–70% if the test images were completely randomly selected and multiple cloud classes occurred in the images. 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2169-8996
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subjects Algorithms
Atmospherics
Automation
Cameras
Classification
cloud classification
Cloud cover
cloud detection
Clouds
Geophysics
Meteorology
Sky
sky camera
total cloud cover
title Cloud observations in Switzerland using hemispherical sky cameras
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