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 |
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container_title | Journal of geophysical research. Atmospheres |
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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 |
format | Article |
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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</description><identifier>ISSN: 2169-897X</identifier><identifier>EISSN: 2169-8996</identifier><identifier>DOI: 10.1002/2014JD022643</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Atmospherics ; Automation ; Cameras ; Classification ; cloud classification ; Cloud cover ; cloud detection ; Clouds ; Geophysics ; Meteorology ; Sky ; sky camera ; total cloud cover</subject><ispartof>Journal of geophysical research. Atmospheres, 2015-01, Vol.120 (2), p.695-707</ispartof><rights>2015. The Authors.</rights><rights>2015. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5152-ff73cbbb237cc8d6ec9c5ea1dbd6ce2d227e83d811193e0205345a18e349e8a73</citedby><cites>FETCH-LOGICAL-c5152-ff73cbbb237cc8d6ec9c5ea1dbd6ce2d227e83d811193e0205345a18e349e8a73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2014JD022643$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2014JD022643$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,1411,1427,27903,27904,45553,45554,46387,46811</link.rule.ids></links><search><creatorcontrib>Wacker, Stefan</creatorcontrib><creatorcontrib>Gröbner, Julian</creatorcontrib><creatorcontrib>Zysset, Christoph</creatorcontrib><creatorcontrib>Diener, Laurin</creatorcontrib><creatorcontrib>Tzoumanikas, Panagiotis</creatorcontrib><creatorcontrib>Kazantzidis, Andreas</creatorcontrib><creatorcontrib>Vuilleumier, Laurent</creatorcontrib><creatorcontrib>Stöckli, Reto</creatorcontrib><creatorcontrib>Nyeki, Stephan</creatorcontrib><creatorcontrib>Kämpfer, Niklaus</creatorcontrib><title>Cloud observations in Switzerland using hemispherical sky cameras</title><title>Journal of geophysical research. 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.
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</description><subject>Algorithms</subject><subject>Atmospherics</subject><subject>Automation</subject><subject>Cameras</subject><subject>Classification</subject><subject>cloud classification</subject><subject>Cloud cover</subject><subject>cloud detection</subject><subject>Clouds</subject><subject>Geophysics</subject><subject>Meteorology</subject><subject>Sky</subject><subject>sky camera</subject><subject>total cloud cover</subject><issn>2169-897X</issn><issn>2169-8996</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNqN0L1OwzAUBeAIgQSCbjxAJBYGAv6JHXuEAoWqAgQFulmOc0td0qTYDaU8PYaiCjEgvNjDd66ObxTtYnSIESJHBOG0e4oI4Sldi7YI5jIRUvL11TsbbEYt78coHIFoytKt6Lhd1k0R17kH96pntq58bKv4bm5n7-BKXRVx4231FI9gYv10BM4aXcb-eREbPQGn_U60MdSlh9b3vR3dn5_12xdJ77pz2T7uJYZhRpLhMKMmz3NCM2NEwcFIw0DjIi-4AVIQkoGghcAYSwqIIBYKaiyAphKEzuh2tL-cO3X1SwN-pkIhA2XoCHXjFeZcilRQjP5DU4KI-KJ7v-i4blwVPhIUY0SiMDGog6UyrvbewVBNnZ1ot1AYqc_tq5_bD5wu-dyWsPjTqm7n9pRhmZKQSpYp62fwtkpp96x4RjOmHq86itOb_uCh3VUn9AOdM5OU</recordid><startdate>20150127</startdate><enddate>20150127</enddate><creator>Wacker, Stefan</creator><creator>Gröbner, Julian</creator><creator>Zysset, Christoph</creator><creator>Diener, Laurin</creator><creator>Tzoumanikas, Panagiotis</creator><creator>Kazantzidis, Andreas</creator><creator>Vuilleumier, Laurent</creator><creator>Stöckli, Reto</creator><creator>Nyeki, Stephan</creator><creator>Kämpfer, Niklaus</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>24P</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope></search><sort><creationdate>20150127</creationdate><title>Cloud observations in Switzerland using hemispherical sky cameras</title><author>Wacker, Stefan ; Gröbner, Julian ; Zysset, Christoph ; Diener, Laurin ; Tzoumanikas, Panagiotis ; Kazantzidis, Andreas ; Vuilleumier, Laurent ; Stöckli, Reto ; Nyeki, Stephan ; Kämpfer, Niklaus</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5152-ff73cbbb237cc8d6ec9c5ea1dbd6ce2d227e83d811193e0205345a18e349e8a73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Atmospherics</topic><topic>Automation</topic><topic>Cameras</topic><topic>Classification</topic><topic>cloud classification</topic><topic>Cloud cover</topic><topic>cloud detection</topic><topic>Clouds</topic><topic>Geophysics</topic><topic>Meteorology</topic><topic>Sky</topic><topic>sky camera</topic><topic>total cloud cover</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wacker, Stefan</creatorcontrib><creatorcontrib>Gröbner, Julian</creatorcontrib><creatorcontrib>Zysset, Christoph</creatorcontrib><creatorcontrib>Diener, Laurin</creatorcontrib><creatorcontrib>Tzoumanikas, Panagiotis</creatorcontrib><creatorcontrib>Kazantzidis, Andreas</creatorcontrib><creatorcontrib>Vuilleumier, Laurent</creatorcontrib><creatorcontrib>Stöckli, Reto</creatorcontrib><creatorcontrib>Nyeki, Stephan</creatorcontrib><creatorcontrib>Kämpfer, Niklaus</creatorcontrib><collection>Istex</collection><collection>Wiley Online Library Open Access</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Journal of geophysical research. Atmospheres</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wacker, Stefan</au><au>Gröbner, Julian</au><au>Zysset, Christoph</au><au>Diener, Laurin</au><au>Tzoumanikas, Panagiotis</au><au>Kazantzidis, Andreas</au><au>Vuilleumier, Laurent</au><au>Stöckli, Reto</au><au>Nyeki, Stephan</au><au>Kämpfer, Niklaus</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cloud observations in Switzerland using hemispherical sky cameras</atitle><jtitle>Journal of geophysical research. Atmospheres</jtitle><addtitle>J. Geophys. Res. Atmos</addtitle><date>2015-01-27</date><risdate>2015</risdate><volume>120</volume><issue>2</issue><spage>695</spage><epage>707</epage><pages>695-707</pages><issn>2169-897X</issn><eissn>2169-8996</eissn><abstract>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</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/2014JD022643</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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source | Wiley Free Content; Wiley Online Library Journals Frontfile Complete; Alma/SFX Local Collection |
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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