Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations
Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Rad...
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| Veröffentlicht in: | Journal of climate 2015-06, Vol.28 (11), p.4373-4389 |
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| creator | Sun, Bomin Free, Melissa Yoo, Hye Lim Foster, Michael J. Heidinger, Andrew Karlsson, Karl-Göran |
| description | Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of −0.40% decade−1, and satellite products show larger downward trends ranging from −0.55% to −5.00% decade−1for 1984–2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables. |
| doi_str_mv | 10.1175/JCLI-D-14-00805.1 |
| format | Article |
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The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of −0.40% decade−1, and satellite products show larger downward trends ranging from −0.55% to −5.00% decade−1for 1984–2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables.</description><identifier>ISSN: 0894-8755</identifier><identifier>EISSN: 1520-0442</identifier><identifier>DOI: 10.1175/JCLI-D-14-00805.1</identifier><language>eng</language><publisher>Boston: American Meteorological Society</publisher><subject>Advanced very high resolution radiometers ; Albedo ; Algorithms ; Archives & records ; Climate change ; Climate cycles ; Climate monitoring ; Climatology ; Cloud albedo ; Cloud climatology ; Cloud cover ; Clouds ; Computer centers ; Correlation ; Correlations ; Daily temperatures ; Datasets ; Homogeneity ; Meteorological satellites ; Meteorology ; Monitoring ; Precipitation ; Radiation ; Radiation-cloud interactions ; Satellites ; Seasons ; Solar radiation ; Stability analysis ; Statistical analysis ; Time series ; Time zones ; Trends ; Variability ; Weather ; Weather station data ; Weather stations</subject><ispartof>Journal of climate, 2015-06, Vol.28 (11), p.4373-4389</ispartof><rights>2015 American Meteorological Society</rights><rights>Copyright American Meteorological Society Jun 1, 2015</rights><rights>Copyright American Meteorological Society 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-c8cec4e463bf61f7c96987645c5ecd6217850bcecc734b6cd6ff71dc6095194b3</citedby><cites>FETCH-LOGICAL-c399t-c8cec4e463bf61f7c96987645c5ecd6217850bcecc734b6cd6ff71dc6095194b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26195144$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26195144$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,777,781,800,3668,27905,27906,57998,58231</link.rule.ids></links><search><creatorcontrib>Sun, Bomin</creatorcontrib><creatorcontrib>Free, Melissa</creatorcontrib><creatorcontrib>Yoo, Hye Lim</creatorcontrib><creatorcontrib>Foster, Michael J.</creatorcontrib><creatorcontrib>Heidinger, Andrew</creatorcontrib><creatorcontrib>Karlsson, Karl-Göran</creatorcontrib><title>Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations</title><title>Journal of climate</title><description>Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of −0.40% decade−1, and satellite products show larger downward trends ranging from −0.55% to −5.00% decade−1for 1984–2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables.</description><subject>Advanced very high resolution radiometers</subject><subject>Albedo</subject><subject>Algorithms</subject><subject>Archives & records</subject><subject>Climate change</subject><subject>Climate cycles</subject><subject>Climate monitoring</subject><subject>Climatology</subject><subject>Cloud albedo</subject><subject>Cloud climatology</subject><subject>Cloud cover</subject><subject>Clouds</subject><subject>Computer centers</subject><subject>Correlation</subject><subject>Correlations</subject><subject>Daily temperatures</subject><subject>Datasets</subject><subject>Homogeneity</subject><subject>Meteorological 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Lim</au><au>Foster, Michael J.</au><au>Heidinger, Andrew</au><au>Karlsson, Karl-Göran</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations</atitle><jtitle>Journal of climate</jtitle><date>2015-06-01</date><risdate>2015</risdate><volume>28</volume><issue>11</issue><spage>4373</spage><epage>4389</epage><pages>4373-4389</pages><issn>0894-8755</issn><eissn>1520-0442</eissn><abstract>Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of −0.40% decade−1, and satellite products show larger downward trends ranging from −0.55% to −5.00% decade−1for 1984–2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables.</abstract><cop>Boston</cop><pub>American Meteorological Society</pub><doi>10.1175/JCLI-D-14-00805.1</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record> |
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| source | American Meteorological Society; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Jstor Complete Legacy |
| subjects | Advanced very high resolution radiometers Albedo Algorithms Archives & records Climate change Climate cycles Climate monitoring Climatology Cloud albedo Cloud climatology Cloud cover Clouds Computer centers Correlation Correlations Daily temperatures Datasets Homogeneity Meteorological satellites Meteorology Monitoring Precipitation Radiation Radiation-cloud interactions Satellites Seasons Solar radiation Stability analysis Statistical analysis Time series Time zones Trends Variability Weather Weather station data Weather stations |
| title | Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations |
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