YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • AMS
    • Journal of Climate
    • View Item
    •   YE&T Library
    • AMS
    • Journal of Climate
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations

    Source: Journal of Climate:;2015:;volume( 028 ):;issue: 011::page 4373
    Author:
    Sun, Bomin
    ,
    Free, Melissa
    ,
    Yoo, Hye Lim
    ,
    Foster, Michael J.
    ,
    Heidinger, Andrew
    ,
    Karlsson, Karl-Göran
    DOI: 10.1175/JCLI-D-14-00805.1
    Publisher: American Meteorological Society
    Abstract: ariability 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?1 for 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.
    • Download: (2.885Mb)
    • Show Full MetaData Hide Full MetaData
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4223863
    Collections
    • Journal of Climate

    Show full item record

    contributor authorSun, Bomin
    contributor authorFree, Melissa
    contributor authorYoo, Hye Lim
    contributor authorFoster, Michael J.
    contributor authorHeidinger, Andrew
    contributor authorKarlsson, Karl-Göran
    date accessioned2017-06-09T17:11:45Z
    date available2017-06-09T17:11:45Z
    date copyright2015/06/01
    date issued2015
    identifier issn0894-8755
    identifier otherams-80918.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4223863
    description abstractariability 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?1 for 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.
    publisherAmerican Meteorological Society
    titleVariability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations
    typeJournal Paper
    journal volume28
    journal issue11
    journal titleJournal of Climate
    identifier doi10.1175/JCLI-D-14-00805.1
    journal fristpage4373
    journal lastpage4389
    treeJournal of Climate:;2015:;volume( 028 ):;issue: 011
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian