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  • 1. Sun, Bomin
    et al.
    Free, Melissa
    Yoo, Hye Lim
    Foster, Michael J.
    Heidinger, Andrew
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations2015In: Journal of Climate, ISSN 0894-8755, E-ISSN 1520-0442, Vol. 28, no 11, p. 4373-4389Article in journal (Refereed)
    Abstract [en]

    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(-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.

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