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Stubenrauch, CJ; Rossow, WB; Kinne, S; Ackerman, S; Cesana, G; Chepfer, H; Di Girolamo, L; Getzewich, B; Guignard, A; Heidinger, A; Maddux, BC; Menzel, WP; Minnis, P; Pearl, C; Platnick, S; Poulsen, C; Riedi, J; Sun-Mack, S; Walther, A; Winker, D; Zeng, S; Zhao, G (2013). Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 94(7), 1031-1049.

Abstract
Clouds cover about 70% of Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the entire globe and across the wide range of spatial and temporal scales that compose weather and climate variability. Satellite cloud data records now exceed more than 25 years; however, climate data records must be compiled from different satellite datasets and can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors and retrieval methods. The Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel (GEWEX Data and Assessment Panel since 2011), provides the first coordinated intercomparison of publicly available, standard global cloud products (gridded monthly statistics) retrieved from measurements of multispectral imagers (some with multiangle view and polarization capabilities), IR sounders, and lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature, or altitude), cloud thermodynamic phase, and cloud radiative and bulk microphysical properties (optical depth or emissivity, effective particle radius, and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by the inherent instrument measurement capability for detecting and/or identifying optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. The monthly gridded database presented here facilitates further assessments, climate studies, and the evaluation of climate models.

DOI:
10.1175/BAMS-D-12-00117.1

ISSN:

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