B. C. Maddux, G. Zhao, Stefan Kinne, Claudia J. Stubenrauch, Jerome Riedi, William B. Rossow, Caroline Poulsen, Dave Winker, S. Zeng, S. Sun-Mack, Patrick Minnis, Brian Getzewich, Steven Platnick, W. P. Menzel, A. Guignard, Steven A. Ackerman, G. Cesana, Andi Walther, C. Pearl, Andrew K. Heidinger, L. Di Girolamo, Hélène Chepfer, Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École des Ponts ParisTech (ENPC)-École polytechnique (X)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), CREST Institute, City College of New York, New York, NY, United States, Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, Cooperative Institute for Meteorological Satellite Studies (CIMSS), National Oceanic and Atmospheric Administration (NOAA)-University of Wisconsin-Madison-NASA, Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States, Science Systems and Applications, Inc. [Hampton] (SSAI), NOAA Center for Satellite Applications and Research (STAR), NOAA National Environmental Satellite, Data, and Information Service (NESDIS), National Oceanic and Atmospheric Administration (NOAA)-National Oceanic and Atmospheric Administration (NOAA), NASA Langley Research Center [Hampton] (LaRC), NASA Goddard Space Flight Center (GSFC), STFC Rutherford Appleton Laboratory (RAL), Science and Technology Facilities Council (STFC), Laboratoire d’Optique Atmosphérique - UMR 8518 (LOA), Institut national des sciences de l'Univers (INSU - CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), City College of New York [CUNY] (CCNY), City University of New York [New York] (CUNY), University of Wisconsin-Madison-NASA-National Oceanic and Atmospheric Administration (NOAA), University of Illinois at Urbana-Champaign [Urbana], and University of Illinois System
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.