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Mark A. Miller, Virendra P. Ghate, and Robert K. Zahn

season rainfall. Like many other under-sampled regions, the Sahel suffers from a scarcity of reliable surface observations and a heavy reliance upon satellite radiation measurements to benchmark GCM performance. Even in data-rich areas the cross-atmosphere radiation flux divergence and its controls are rarely measured with the temporal resolution, completeness, and accuracy required to determine how the controls and the radiative fluxes interact. This study overcomes this obstacle using nearly

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Shannon Mason, Christian Jakob, Alain Protat, and Julien Delanoë

M2 (23%) and M1 (19%). Fig . 10. Instantaneous cloud structure classes displayed according to (top) atmospheric temperature and (middle) height. The bar represents the distribution of known radar–lidar (DARDAR) cloud phase categories within each cloud structure class. To indicate radar signal contamination near the ground, the lowest 1.50 km of the height profiles are stippled. (bottom) The column chart indicates the relative frequency of occurrence of each cloud structure class within the cloud

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Janet M. Intrieri and Matthew D. Shupe

a 2800-km path from 75°N, 143°W to 79°N, 166°W. All observations presented in this paper were gathered as part of the SHEBA program. 2. Diamond dust observations a. Instrumentation and analysis Observations of diamond dust and cloud occurrence, heights, and phase were obtained during SHEBA by a combination of measurements from ground-based lidar and radar. These vertically pointing, range-resolved active remote sensing instruments were developed at the National Oceanic and Atmospheric

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A. Lacour, H. Chepfer, N. B. Miller, M. D. Shupe, V. Noel, X. Fettweis, H. Gallee, J. E. Kay, R. Guzman, and J. Cole

precipitation processes. Thanks to enhanced observational networks put in place over the last decade, we have both in situ ground observations and satellite observations of Greenland clouds and precipitation. From the ground, the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit (ICECAPS; Shupe et al. 2013 ) project has collected cloud profiles, radiative fluxes, and precipitation at Summit since 2010. From space, the lidar CALIOP and radar CloudSat ( Winker et

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Jason M. English, Jennifer E. Kay, Andrew Gettelman, Xiaohong Liu, Yong Wang, Yuying Zhang, and Helene Chepfer

, simulator packages such as the Cloud Feedback Model Intercomparison Project (CFMIP) ( Bony et al. 2011 ) Observation Simulator Package (COSP) ( Bodas-Salcedo et al. 2011 ), including a lidar simulator ( Chepfer et al. 2008 ), are now commonly utilized to enable a more direct comparison of modeled clouds to observations. Assumptions are often made in the parameterizations that apply the simulator algorithms to model output, however, and there continues to be opportunities to improve the accuracy of using

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Peter Kalmus, Matthew Lebsock, and João Teixeira

mean Lagrangian evolution of individual air masses. We limit the domain of our budgets to the atmospheric boundary layer between 35° and 15°N along the GPCI transect. To construct the budgets, we use data from satellite and surface observations and from reanalyses. To determine radiation, precipitation, and cloud fraction (CF), we use satellite data from instruments on three A-Train satellites, CloudSat , Aqua , and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO

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Catherine M. Naud, James F. Booth, and Anthony D. Del Genio

et al. 2011 ). For this task, we use a variety of observations from the NASA Terra and the A-Train: the Terra Moderate Resolution Imaging Spectroradiometer (MODIS; Salomonson et al. 1989 ), the Multiangle Imaging Spectroradiometer (MISR; Diner et al. 1998 ), the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E; Kawanishi et al. 2003 ), the Atmospheric Infrared Sounder (AIRS; Aumann et al. 2003 ), CloudSat ( Stephens et al. 2002 ), and the Cloud–Aerosol Lidar

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Yi Huang, Alain Protat, Steven T. Siems, and Michael J. Manton

budget ( Potter and Cess 2004 ; Mace and Wrenn 2013 ). As such, this constraint per se is insufficient to examine cloud physical processes, which is a major cause of the present uncertainty in cloud feedbacks ( Dufresne and Bony 2008 ). CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ), launched in 2006 as part of the A-Train constellation ( Stephens et al. 2002 ), added crucial active remote sensing capabilities to cloud observations. The 94-GHz cloud

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Louis Rivoire, Thomas Birner, John A. Knaff, and Natalie Tourville

CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements . J. Geophys. Res. , 113 , D00A12 , . 10.1029/2008JD009972 Sassen , K. , Z. Wang , and D. Liu , 2009 : Cirrus clouds and deep convection in the tropics: Insights from CALIPSO and CloudSat . J. Geophys. Res. , 114 , D00H06 , . Schubert , W. H. , and B. D. McNoldy , 2010 : Application of the concepts of Rossby

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A. K. Steiner, F. Ladstädter, W. J. Randel, A. C. Maycock, Q. Fu, C. Claud, H. Gleisner, L. Haimberger, S.-P. Ho, P. Keckhut, T. Leblanc, C. Mears, L. M. Polvani, B. D. Santer, T. Schmidt, V. Sofieva, R. Wing, and C.-Z. Zou

from ground-based observations, specifically radiosonde and lidar measurements. Reference radiosonde stations have been established over the past decade within the GCOS Reference Upper Air Network (GRUAN), adhering to the GCOS climate monitoring principles (e.g., Seidel et al. 2009 ; Bodeker et al. 2016 ). However, such series are still too short for trend retrievals. Gridded radiosonde records ( Haimberger et al. 2012 ) have been updated recently, as well as observations from light detection and

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