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Shu-peng Ho, Liang Peng, Richard A. Anthes, Ying-Hwa Kuo, and Hsiao-Chun Lin

observations from lidar and passive instruments. Sensors, systems, and next-generation satellites VI, H. Fujisada, et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 481), 419 – 426 , doi: 10.1117/12.462519 . Powell , K. A. , and Coauthors , 2009 : CALIPSO lidar calibration algorithms. Part I: Nighttime 532-nm parallel channel and 532-nm perpendicular channel . J. Atmos. Oceanic Technol. , 26 , 2015 – 2033 , doi: 10.1175/2009JTECHA1242.1 . Powell , K. A. , M. A

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A. Bodas-Salcedo, K. D. Williams, M. A. Ringer, I. Beau, J. N. S. Cole, J.-L. Dufresne, T. Koshiro, B. Stevens, Z. Wang, and T. Yokohata

in the CMIP5 ensemble of atmosphere-only models. Then, section 4 presents the results of the cyclone composite for a subset of models and looks at the role of cyclones in the RSR climatological biases. Section 5 separates the radiation biases within the cyclone composite in cloud regimes, and section 6 uses information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) lidar to understand the vertical structure of clouds in the regime that dominates

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William D. Collins, Junyi Wang, Jeffrey T. Kiehl, Guang J. Zhang, Daniel I. Cooper, and William E. Eichinger

the R/V Vickers. High-resolution profiles of water vapor mixing ratio were also derived from a Raman lidar system on the R/V Vickers. The R/V Vickers traversed the CEPEX experimental domain as it sailed from 160°E to 160°W at 2°S. A detailed description of the location and times of the aircraft and ship observations is given in Williams (1993) . The buoy data used in this study are from the TOGA–TAO buoy array in the equatorial Pacific ( McPhaden 1993 ). For the 31 buoys from 10°N to 10°S

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Kuan-Man Xu and Anning Cheng

), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ; Winker et al. 2010 ), Clouds and the Earth’s Radiant Energy System (CERES; Wielicki et al. 1996 ), and Moderate Resolution Imaging Spectroradiometer (MODIS; King et al. 1992 ) data product (C3M; Kato et al. 2011 ). The vertical structures of simulated clouds were still very different from those observed. These differences were mainly related to the insufficient model resolution used in SPCAM–IPHOC. The host GCM, the

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Anthony E. Morrison, Steven T. Siems, and Michael J. Manton

a fractional cloud cover of 70%–90% ( Mace et al. 2007 ). Direct knowledge of these clouds is relatively limited as routine in situ observations are unavailable. High wind speeds and a lack of inhabited land have conspired to deter such observations. Indeed, the last major field experiments that undertook in situ observations of the Southern Ocean boundary layer and the ubiquitous low-level clouds were the first Aerosol Characterization Experiment (ACE-1) in 1995 ( Bates et al. 1998 ) and the

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Steven M. Lazarus, Steven K. Krueger, and Gerald G. Mace

1. Introduction Cloud amount is the most basic measure of cloudiness. Because clouds are a major component of the climate system, it is important to have quality cloud observations. This includes, but is not limited to, knowledge of quantities such as the average cloud amount as a function of cloud type, season, and time of day. Clouds remain a source of uncertainty in climate models. Fluctuations in outgoing longwave radiation have been linked to temporal and spatial variations of cloud amount

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Ryan E. Stanfield, Xiquan Dong, Baike Xi, Aaron Kennedy, Anthony D. Del Genio, Patrick Minnis, and Jonathan H. Jiang

–Moderate Resolution Imaging Spectroradiometer (MODIS) edition 2 cloud results ( Minnis et al. 2011a ) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) profiles ( Kato et al. 2010 ). Model-simulated liquid and ice water paths (LWP and IWP) are compared with CloudSat results ( Austin et al. 2009 ). Simulated precipitable water vapor (PWV) is compared to Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) retrievals ( Wentz 1997 ), while both PWV and

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Terence L. Kubar, Duane E. Waliser, and J-L. Li

attempt to quantify a possible simple metric and parameterization of isolated single-layer low-level clouds. For the observational half of this study, we analyze A-Train satellite data including CloudSat , the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and MODIS. The A-Train constellation ( Stephens et al. 2002 ) has equatorial passing times of 0130 LT and 1330 LT, and we analyze one full year of data. 1) CloudSat CloudSat 2B-Geoprof provides radar reflectivity and

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Juan Huo and Daren Lu

–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) bring us new data to perform scientific analyses on cloud vertical structure and properties. For instance, Sassen et al. (2008) studied cirrus occurrence and optical depth over the global area; Adhikari et al. (2012) analyzed the distribution of cirrus height, thickness, particle effective radius, and other parameters over the Antarctic through CloudSat and CALIPSO data. The aim of the present study is to analyze cirrus cloud

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Catherine M. Naud, Anthony D. Del Genio, Mike Bauer, and William Kovari

-dimensional cloud distributions have until recently been unavailable, as passive remote sensing is limited to cloud-top or cloud-base properties depending on where the observing platform is located. High vertical resolution cloud observations from the National Aeronautic and Space Administration CloudSat ( Stephens et al. 2002 ) and Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO) ( Winker et al. 2009 ) satellites have now become available since summer 2006 and allow for the first time the aggregation of

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