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Catherine M. Naud, Derek J. Posselt, and Susan C. van den Heever

Space Administration (NASA)’s A-Train constellation to investigate the difference in cloud occurrence and precipitation across warm fronts and in the warm sector of Northern and Southern Hemisphere cyclones. Cloud vertical profiles are obtained jointly from the active radar and lidar sensors on CloudSat ( Stephens et al. 2002 ) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) ( Winker et al. 2009 ), and liquid water path, precipitable water vapor, and precipitation

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Aaron M. Adams, Joseph M. Prospero, and Chidong Zhang

, satellite data were restricted to measuring column values or averages. The launch of Cloud–Aerosol Lidar Infrared Pathfinder Satellite Observations ( CALIPSO; Hunt et al. 2009 ) in 2006 enabled aerosol profiling with unprecedented vertical resolution and provides a first glimpse of three-dimensional (3D) aerosol profiles on a near-global scale. Since the launch of CALIPSO , various methods have been employed to utilize the data. CALIPSO data have been used in concert with model simulations and

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Seiji Kato, Norman G. Loeb, Fred G. Rose, David R. Doelling, David A. Rutan, Thomas E. Caldwell, Lisan Yu, and Robert A. Weller

observationally, utilizing A-Train data, including data from the Atmospheric Infrared Sounder (AIRS; Chahine et al. 2006 ), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ; Winker et al. 2010 ), CloudSat ( Stephens et al. 2008 ), and Moderate Resolution Imaging Spectroradiometer (MODIS). Because the adjustment of inputs depends on their uncertainties, the uncertainty estimate plays a critical role in the process. Accordingly, adjustments to surface irradiances are made by a

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Julien P. Nicolas and David H. Bromwich

addressed. Nonetheless, a comprehensive evaluation of AMPS forecasts run with the upgraded model lies beyond the scope of this study. A review of the model’s forecasting performance is first presented for near-surface variables over WA. Since clouds are a prominent signature of moist marine air in the Antarctic atmosphere, AMPS cloud cover is also compared with a recent cloud product derived from spaceborne-lidar measurements. a. Near-surface variables Near-surface observations from West Antarctic

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Yinghui Liu and Jeffrey R. Key

amount and anomaly climatologies, such as active spaceborne radar–lidar and surface observations. CALIPSO cloud amount data from 2006 to 2014 are therefore employed in this study as a complement to the MODIS cloud data. However, while active sensors may be more effective in cloud detection than passive sensors, there are challenges in their use, including difficulties in detecting the low-level clouds ( Zygmuntowska et al. 2012 ; Liu et al. 2012b ) and uncertainties due to limited spatial sampling

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Xiping Zeng, Gail Skofronick-Jackson, Lin Tian, Amber E. Emory, William S. Olson, and Rachael A. Kroodsma

( Stephens et al. 2002 ) intersected the GPM core satellite and thus provided additional cloud information to distinguish the contributions of clouds and Earth’s surface. In other words, the coincidence data of CloudSat and GMI can be used to identify the contribution of clouds to GMI PD, providing information on ice crystal orientation. Lidar data of backscatter and depolarization from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) were used to retrieve ice crystal

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Anthony D. Del Genio, Yonghua Chen, Daehyun Kim, and Mao-Sung Yao

convective clouds and the concurrent moistening of the lower troposphere during the MJO developing phase ( Lin and Johnson 1996 ; Johnson et al. 1999 ; Haertel et al. 2008 ), although C-band too typically underestimates the actual cloud top ( Frederick and Schumacher 2008 ; Wu et al. 2009 ). In this paper, we take advantage of the unique active remote sensing capabilities of the CloudSat ( Stephens et al. 2008 ) and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO

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John M. Haynes, Christian Jakob, William B. Rossow, George Tselioudis, and Josephine Brown

. First, geostationary and polar-orbiting satellite observations of clouds from the ISCCP ( Rossow and Schiffer 1999 ) are used to find large-scale, repeating cloud features over the Southern Ocean (cloud regimes). Next, measurements from the CloudSat radar ( Stephens et al. 2008 ) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) lidar systems ( Winker et al. 2007 ) are matched to these ISCCP-derived cloud regimes and are utilized to derive detailed vertical cloud

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Xiquan Dong, Baike Xi, Aaron Kennedy, Patrick Minnis, and Robert Wood

. The four seasons are winter (DJF), spring (MAM), summer (JJA), and fall (SON). Cloud macrophysical properties such as fraction, height, thickness, and temperature are taken directly from the AMF merged soundings, radar, ceilometer, and lidar measurements. Primary AMF cloud observations and retrievals, and their uncertainties and references, are listed in Table 1 . The centerpiece of the cloud instrument array is the 95 GHz W-band ARM Cloud Radar (WACR) ( Mead and Widener 2005 ). The WACR operates

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Shan Zeng, Frédéric Parol, Jérôme Riedi, Céline Cornet, and François Thieuleux

limitations of these climatological records need to be clearly established to assess potential trends in cloud cover and its associated properties. This calls for the establishment of additional and carefully characterized datasets that can serve as a reference to evaluate longer records derived from series of operational satellites. The Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar ( PARASOL ) and Aqua are among the five sun

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