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Simon P. de Szoeke, Christopher W. Fairall, Daniel E. Wolfe, Ludovic Bariteau, and Paquita Zuidema

, warming the ocean surface. Solar warming is only partly compensated by evaporation, thermal infrared radiation, and sensible turbulent heat flux from the ocean surface. Measuring the surface flux components of the heat budget leads to better understanding of the terms influencing the SST, and assessing these terms in models will help us diagnose reasons for SST errors in simulations. Seven ship cruises from 2001 to 2008 have collected climate-quality time series of surface meteorological observations

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Jasmine Rémillard, Pavlos Kollias, Edward Luke, and Robert Wood

indicator of how close to the top-hat representation of updrafts and downdrafts the observed vertical air motion is. Furthermore, it is an indicator of how well high-order closure models can be used to determine the area and mass flux of updrafts. 4. Results a. Cloud and liquid precipitation occurrence Using the radar–lidar synergistic observations, the monthly fraction of time hydrometeors detected in the atmospheric column is shown in Fig. 3a . A weak seasonal cycle is observed with minimum (60

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

more precise satellite-based measurements such as the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar will be extremely useful to evaluate the impact of thin cirrus over lower water clouds on the relationship between phase and cloud-top temperature retrievals. Our results at cloud top are similar to those from previous aircraft observations inside clouds over the British Isles in frontal regions ( Bower et al. 1996 ). They suggested that strong local updrafts in

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Carole J. Hahn, William B. Rossow, and Stephen G. Warren

Organization, 212 pp. Woodruff, S. D., R. J. Slutz, R. L. Jenne, and P. M. Steurer, 1987: A Comprehensive Ocean-Atmosphere Data Set. Bull. Amer. Meteor. Soc., 68, 1239–1250. Wylie, D., P. Piironen, W. Wolf, and E. Eloranta, 1995: Understanding satellite cirrus cloud climatologies with calibrated lidar optical depths. J. Atmos. Sci., 52, 4327–4343. Fig. 1. (a) Correspondence of cloud types as defined in visual surface observations to combinations of optical thickness τ and cloud-top pressure

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John E. Walsh, William L. Chapman, and Diane H. Portis

vertical point or transect in the case of a wind-driven cloud field. Retrieval differences between the point-source and gridcell averaging techniques need to be reconciled before one can make direct comparisons between the observed and reanalysis data. Astin et al. (2001) outline a method to quantify confidence intervals on the estimated cloud fractions obtained from point-source transects, and we apply the method here to the lidar observations at Barrow. Confidence intervals for the means are

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Pavlos Kollias and Bruce Albrecht

’s surface. In addition, since fields of fair-weather cumuli often have fractional cloudiness of 25% or more ( Albrecht 1991 ), they can have an important effect on the radiation budget of the surface and the boundary layer (BL; Chertock et al. 1993 ). For climate sensitivity studies, marine boundary layer clouds remain at the center of tropical cloud feedback issues ( Bony and Dufresne 2005 ). However, observations needed to define the percent of the fair-weather cumuli that are active in the moisture

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Chao Xu, Yaoming Ma, Kun Yang, and Chao You

Plateau, InP indicates Indian subcontinent, TB indicates Tarim basin, TP indicates the Tibetan Plateau, PO indicates the Pacific Ocean, and AO indicates the Atlantic Ocean (these abbreviations are used repeatedly in the figures). 2. Data and methodology a. CALIPSO Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) provides new insights into clouds and atmospheric aerosols ( Winker et al. 2007 , 2010 ). CALIOP is an instrument on board the CALIPSO satellite. The CALIPSO

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Gerald G. Mace, Sally Benson, and Erik Vernon

likely associated with an increased frequency of thunderstorm anvil cirrus. Also, owing to the nonlinear character of the radar reflectivity, it is important to realize that the increase may also be due to an increased frequency of warmer or thicker layers that may not show up statistically in Fig. 2d or Fig. 2e . In Table 3 , we compare the height and temperature statistics of ThCi with similar statistics reported by Sassen and Campbell (2001) that were derived using lidar observations over

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Hua Song, Zhibo Zhang, Po-Lun Ma, Steven J. Ghan, and Minghuai Wang

. 2017 ). Perhaps, the most unique satellite instruments in the A-Train are the two active sensors—the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) ( Winker et al. 2009 ) and CloudSat ( Stephens et al. 2002 ). Together they depict a three-dimensional distribution of cloud and aerosols that is impossible for passive sensors. What makes the A-Train cloud observations even more useful is that the observations are collocated and therefore can be easily combined and

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Larry K. Berg and Evgueni I. Kassianov

results shown in Fig. 1 . c. Average CCL In this study, the CCL is defined as the length of a slice of cloud that passes through the cloud radar or lidar beams. This value is the product of the length of time that a cloud is intercepted by the radar or lidar beam and the wind speed at which the cloud is moving through the beam. The length of time over the sensors was simply computed by the number of sequential ARSCL VAP observations that showed a cloud overhead. No adjustment was applied to account

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