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Casey J. Wall, Joel R. Norris, Blaž Gasparini, William L. Smith Jr., Mandana M. Thieman, and Odran Sourdeval

satellite, the Cloud–Aerosol Lidar with Orthogonal Polarization instrument on board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) satellite, and the Cloud Profiling Radar on board the CloudSat satellite. Coincident measurements from these sensors are used to derive two of the datasets that are analyzed in this study. We analyze vertical profiles of cloud fraction and irradiance from the CALIPSO - CloudSat -CERES-MODIS (CCCM) Merged Product version RelB1 ( CERES

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Shuyan Liu and Xin-Zhong Liang

, including mesoscale convection and low-level jets (LLJs). Observations in both CASES and FIFE were made over plain surfaces under fair-weather conditions at a high temporal (1–3 h) resolution. They also are ideal for depicting the representative PBLH diurnal evolution. Soundings from CASES were selected to illustrate physical definitions and numerical procedures for diagnosing PBLH of different regimes or phases. Given the availability of concurrent PBLH retrievals from lidar and sodar measurements

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R. A. Roebeling and E. van Meijgaard

from passive imagers. The methods that have been developed to retrieve IWP from ground-based measurements are either only applicable to thin cirrus clouds or not accurate enough for validation studies. Illingworth et al. (2007) found that the methods based on radar reflectivities and lidar backscatter observations ( van Zadelhoff et al. 2007 ; Donovan 2003 ) are accurate but only applicable to thin cirrus clouds (IWP < 50 g m −2 ). On the other hand, the methods that can be applied to thick

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Mikhail Ovtchinnikov, Thomas Ackerman, Roger Marchand, and Marat Khairoutdinov

in this study come from two ARM sites: the tropical western Pacific (TWP) site located on the island of Nauru (0.521°S, 166.916°E) and the Southern Great Plain (SGP) Central Facility site in north-central Oklahoma (36.617°N, 97.50°W). Cloud fraction statistics are derived from vertically pointing millimeter wave (35 GHz) cloud radar and lidar observations using the cloud masking algorithm of Clothiaux et al. (2000) . The radar detects hydrometeors with reflectivities in the range of

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Xiquan Dong, Baike Xi, and Patrick Minnis

between the random overlap–corrected surface observations and the radar–lidar results and suggests that the random overlap correction may be too extreme (e.g., Hogan and Illingworth 2000 ). Figure 7 compares the surface SW fluxes and CRFs from this study with those from other previous research that used empirical parameterizations and satellite observations. The monthly downwelling all-sky SW fluxes ( Fig. 1b ) are compared with those of Gautier and Landsfeld (1997) , who used GOES-7 data taken

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Graeme L. Stephens, Martin Wild, Paul W. Stackhouse Jr., Tristan L’Ecuyer, Seiji Kato, and David S. Henderson

cloudiness and atmospheric state parameters are other examples of synthesis products. One uses the updated version of the radiative flux product (2B-FLXHR) product ( L’Ecuyer et al. 2008 ) that includes improved depictions of clouds through the combination of lidar and radar observations (Henderson et al. 2011, manuscript submitted to J. Appl. Meteor. Climatol. ). Vertical distributions of liquid and ice cloud water contents and effective radii from the level-2 cloud water content product (2B-CWC) are

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Junhong Wang, William B. Rossow, and Yuanchong Zhang

boundary layer cloud-base heights are underestimated by 200–300 m in the raobs results ( Wang et al. 1999 ). This difficulty should be less significant in drier continental boundary layers. The second problem is that the rawinsonde humidity sensor loses sensitivity at very cold temperatures. Comparison with a long record of lidar observations collected at Salt Lake City by Sassen and Cho (1992) suggests that the raobs tend to miss about 20%–30% of the highest-level clouds, especially in wintertime

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Dominique Bouniol, Rémy Roca, Thomas Fiolleau, and D. Emmanuel Poan

and lidar) is in the expected range for tropical cirrus. Table 1. Number of MCS subregions [convective (conv), stratiform (strat), and cirriform (cirri)] and the corresponding number of profiles sampled at each normalized life step (between 1 and 10) for the three geographical areas. Boldface numbers in the table highlight the subregions that were sampled in less than 20 independent MCSs. Fig . 1. Mean cloud-top height evolution over the MCS life cycle retrieved from radar data (solid line) and

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Ali Behrangi, Alex Gardner, John T. Reager, Joshua B. Fisher, Daqing Yang, George J. Huffman, and Robert F. Adler

: Global distribution of cirrus clouds from CloudSat/Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements . J. Geophys. Res. , 113 , D00A12 , https://doi.org/10.1029/2008JD009972 . 10.1029/2008JD009972 Scaff , L. , D. Yang , Y. Li , and E. Mekis , 2015 : Inconsistency in precipitation measurements across the Alaska–Yukon border . Cryosphere , 9 , 2417 – 2428 , https://doi.org/10.5194/tc-9-2417-2015 . 10.5194/tc-9-2417-2015 Schneider , U. , A

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Ryan C. Scott, Dan Lubin, Andrew M. Vogelmann, and Seiji Kato

Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) ( Winker et al. 2009 ) during 2007–10, when satellite data are available in collocated form. Section 2 describes the satellite cloud and surface radiation measurements and evaluates the performance of satellite-modeled radiative fluxes. Section 3 examines the distribution and phase of clouds over the WAIS and major ice shelves (Ross and Ronne–Filchner) and

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