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Thorwald H. M. Stein, Julien Delanoë, and Robin J. Hogan

sensitivities ( Waliser et al. 2009 ). The A-Train constellation of satellites takes various measurements of ice clouds ( Stephens et al. 2002 ). It started with the launch of Aqua in 2002, carrying the Moderate Resolution Imaging Spectroradiometer (MODIS), which retrieves cloud optical properties using shortwave and infrared radiances. In 2006, Aqua was joined by CloudSat and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) ( Winker et al. 2003 ), providing

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Ryan Reynolds Neely III and Jeffrey P. Thayer

effect to date in this region ( Houghton et al. 2001 ; Blanchet and Girard 1995 ; Solomon et al. 2007 ). More observations are particularly needed to understand the complex set of feedback cycles that involve water vapor as the Arctic atmosphere responds to climate change. The Arctic Lidar Technology (ARCLITE) facility, a Rayleigh/Mie/Raman lidar system, has been in operation at the Sondrestrom Upper Atmospheric Research Facility, near the town of Kangerlussuaq, Greenland (67.0°N, 50.9°W), since

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Christine C. W. Nam and Johannes Quaas

are able to provide global coverage, are a particularly valuable source of data for evaluations of general circulation models. This paper aims to evaluate how well the ECHAM5 atmospheric GCM represents clouds and precipitation in the present climate using the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) and CloudSat satellites. CALIPSO and CloudSat are polar-orbiting satellites hosting active lidar and radar instruments. Together they provide the first

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Karoliina Hämäläinen, Elena Saltikoff, Otto Hyvärinen, Ville Vakkari, and Sami Niemelä

calibration methods combined with a new type of ground-based remote sensing observations.The calibration method (BCT) was chosen based on our previous experiences in the GLAMEPS consortium ( GLAMEPS 2015 ), in which the chosen method has been used for the calibration of the operational forecasting model. The method used in this paper has been found to be efficient in correcting the 10-m winds. The lidar network of the Finnish Meteorological Institute (FMI) includes four observation locations operating in

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D. D. Turner, R. A. Ferrare, V. Wulfmeyer, and A. J. Scarino

, this generally occurs in the mid- to late afternoon, after the mixed layer has reached its maximum depth. Afternoons that are affected by synoptic events such as frontal or dryline passages are not analyzed with this lidar technique. b. Twin Otter diode laser hygrometer ARM has had a focus on liquid water clouds with low optical depths for many years due to the difficulty in characterizing these clouds from ground-based observations and the importance of these clouds in radiative closure, aerosol

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Jennifer D. Hegarty, Jasper Lewis, Erica L. McGrath-Spangler, John Henderson, Amy Jo Scarino, Philip DeCola, Richard Ferrare, Micheal Hicks, Rebecca D. Adams-Selin, and Ellsworth J. Welton

deployed at the Goddard Space Flight Center (GSFC) since 2001 ( Lewis et al. 2013 ). Additionally, airborne lidars such as the High Spectral Resolution Lidar (HSRL) have been used to estimate PBLHs during field measurements campaigns (e.g., Lewis et al. 2010 ; Baker et al. 2013 ; Scarino et al. 2014 ). Since 2006, the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) satellite has been providing

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Yann Blanchard, Jacques Pelon, Edwin W. Eloranta, Kenneth P. Moran, Julien Delanoë, and Geneviève Sèze

present the observations from active instruments (lidar and radar) as well as satellite and surface data. Statistical analyses that are based on independent datasets are summarized in a third section looking to annual, seasonal, and monthly variations of cloud occurrence. On the basis of the coincident data, joint statistics of cloud cover and vertical distribution are given in section 4 . The section also highlights the limits of each observational dataset. We discuss results of the comparisons and

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Kathrin Folger and Martin Weissmann

). Folger and Weissmann (2014) proposed to assign AMVs from Meteosat-9 and Meteosat-10 to vertical layers beneath lidar-derived cloud-top heights. The evaluation of this height reassignment using nearby operational radiosondes resulted in a significant reduction of AMV wind errors. The aim of the present paper is to further elaborate this concept and to overcome the limitations of spatially and temporally rare radiosonde observations by using model equivalents (O-B statistics; see, e.g., Cotton

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Laura D. Riihimaki, Sally A. McFarlane, and Jennifer M. Comstock

tropical midlevel clouds. Previous studies indicate that tropical midlevel clouds are different than their counterparts in midlatitudes and polar regions in their properties, including frequency, thickness, and phase. Zhang et al. (2010) found higher frequencies of thin midlevel clouds in Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) night overpasses than during daytime overpasses. This difference was substantially higher in the tropics than in other regions

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Adrien Lacour, Helene Chepfer, Matthew D. Shupe, Nathaniel B. Miller, Vincent Noel, Jennifer Kay, David D. Turner, and Rodrigo Guzman

radiation for cloud detection, which compromises the accuracy of cloud detection over iced or snow-covered surfaces ( Liu et al. 2010 ; Stubenrauch et al. 2012 ). Available since 2006, active remote sensing observations from spaceborne radar and lidar provide the opportunity for a cloud detection that is robustly independent of surface characteristics ( Kay and L’Ecuyer 2013 ; Mioche et al. 2015 ). Spaceborne lidar observations also provide the opportunity to accurately retrieve cloud phase ( Cesana

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