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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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Daniel Leuenberger, Alexander Haefele, Nadja Omanovic, Martin Fengler, Giovanni Martucci, Bertrand Calpini, Oliver Fuhrer, and Andrea Rossa

( Reichardt et al. 2012 ), and the Raman Lidar for Meteorological Observations (RALMO) operated by MeteoSwiss ( Dinoev et al. 2013 ). We consider RALMO representative of state-of-the-art automated Raman lidars and a more detailed description is provided in the following section. Advances in laser technology have paved the way for commercial instruments, which are nowadays available ( Lange et al. 2019 ; Fréville et al. 2015 ). Though operational deployment of such commercial instruments is still very

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P. C. S. Devara, P. E. Raj, K. K. Dani, G. Pandithurai, M. C. R. Kalapureddy, S. M. Sonbawne, Y. J. Rao, and S. K. Saha

parameter, and not many observations are available in the literature. In the present paper, we have made an attempt to infer the aerosol shape qualitatively from the lidar depolarization ratio. Also, by utilizing the unique facility (the switching of the state of polarization of the laser pulse energy between parallel and perpendicular) available with the DPMPL, datasets are being collected to undertake detailed analyses of cloud composition [such as determination of water, ice, or mixed phase and the

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