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Chao Xu, Yaoming Ma, Jiehua Ma, Chao You, and Huijun Wang

dust source regions and with large-scale atmospheric circulation variability. Fig . 1. The topography (units: m) of the TP and its surrounding regions. The gray lines show the core regions. A indicates the Arabian Peninsula, B indicates central Asia, C indicates the northwest Indian Peninsula, and D indicates the Tarim Basin. 2. Methods and data a. CALIPSO data Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) provides the first multiyear global view of the vertical

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Kevin E. Trenberth and John T. Fasullo

on the methodologies and sensors used and therefore differences are large between ISCCP and High Resolution Infrared Radiation Sounder (HIRS) ( Wylie et al. 2005 ), while new observations from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) ( Chepfer et al. 2008 ) reveal other sources of bias. Perhaps more importantly, modeled and observed clouds are not strict analogs. In instances, models report the existence of clouds with minute liquid and ice water

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Dana E. Lane, Kristen Goris, and Richard C. J. Somerville

linearly dependent on cloud size. 1) Cloud-base height At the SGP site, several instruments measure cloud-base height. For this study, the Belfort Laser Ceilometer (BLC), a Vaisala ceilometer, and the Micropulse Lidar (MPL) were used. The BLC and MPL instruments are active, pulse-mode lidars that resolve the height of the cloud base using the return-signal strength. The MPL continuously transmits a 2500-Hz pulse, averages 1 min of observations, and reports the cloud-base height in 300-m bins

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Wouter Greuell, Erik van Meijgaard, Nicolas Clerbaux, and Jan Fokke Meirink

-SAF) algorithm theoretical basis document (ATBD) available online at ]. Cloud-top height has been validated ( Derrien and Le Gléau 2010 ) with 1 yr of observations based on lidar and radar signals in Palaiseau (France). Standard deviations are approximately 1 km, both for opaque and semitransparent clouds. On the assumption of a temperature lapse rate of −7 K km −1 , this corresponds to an uncertainty estimate in CTT of 7 K. For the present study we obtained hourly, instantaneous data of

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A. Bodas-Salcedo, P. G. Hill, K. Furtado, K. D. Williams, P. R. Field, J. C. Manners, P. Hyder, and S. Kato

shortwave (SW) reflected radiation model errors according to cloud regimes. The cloud regimes are defined using the cloud clustering algorithm developed by Williams and Webb (2009) , applied to model data from phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012 ) and observations from the International Satellite Cloud Climatology Project (ISCCP; Rossow and Schiffer 1999 ). Using additional information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite

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Jean-Jacques Morcrette

quantities produced by the parameterizations of the physical processes is archived, which can then be compared to observations not assimilated in the analysis process. Among other radiative fluxes and heating rates, one such parameter is the longwave (LW) radiation at the surface, which mainly depends on the temperature and water vapor distribution in the planetary boundary layer and on the presence of clouds in the first few kilometers above the surface. Various authors have aimed at deriving this

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Virendra P. Ghate, Bruce A. Albrecht, Christopher W. Fairall, and Robert A. Weller

et al. (2008) using CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The annual cycle of CF reported by Klein and Hartmann (1993) peaks at about 70% in September–November (SON) and has a minimum of about 40% in DJF. It is difficult, however, to compare the ORS-derived annual cycle of CF to that reported by Klein and Hartmann (1993) because of the differences in temporal resolution, spatial resolution, and the definition of CF between the two

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Yi Huang, Steven T. Siems, Michael J. Manton, Daniel Rosenfeld, Roger Marchand, Greg M. McFarquhar, and Alain Protat

than in COT (following the power relationships). As such, disproportionally larger uncertainties are expected for N d compared to other retrieved variables. Note that while the 80% adiabatic assumption may cause some temperature-dependent biases, its contribution to the overall error budget is relatively small. 3) CALIPSO The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) satellite carries the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), a nadir

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A. Dommo, N. Philippon, Derbetini A. Vondou, G. Sèze, and R. Eastman

the land surface, and their type (morphology) is determined by trained observers. The three products are 1) the cloud type (CT) product processed by the Satellite Application Facility for Supporting Nowcasting and very short range forecasting (SAFNWC) from MSG retrievals, 2) the global climate model–oriented Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) cloud product issued from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) retrievals, and 3) the

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Kirstie Stramler, Anthony D. Del Genio, and William B. Rossow

nature of clouds embedded in each state, then document their influence on the surface in the following subsection. Over the SHEBA winter, 67% of the hourly NetLW observations corresponded to the presence of cloud. For NetLW ≈ 0 W m −2 , 100% of the MMCR–lidar observations indicate the presence of cloud, yet 51% of the NetLW ≈ −40 W m −2 observations were cloudy, as well. As noted in section 3a , there is a 13-K T sfc discrepancy (250 versus 237 K) between the 0 and −40 W m −2 NetLW modes. When

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