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Aaron D. Kennedy, Xiquan Dong, Baike Xi, Patrick Minnis, Anthony D. Del Genio, Audrey B. Wolf, and Mandana M. Khaiyer

data ( Clothiaux et al. 2000 ). Inclusion of the lidar allows for the filtering of insects, which produce a significant reflectivity during the spring and summer seasons over the ARM SGP site. Another source of error in the cloud radar observations is attenuation during heavy precipitation events, which leads to underestimated cloud-top heights. To mitigate this issue, only times are considered when MPL and MMCR cloud-base estimates are available during dry or lightly precipitating periods. This is

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P. Jonathan Gero and David D. Turner

lidar at the site. Long et al. (2009) have examined trends in broadband shortwave irradiance. Michalsky et al. (2010) studied the multiyear trends in aerosol optical depth and its wavelength dependence. At least one AERI has been deployed at the SGP site since 1995, resulting in more than 14 years of downwelling infrared radiance observations. Between 1996 and 2010, more than 800 000 spectra have been collected. For the current analysis, we have selected the 14 yr of AERI observations in the

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Donald Wylie, Edwin Eloranta, James D. Spinhirne, and Steven P. Palm

higher sensitivity and spatial resolution than the sensors used in the previous studies. The Geosciences Laser Altimeter System (GLAS) was launched on the Ice Cloud and Land Elevation Satellite (ICESat) in January 2003. This is a lidar on a polar-orbiting spacecraft collecting global cloud data. The GLAS observations were designed for multidisciplinary earth science research specifically including very high performance cloud and aerosol profiling ( Spinhirne et al. 2005 ). The GLAS measurements were

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Yali Luo, Renhe Zhang, and Hui Wang

the Cloud Profiling Radar (CPR) ( Im et al. 2006 ), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO; Winker et al. 2003 ), fielding the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) ( Winker et al. 2007 ), were inserted into nearly identical orbits, where they joined the A-Train constellation ( Stephens et al. 2002 ) of three other earth-orbiting satellites: Aqua , Aura , and Parasol . The formation of A-Train has made it possible to make a global

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Betty Carlin, Qiang Fu, Ulrike Lohmann, Gerald G. Mace, Kenneth Sassen, and Jennifer M. Comstock

to compare the statistical properties at this spatial and temporal scale to the previous two cases, which do represent a GCM grid box. We see a definite similarity in comparing Fig. 5 and Fig. 6a . Figure 6a shows the probability density and the gamma fit for the MMCR compilation. The mean cloud optical depth, standard deviation of optical depth, and ν were found to be 0.85, 1.10, and 0.60, respectively. b. Lidar observations The final dataset is a lidar compilation. Optical depth was

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Zheng Liu and Axel Schweiger

complete and reliable observations of clouds in the Arctic is a challenging problem. Passive remote sensing instruments on satellites have difficulty distinguishing clouds from the snow and ice surfaces under some conditions and significant errors in cloud detection are possible ( Y. Liu et al. 2010 ). Active remote sensors—the Cloud Profiling Radar (CPR) on CloudSat and the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on CALIPSO in the A-Train constellation—provide detailed vertical

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Elizabeth Berry, Gerald G. Mace, and Andrew Gettelman

( Z ) profiles ( Tanelli et al. 2008 ). CALIOP, the optical lidar on CALIPSO ( Winker et al. 2010 ) provides measurements of attenuated backscatter and depolarization. While the CPR provides information primarily on optically thicker hydrometeor layers, the lidar senses optically thin clouds that are often below the sensitivity of the radar. Taken together, observations from CC provide detailed and unprecedented cloud statistics ( Mace et al. 2009 ; Mace and Wrenn 2013 ). The lidar is

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Tyler J. Thorsen, David M. Winker, and Richard A. Ferrare

relies on modeling (gray) to fill in observational gaps. While not purely observation-based like the other studies, Lacagnina et al. (2017) is the only study to use satellite retrievals [ Polarization and Anisotropy of Reflectances for Atmospheric Sciences Coupled with Observations from a Lidar ( PARASOL ) and Ozone Monitoring Instrument (OMI)] of the SSA, which, as shown above, is a critically important parameter. Retrievals from PARASOL and OMI have poor yields, requiring Lacagnina et al

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Ryan Eastman and Stephen G. Warren

Schweiger et al. (2002) shows a low bias in surface-observed cloud fraction when compared to cloud fraction observed simultaneously by lidar, during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment in the Arctic Ocean. This bias is partially explained by a lack of adequate moonlight in some of the observations, and by the lidar’s inability to distinguish clear-sky ice particle precipitation (“diamond dust”) from clouds. This problem could also exist when comparing surface observations to

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Yunying Li and Minghua Zhang

small spatial scale of Cu and the lack of high-resolution observation, few studies have examined the distribution and variability of Cu over the TP and their physical causes. The purpose of this study is to characterize the climatology and variability of Cu over the TP and explain the physical mechanisms that cause them. The fine-resolution cloud-type product of CloudSat ( Stephens et al. 2008 ) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ; Winker et al

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