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Lilia Lemus, Lawrie Rikus, C. Martin, and R. Platt

parameterization for optical properties that implicitly included the ice content as a function of cloud temperature based on empirical relationships found in lidar studies. These theoretical and empirical studies show that the cloud water content is a strong function of temperature. On this basis Lemus et al. (1994) derived a simple parameterization of the TCWC using the cloud liquid water and ice contents dataset for several different cloud types from Platt (1994) . The data 1 are shown in Fig. 1 along

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Karen M. Shell, Simon P. de Szoeke, Michael Makiyama, and Zhe Feng

cloudy profiles and, hence, underestimation of the longwave cloud radiative effect ( Feng et al. 2014 ). OLR, readily observed by satellites, is the most varying component of the column radiative divergence. While active-wavelength remote sensing satellite products, such as CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation ( CALIPSO ), provide retrievals of atmospheric heating rates ( Del Genio and Chen 2015 ), more widely available passive-radiance satellite

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Kuan-Man Xu and Anning Cheng

-order turbulence closure in its cloud-resolving model (CRM) component ( Cheng and Xu 2011 ). The upgraded MMF can produce a global- and annual-mean low-cloud amount that is within 5.3% of observations from the merged CloudSat ( Stephens et al. 2002 ), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ; Winker et al. 2010 ), Clouds and the Earth’s Radiant Energy System (CERES; Wielicki et al. 1996 ), and Moderate-Resolution Imaging Spectroradiometer (MODIS; King et al. 1992

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Yiyi Huang, Xiquan Dong, Baike Xi, Erica K. Dolinar, Ryan E. Stanfield, and Shaoyue Qiu

poorly understood over the Arctic ( Curry et al. 1996 ; Shupe and Intrieri 2004 ; Walsh et al. 2009 ). Reanalysis datasets are convenient tools for studying Arctic cloud and radiation interactions, especially in data-sparse regions where in situ observations are difficult to obtain on account of the unique and extreme environments ( Walsh et al. 2009 ). Specifically, a reanalysis combines an unchanging data assimilation scheme and model results with all available observations into a spatially

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K. D. Williams, A. Bodas-Salcedo, M. Déqué, S. Fermepin, B. Medeiros, M. Watanabe, C. Jakob, S. A. Klein, C. A. Senior, and D. L. Williamson

model with a variety of observations for particular meteorological events (e.g., Boyle and Klein 2010 ). In addition, understanding the development of biases as they grow from a well-initialized state can provide significant insight into the origin of these biases, which can be used in the future development of the model (e.g., Williamson et al. 2005 ). Many of the principal sources of model spread in terms of simulating climate and climate change are fast processes (e.g., clouds), so examining

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Mark Aaron Chan and Josefino C. Comiso

, due to strong surface clutter, CPR sensitivities within 1 km above the surface are limited, to the extent that backscatters from the lowest 500 m do not provide useful data. c. CALIOP CALIOP is an active sensor onboard Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) that trails CloudSat by approximately 15 s. It is a near-nadir-viewing, polarization-sensitive, elastic backscatter lidar that uses a pumped neodymium-doped yttrium aluminum garnet (Nd:YAG) laser

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Lei Wang, Yuqing Wang, Axel Lauer, and Shang-Ping Xie

simulated low-level cloud-base and cloud-top heights are compared with the cloud-layer product from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). CALIPSO was launched on 28 April 2006 and is a two-wavelength (532 and 1064 nm) lidar providing high-resolution vertical profiles of aerosols and clouds. Details on CALIPSO and the algorithms used can be found in Winker et al. (2009) . Boundary layer clouds are detected at a resolution of 30 m in the vertical and 333 m in the

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Xiaoyan Wang and Kaicun Wang

mandatory radiosonde measurements of the World Meteorological Organization (WMO) are performed at 0000 and 1200 UTC, but a small number of soundings are obtained at other coordinated universal times (UTCs). It is difficult to derive the diurnal variation of boundary layer development based on twice-daily observations at each station. In this study, we used data from the two most frequent observation times at each station and calculated the long-term annual or seasonal frequency of occurrence of the SBL

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Liping Deng, Sally A. McFarlane, and Julia E. Flaherty

from the Millimeter Wavelength Cloud Radar (MMCR) and micropulse lidar (MPL). Clutter-screened reflectivity from the 35-GHz MMCR (at 90 m, 10-s resolution) and attenuated backscatter from the 532-nm MPL (30 m, 30-s resolution) are averaged to a common temporal (120 s) and vertical (~30 m) grid. A cloud is identified from the radar as any point with reflectivity >−50 dB Z , which may also include precipitation. A cloud is identified from the lidar using the algorithms of Comstock and Sassen (2001

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Hailong Wang, Casey D. Burleyson, Po-Lun Ma, Jerome D. Fast, and Philip J. Rasch

layer of observed liquid stratiform clouds is likely formed through the melting of stratiform ice precipitation followed by cooling and increased stability (e.g., Johnson et al. 1996 ; Riihimaki et al. 2012 ). Satellite observations using lidar have revealed that such thin midlevel clouds are ubiquitous in the tropics and the magnitude of their radiative cooling effect could be as large as the warming effect of cirrus (e.g., Bourgeois et al. 2016 ). Biases in simulating this type of cloud may

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