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J. K. Angell

, Silver Spring Metro Center 2, Room 9358, 1325 EastWest Highway, Silver Spring, MD 20910.in the low stratosphere to a cooling of about 1.5-C inthe high stratosphere over the 6-year interval. The use of lidar to estimate stratospheric and mesospheric temperature changes was first discussed indetail by Hauchecorne and Chanin (1980), whoshowed good agreement between lidar and rocketsondeprofiles up to a height of 50 kin. In a later paper(Chanin et al. 1987), lidar-derived temperature trendsfrom 1979

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Michael S. Town, Von P. Walden, and Stephen G. Warren

1. Introduction In spite of nearly 50 yr of routine weather observations at South Pole Station, cloud cover over the South Pole is still not well known. Estimates of cloud cover from visual observations are poor during the polar night because of the high frequency of optically thin clouds (through which stars can be seen) and inadequate moonlight ( Hahn et al. 1995 ). It is also difficult to determine cloud cover from satellite data over the Poles because of the small contrast in both albedo

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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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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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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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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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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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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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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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