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Roger Marchand, Nathaniel Beagley, and Thomas P. Ackerman

synoptic fields may not contain sufficient information to effectively capture factors that influence convective initiation or efficiency. We plan to test this hypothesis by applying the classification scheme to other locations including the ARM tropical western Pacific sites and also plan eventually to test the technique using satellite observations [e.g., from the NASA CloudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), Multiangle Imaging Spectroradiometer (MISR

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Kirk D. Poore, Junhong Wang, and William B. Rossow

accuracyof the measured cloud-base and cloud-top altitudes bycomparison with surface (e.g., lidar, radar, and. ceilometer) and satellite measurements, but initial comparisons indicate uncertainties less than 1000 m forlow- and midlevel clouds. The largest drawback to theRAOBS measurements is their low sensitivity to upperlevel moisture and clouds. In addition, the currentanalysis method, which requires strict agreement withsurface observations, does not adequately representmultilayer cases. Even with

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Ulrike Lohmann, Norman McFarlane, Lubomir Levkov, Kenzu Abdella, and Frank Albers

simulations and the cloud optical depth is close to one. Cloud-top pressure is 300 hPa in GESIMA, which corresponds to 9.3 km. This is in good agreement with the DLR backscatter lidar who reported cloud tops between 8 and 10 km between 1247 and 1252 UTC (M. Quante 1998, personal communication), but lower than the cloud top estimated from the aircraft at 10.3 km. Cloud tops are 50–70 hPa lower in the SCM simulations and are thus too low as compared with observations. Figure 5 shows the temporal evolution

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Xiquan Dong and Gerald G. Mace

Radiation Measurement (ARM) program ( Stokes and Schwartz 1994 ) established the ARM North Slope of Alaska (NSA) site (71.3°N, 156.6°W) at Barrow, Alaska, in 1998 ( Stamnes et al. 1999 ). The general approach adopted by the ARM program is to use long records of surface observations to develop, test, and improve cloud parameterizations in the context of single GCM grid columns and then to transfer the resulting parameterizations into full three-dimensional GCMs ( Randall et al. 1996 ). As a precursor to

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Keith M. Hines, David H. Bromwich, Philip J. Rasch, and Michael J. Iacono

, 1997 : Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res. , 102 , 16663 – 16682 . Morely , B. M. , E. E. Uthe , and W. Viezee , 1989 : Airborne lidar observations of clouds in the Antarctic troposphere. Geophys. Res. Lett. , 16 , 491 – 494 . Pavolonis , M. J. , and J. Key , 2003 : Antarctic cloud radiative forcing at the surface estimated from the AVHRR Polar Pathfinder and ISCCP D1 datasets, 1985–93. J

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Jacola A. Roman, Robert O. Knuteson, Steven A. Ackerman, David C. Tobin, and Henry E. Revercomb

that “water vapor provides the largest positive feedback and that the strength of this feedback can be estimated assuming constant relative humidity in all models.” As global temperatures in the troposphere increase along with the saturation vapor pressure, there is evidence that water vapor amounts also increase ( Trenberth et al. 2005 ). A strong correlation between sea surface temperature (SST) and precipitable water vapor (PWV) has been established using satellite microwave observations over

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Xiquan Dong, Baike Xi, and Peng Wu

-layered and overcast low clouds are present as determined from cloud radar/lidar observations, (ii) Z top < 3 km, (iii) 20 < LWP < 700 g m −2 , (iv) cosine of solar zenith angle μ 0 > 0.1, and (v) 0.08 < solar transmission (γ) < 0.7. The criteria (i)–(iii) for selecting daytime cloudy cases have been used for choosing nighttime cloudy cases in this study. Following the method of DM03 , we develop a new method to retrieve the MBL cloud-droplet effective radius profile r e ( h ) in this study, which is

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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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J. K. Angell

, Silver Spring Metro Center 2, Room 9358, 1325 EastWest Highway, Silver Spring, MD 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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Abhay Devasthale and Manu Anna Thomas

data ( Stephens et al. 2002 ; Wood 2008 ). So far, there are very few observational studies that characterize cloud LWC globally ( Hogan et al. 2004 ; Hu et al. 2007 , 2010 ; Lee et al. 2010 ). In particular, Hu et al. (2010) present comprehensive global statistics of supercooled water clouds combining Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS) data and also examine the relationship between temperature

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