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William B. Rossow, Yuanchong Zhang, and Junhong Wang

these results are unexpected because comparison of raobs high-level cloud amounts with lidar observations showed that raobs miss high-level clouds in the Tropics because of the limited altitudes reached by the balloons and miss the thinner, more scattered cirrus types at midlatitudes ( Wang et al. 2000 ). Although the ISCCP measurements also miss the very thinnest cirrus ( Liao et al. 1995a ; Jin et al. 1996 ; Stubenrauch et al. 1999 ), the satellite sensitivity to thin cirrus is greater than that

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Xiquan Dong, Patrick Minnis, and Baike Xi

(or the cross-sectional area of the particle) instead of sixth moment like the MMCR, the ceilometer and lidar can provide a more faithful estimate of Z base than MMCR because MMCR often detects precipitation-sized particles below cloud base and false cloud base due to the insect interference of MMCR observations at the SGP site. The ceilometer and lidar signals, however, can be severely attenuated due to absorption by optically thick liquid cloud layers, and these signals can only penetrate

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

. , A. Tanskanen , B. Veihelmann , C. Ahn , R. Braak , P. K. Bhartia , P. Veefkind , and P. Levelt , 2007 : Aerosols and surface UV products from ozone monitoring instrument observations: An overview . J. Geophys. Res. , 112 , D24S47 , . Venkata , S. , and J. Reagan , 2016 : Aerosol retrievals from CALIPSO lidar ocean surface returns . Remote Sens. , 8 , 1006 , . Waquet , F. , J. Riedi , L

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Manajit Sengupta, Eugene E. Clothiaux, Thomas P. Ackerman, Seiji Kato, and Qilong Min

. While the clouds are detectable using radars and lidars, or a combination of both, inferring the size distributions of cloud droplets is a complex task. Possible methods involve the use of radar reflectivities ( Kato et al. 2001 ) or surface radiation measurements ( Min and Harrison 1996 ) along with the column liquid water path retrieved from a microwave radiometer ( Liljegren et al. 2001 ) as a constraint. Ackerman et al. (1999) developed a paradigm for assessing the accuracy of parameterizations

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Rachel L. Storer, Guang J. Zhang, and Xiaoliang Song

observations and models. The large-scale cloud fraction resulting from the CAM5 physics can be compared with satellite observations. In Fig. 4 , the regional stratus fraction for the four regions is plotted against cloud fraction from the CloudSat geometric profiling product (2B-GEOPROF)-lidar data ( Mace and Zhang 2014 ; ), averaged over two years (2009–10, annually or seasonally where appropriate). For the most part, in all four regions the cloud fraction

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I. Gultepe, G. Isaac, D. Hudak, R. Nissen, and J. W. Strapp

observations collected during the Beaufort and Arctic Storms Experiment (BASE) field project. In addition, relationships between cloud dynamical processes and microphysical parameters are discerned as a first step toward developing parameterizations of Arctic cloud processes for climate models. 2. Observations Data used in this study were gathered from aircraft, Doppler radar, and LANDSAT observations collected during BASE, which took place in the fall of 1994 between 1 September and 13 October in the

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C. W. Fairall, J. E. Hare, and J. B. Snider

). Our understanding of marine stratocumulus is basedon a combination of observations and theoretical reasoning. Until recently the historical records of marinecloud properties consisted primarily of eyeball estimates of cloud type and amount made from coastalstations, islands, and a few ships. The last few decadeshave seen various types of instrumented aircraft usedto probe .the clouds directly and a bewildering varietyof numerical simulations used to investigate stratocumulus microphysical and

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Simon P. de Szoeke, Sandra Yuter, David Mechem, Chris W. Fairall, Casey D. Burleyson, and Paquita Zuidema

–driven upwelling at the northwest–southeast-slanted American coast (reviewed by Xie 2004 ). The clouds and their feedbacks are difficult to simulate accurately because of uncertainties in parameterizations of critical turbulence and precipitation processes in the atmospheric models. Testing models and improving parameterizations thus call for detailed observations of stratus cloud processes. Figure 1a shows the location of cool tropical SST (shaded) and the stratocumulus cloud deck (gray contours) for

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Deepashree Dutta, Steven C. Sherwood, Katrin J. Meissner, Alex Sen Gupta, Daniel J. Lunt, Gregory J. L. Tourte, Robert Colman, Sugata Narsey, David Fuchs, and Josephine R. Brown

SST changes at high latitudes, rather than different tropical SSTs in these experiments. The SST anomaly for HL20 ( Fig. 1 ) is given by HL 20   d SST = 0, latitude ≤ ± 30 ° = 10 × [ 1 − cos ⁡ ( π latitude / 70 ° ) ] , latitude > ± 30 ° . CAM4, CAM5, and ACCESS include the CFMIP Observation Simulator Package (COSP) ( Bodas-Salcedo et al. 2011 ; Kay et al. 2012a ). This allows direct comparison of model output to the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO

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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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