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Keith D. Hutchison, Bruce Hauss, Barbara D. Iisager, Hiroshi Agravante, Robert Mahoney, Alain Sei, and John M. Jackson

1. Introduction A new approach is demonstrated to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program ( Hutchison et al. 2008 ). This approach exploits differences in both spectral and textural signatures between clouds and aerosols to identify pixels that contain heavy aerosols but were originally classified as clouds by the Visible Infrared Imager

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Li Yi, King-Fai Li, Xianyao Chen, and Ka-Kit Tung

: Radiative properties of terrestrial clouds at visible and infrared thermal window wavelengths . Quart. J. Roy. Meteor. Soc. , 99 , 346 – 369 , . Intrieri , J. M. , C. W. Fairall , M. D. Shupe , P. O. G. Persson , E. L. Andreas , P. S. Guest , and R. E. Moritz , 2002 : An annual cycle of Arctic surface cloud forcing at SHEBA . J. Geophys. Res. , 107 , 8039 , . 10.1029/2000JC000439 Kay , J. E. , and

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Joel R. Norris and Amato T. Evan

subgrid-scale cloud processes and produce cloud simulations that are inconsistent with one another and with observations (e.g., Clement et al. 2009 ; Klein et al. 2013 ). The shortcomings of theory and global climate models motivate the alternative approach of observing how clouds have changed in recent decades, a time period of rapidly increasing anthropogenic forcing and warming of the climate system. If patterns of multidecadal cloud variability likely to be associated with anthropogenically

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Robert F. Cahalan, Matthew McGill, John Kolasinski, Tamás Várnai, and Ken Yetzer

this campaign the NASA P-3B aircraft was dedicated solely to THOR measurements and based at McConnell Air Force Base near Wichita, Kansas, from which it made repeated passes over the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program’s Southern Great Plains (SGP) site in central Oklahoma. This site was chosen because of its collection of ground-based instruments that provided a wealth of information for validating THOR cloud thickness retrievals (see information online at

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Melissa Free and Bomin Sun

available online. Military station data were previously found in DATSAV3 (DSI-9956), a U.S. Air Force archive. All were kindly provided by NCDC. The appendix gives details about the characteristics of each of these sources of cloud data. Although the Extended Edited Synoptic Cloud Reports Archive land station dataset ( Warren et al. 2007 ) contains quality-controlled cloud data starting at 1973 for many stations, it does not include most U.S. stations after the early 1990s, and so is not suitable for

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Shashank S. Joshil, Cuong M. Nguyen, V. Chandrasekar, J. Christine Chiu, and Yann Blanchard

, B. Xi , Y. Liu , M. Thieman , and P. Minnis , 2017 : Effects of environment forcing on marine boundary layer cloud-drizzle processes . J. Geophys. Res. Atmos. , 122 , 4463 – 4478 , . 10.1002/2016JD026326 Yamaguchi , T. , G. Feingold , and J. Kazil , 2017 : Stratocumulus to cumulus transition by drizzle . J. Adv. Model. Earth Syst. , 9 , 2333 – 2349 , . 10.1002/2017MS001104 Zhou , X. , T

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Lucas Craig, Allen Schanot, Arash Moharreri, David C. Rogers, and Suresh Dhaniyala

blunt body because its narrow exit area creates a region of high static pressure just upstream of the cone entrance. The high pressure region helps force a larger flow through the perpendicular subsample tube. Second, the increase in static pressure will result in a decrease in flow velocity, which acts to slow the cloud droplets approaching the inlet. Thus, smaller droplets are able to pass through the cone without impaction on the SMAI surfaces because of their small inertia while generation of

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Sergey Y. Matrosov, Alexei V. Korolev, and Andrew J. Heymsfield

proposed here with V Z – D 0 relations derived from data suggested earlier by different authors for some common particle habits. Different lines in Fig. 1 show results of calculations using the expressions for the aerodynamic drag force and mass- and area-dimensional power laws for particle types commonly found in ice clouds. The calculation procedure from the drag force equations was outlined by Mitchell (1996) and Matrosov and Heymsfield (2000) . The results of calculations are shown for such

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Taneil Uttal and Robert A. Kropfli

.1007/BF01032004 Slingo, A. , and Slingo J. M. , 1988 : The response of a general circulation model to cloud longwave radiative forcing. I: Introduction and initial experiments. Quart. J. Roy. Meteor. Soc. , 114 , 1027 – 1062 . 10.1002/qj.49711448209 Stackhouse Jr., P. W. , and Stephens G. L. , 1991 : A theoretical and observational study of the radiative properties of cirrus: Results from FIRE 1986. J. Atmos. Sci. , 48 , 2044 – 2059 . 10.1175/1520-0469(1991)048<2044:ATAOSO>2.0.CO;2

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Yi Huang, Steven Siems, Michael Manton, Alain Protat, Leon Majewski, and Hanh Nguyen

ACHA algorithm has a tendency of misclassifying some lower cloud signals (or perhaps even clear sky conditions) as CIRRUS or OVERLAP types. The exact causes of these errors remain elusive, but the misclassification may exacerbate the uncertainties of cloud remote sensing even more, particularly for deep convective and corresponding deep outflow (anvil and cirrus) clouds, with a lack of information related to their longwave cloud forcing that is strongly dependent on CTT. The radiative effect of

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