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Curtis J. Seaman, Yoo-Jeong Noh, Steven D. Miller, Andrew K. Heidinger, and Daniel T. Lindsey

’s guide. Version 1.1, NOAA Tech. Rep. NESDIS 147, 30 pp. [Available online at .] Slingo , A. , and J. M. Slingo , 1988 : The response of a general circulation model to cloud longwave forcing. I: Introduction and initial experiments . Quart. J. Roy. Meteor. Soc. , 114 , 1027 – 1062 , doi: 10.1002/qj.49711448209 . 10.1002/qj.49711448209 Stephens , G. L. , 1994 : Remote Sensing of the Lower Atmosphere

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Hai-Tien Lee and Robert G. Ellingson

where a i are regression coefficients. Besides the first two temperature ratio terms in Eq. (17) , which are related to the water vapor content [cf. Eq. (16) ], the formulation was determined by stepwise regression analyses with candidate predictors expressed as different functions of the HIRS brightness temperatures. The approach is a little different for overcast conditions. We take the difference between overcast and clear-sky DLR, that is, the cloud radiative forcing, where

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Derek J. Straub and Jeffrey L. Collett Jr.

, cloud drop trajectories were calculated via integration of a force balance on individual drops. These trajectory simulations were used for predicting collection efficiency, drop shatter, and evaporation. Trajectory calculations for drops 1 μ m in diameter and for drops 2–50 μ m in diameter, in 2- μ m increments, proceeded until a wall was encountered or the drops exited the computational domain. For illustrative purposes, 10 sample trajectories for drops 1, 10, and 30 μ m in diameter are shown in

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Ray Harris-Hobbs, Kathy Giori, Michael Bellmore, and Arleen Lunsford

-74C (5-cm wavelength)radar located at Patrick Air Force Base.c 1994 American Meteorological SocietyJUNE I994 HARRIS-HOBBS ET AL. 739 FIG. 1. ABFM system installed on the Learjet 36A. The black paint on the second window is the location of one ofthe field mills. A two-dimensional precipitation (2D-P) probe and a one-dimensional cloud (ID-C) probe can be seenin the wing tip and underneath the wing

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Thomas R. Parish and David Leon

1. Introduction Convective motions inevitably modulate the ambient pressure field. Conventional thinking regarding early stages of simple convection (e.g., Houze 1993 ; Bluestein 1993 ; Markowski and Richardson 2010 ) suggests that in response to upward accelerations due to buoyancy, horizontal compensating motions must exist beneath and above the rising parcel. Horizontal accelerations and flow toward the cloud base require a local pressure deficit and hence a negative horizontal pressure

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Toshihisa Matsui, Xiping Zeng, Wei-Kuo Tao, Hirohiko Masunaga, William S. Olson, and Stephen Lang

based on well-established field campaigns and have already been used previously for long-term CRM simulations to study tropical cloud and precipitation processes ( Zeng et al. 2008 ; Zhou et al. 2007 ; Blossey et al. 2007 ). Those studies demonstrated that CRMs driven by the large-scale forcing could simulate the general features of the observed cloud processes but with essentially similar biases. Zeng et al. (2008) found that the GCE tended to overestimate surface precipitation throughout the

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Zhuocan Xu, Gerald G. Mace, and Derek J. Posselt

contributor to the cloud feedback uncertainties is marine boundary layer clouds ( Bony and Dufresne 2005 ; Sherwood et al. 2014 ). MBL clouds exert a significant influence on Earth’s radiative balance due to their extensive coverage over the global oceans ( Hartmann et al. 1992 ; Haynes and Stephens 2007 ). The complexity of estimating the climate forcing by MBL clouds arises from the wide range of spatial and temporal scales at which various physical processes act. Macrophysical properties of MBL

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Natalie Midzak, John E. Yorks, Jianglong Zhang, Bastiaan van Diedenhoven, Sarah Woods, and Matthew McGill

main source of ice cloud radiative forcing error stems from scattering parameters of the varying ice crystal habits ( Wendisch et al. 2005 ). A better understanding of cirrus microphysical properties, especially the shape and size of ice crystals, is necessary to more accurately quantify their effects on the climate system. Fig . 1. SPEC CPI imagery collected during an 18 Sep 2013 flight from the SEAC 4 RS field campaign highlighting variations in crystal shape and size for plates, irregulars

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Lucas Craig, Arash Moharreri, David C. Rogers, Bruce Anderson, and Suresh Dhaniyala

along the droplet trajectory and compared against their critical values. Droplets in the size range of 2–500 μ m were considered in this study. Droplet velocities and trajectories were determined considering the 1D linearized velocity profile [Eq. (4) ] and the net force acting on the droplets. Because the cloud droplet sizes are large and they travel through regions of strong flow velocity gradients, the droplets can attain a finite Reynolds number (Re d > 0.1) along their trajectory. Thus

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Dan K. Arthur, Sonia Lasher-Trapp, Ayman Abdel-Haleem, Nicholas Klosterman, and David S. Ebert

December 2004 and January 2005. The observational strategy of RICO was to gather data on shallow maritime convection and trade wind cumuli at a wide range of scales ( Rauber et al. 2007 ). One goal of the project was to gain a better understanding of the warm-rain process, as traditional theory has been unable to explain how the growth of cloud droplets by condensation alone proceeds to drop sizes large enough to begin coalescence and produce precipitation as rapidly as has been observed (e.g., Paluch

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