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Paul J. Neiman, F. Martin Ralph, Robert L. Weber, Taneil Uttal, Louisa B. Nance, and David H. Levinson

Cyclones: The Erik Palmén Memorial Volume, C. W. Newton and E. Holopainen, Eds., Amer. Meteor. Soc., 107–127 . Wakimoto , R. M. , 1982 : The life cycle of thunderstorm gust fronts as viewed with Doppler radar and rawinsonde data. Mon. Wea. Rev , 110 , 1060 – 1082 . Weber , B. L. , D. B. Wuertz , D. C. Law , A. S. Frisch , and J. M. Brown , 1992 : Effects of small-scale vertical motion on radar measurements of wind and temperature profiles. J. Atmos. Oceanic Technol , 9 , 193

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Steven E. Koch, Robert E. Golus, and Paul B. Dorian

(Manuscript received 15 June 1987, in final form 16 June 1988) ABSTRACT This paper presents the results of a very detailed investigation into the effects of preexisting gravity wavesupon convective systems, as well as the feedback effects of convection of varying intensity upon the waves. Theanalysis is based on the synthesis of synoptic surface and barograph data with high-resolution surface mesonetwork,radar, and satellite data collected during a gravity wave event described by

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Daniel F. Steinhoff, David H. Bromwich, Michelle Lambertson, Shelley L. Knuth, and Matthew A. Lazzara

slopes, such as Cape Denison, which is associated with the broad-scale convergence of katabatic flow from the continental interior to a restricted coastal zone ( Schwerdtfeger 1984 ). Katabatic effects occasionally reach McMurdo directly from Siple Coast ( Bromwich et al. 1992 ) and sources along the Transantarctic Mountains ( Liu and Bromwich 1993 ). For this case, the complex topography of the region ( Figs. 1b,c ) modifies preexisting synoptic-scale and mesoscale features to produce wind speeds

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Geng Xia, Matthew C. Cervarich, Somnath Baidya Roy, Liming Zhou, Justin R. Minder, Pedro A. Jimenez, and Jeffrey M. Freedman

on local vegetation growth in Texas and Illinois using MODIS vegetation greenness measurements . Remote Sens. , 9 , 698 , . 10.3390/rs9070698 Xia , G. , L. Zhou , J. M. Freedman , S. Baidya Roy , R. A. Harris , and M. C. Cervarich , 2016 : A case study of effects of atmospheric boundary layer turbulence, wind speed, and stability on wind farm induced temperature changes using observations from a field campaign . Climate Dyn. , 46 , 2179

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David R. Fitzjarrald and Michael Garstang

FxH represents the flux of X below the discontinuous jump. For other variables, subscripts Hand zero denote values of a variable evaluatedat the top of the mixed layer (above the discontinuous jump) and the value of a variable at thesurface, respectively. Subscript rn refers to themixed layer mean of a variable. For completeness,variable Qxm is added to represent the sum of advective effects and radiative cooling (for 0v) and thenet result of evaporation minus condensation (for0~ and q). Following

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Mark F. Geldmeier and Gary M. Barnes

, 117–133. Stull, R. B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, 666 pp. Sverdrup, H. U., M. W. Johnson, and R. H. Fleming, 1942: The Oceans. Prentice-Hall, 1087 pp. Webb, E. K., G. I. Pearman, and R. Leuning, 1980: Correction of flux measurements for density effects due to heat and water vapor transport. Quart. J. Roy. Meteor. Soc., 106, 85–100. Webster, P. J., and R. Lukas, 1992: TOGA COARE: The Coupled Ocean–Atmosphere Response Experiment

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Scot T. Heckman and William R. Cotton

satellite imagery, lidar, and aircraft measurements taken during the FIREcirrus intensive field observation. Cloud-top generation zones and layering were simulated. Sensitivity simulationswere run to determine long- and shortwave radiative forcing. Also, a simulation was run with no condensateto examine cloud feedbacks on the environment. Longwave radiation appeared to be instrumental in developingweak convective-like activity, thereby increasing the cloud's optical depth.1. Introduction Cirrus

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Alicia R. Karspeck

covariances still plays a critical role in designing a useful assimilation system ( Dee 1995 ). In addition to errors associated with the measurement process (including instrumental errors), observational errors also include “representativeness errors.” Representativeness error is commonly understood to account for physical processes and scales that are detectable through observation but are not resolvable by the numerical model (e.g., Cohn 1997 ; Fukumori et al. 1999 ; Oke and Sakov 2008 ). This

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Charles A. Doswell III and Paul M. Markowski

governing equations are derived in a manner analogous to other disciplines involving fluid flow, whereby the so-called continuum hypothesis is applied to various physical laws presumed to apply to fluids. As discussed in, for example, Batchelor (1967 , 4–6), this avoids the additional complexity associated with the molecular nature of real fluids. Measurements within fluids are assumed to apply to some volume of essentially arbitrary size (i.e., a parcel) containing enough molecules to allow the

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Ying-Hwa Kuo, Yong-Run Guo, and Ed R. Westwater

improvedhorizontal and temporal resolution, as well as beingless sensitive to the contaminating effects of clouds.Coupled with the continued deployment of wind profilers and advanced data assimilation techniques, thesedata should add substantially to our ability to monitorand predict the four-dimensional behavior of tropospheric water vapor.Acknowledgments. This project was partially supported by the Department of Energy Atmospheric Radiation Measurement Program under Project DOE IADE Al 05-9OER 61070. We thank

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