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Chu-Chun Huang, Shu-Hua Chen, Yi-Chiu Lin, Kenneth Earl, Toshihisa Matsui, Hsiang-He Lee, I-Chun Tsai, Jen-Ping Chen, and Chao-Tzuen Cheng

–cloud interactions, both separately and together, on the MCS, and conclusions are presented in section 5 . 2. Numerical experimental design a. Case description We selected an MCS that developed over North Africa during 4–6 July 2010 primarily for its proximity to a moderate dust plume ( Fig. 1 ), but also since the NASA CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) satellites passed over the MCS after it reached maturity ( Fig. 1d ), providing useful vertical

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Samuel K. Degelia, Xuguang Wang, and David J. Stensrud

profilers similar to that recommended by the National Research Council (2009) . The observations assimilated here consist of atmospheric emitted radiance interferometers (AERIs; Turner and Löhnert 2014 ), Doppler wind lidars (e.g., Menzies and Hardesty 1989 ), radio wind profilers (e.g., Benjamin et al. 2004 ), high-frequency rawinsondes, and special surface data taken from fixed and mobile PECAN platforms. Assimilating similar datasets individually has been shown to improve convective

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Andreas Dörnbrack, Sonja Gisinger, Michael C. Pitts, Lamont R. Poole, and Marion Maturilli

1. Introduction The “picture of the month” as presented in this short contribution is not a photo of the sky spontaneously shot from a digital camera. The picture as displayed in Fig. 1 is a combination of spaceborne measurements by the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) satellite during one of several Arctic overpasses on 30 December 2015 and a high-resolution short

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Takuya Kawabata, Hironori Iwai, Hiromu Seko, Yoshinori Shoji, Kazuo Saito, Shoken Ishii, and Kohei Mizutani

, although the technique is limited to relatively calm atmospheric conditions in the lower atmosphere (e.g., Kusunoki 2002 ). For instance, Kawabata et al. (2007) reproduced the entire life cycle of an isolated cumulonimbus by assimilating clear-air echoes as environmental observations. Rennie et al. (2011) reported that assimilating clear-air echoes in 3D-Var had small but positive impacts for three convective cases. Another available technique for this purpose is Doppler wind lidar (DWL), which

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J. W. Wilson, S. B. Trier, D. W. Reif, R. D. Roberts, and T. M. Weckwerth

parameters. The UWKA mostly flew at an elevation of roughly 2.15 km MSL. The primary leg of interest was flown from west to east between 0408 and 0432 UTC. Convection initiation occurred 25 km north of the track at 0405 UTC. Other flight legs helped identify the location of a wind-shift line to be discussed later. Particularly important were Doppler lidar observations from TWOLF and MP3 to help determine vertical wind profiles and cloud base and to detect atmospheric gravity waves. In addition, there was

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Christopher S. Bretherton, Isabel L. McCoy, Johannes Mohrmann, Robert Wood, Virendra Ghate, Andrew Gettelman, Charles G. Bardeen, Bruce A. Albrecht, and Paquita Zuidema

potential cloud-controlling factors, inversion stability and cloud droplet number concentration. Section 6 compares observations from an illustrative CSET flight with reanalysis and a weather-nudged climate model, followed by a summary in section 7 . 2. CSET observations and analysis methods a. Measurements used in this study The G-V instrumentation used for CSET was described in detail by A19 . It included a 94-GHz cloud radar, a high spectral resolution lidar, dropsondes, and in situ probes for

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Kenneth Sassen, Christian J. Grund, James D. Spinhirne, Michael M. Hardesty, and Jose M. Alvarez

of measurement techniques.The lidar sites served as hubs for special rawinsondereleases, radiometric observations, and, when cirruswere present, aircraft operations. Research aircraft fromNCAR filed flight patterns based on the availability ofsatellite and lidar data, and operations in the vicinitiesof the lidar sites were often guided through ground-toair communications. Corresponding author address: Dr. Kenneth Sassen, Det~artmentof Meteorolog% University of Utah, Salt Lake City, UT 84112

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Lisa S. Darby and Gregory S. Poulos

), mesoscale winds, and aircraft turbulence; and 2) qualitatively and quantitatively evaluate mesoscale model results on the observational scale of a lidar. Section 2 provides background information on lee waves and rotors. In section 3 the 1997 MCAT field project is described. The model description appears in section 4 . Section 5 includes weather conditions, observations from the various instruments deployed, and the Regional Atmospheric Modeling System (RAMS) simulation results. Implications for

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Lisa S. Darby, William D. Neff, and Robert M. Banta

visible at the maximum range of the lidar, a second airsonde was prepared and launched just after the mesofront passage at the lidar site. 4. Detailed measurements of mesofront passage a. Premesofront Prior to the mesofront passage, lidar observations concentrated on canyon winds and the horizontal variability of the flow along the foothills. A series of nearly horizontal (0.5° elevation) Doppler lidar scans that began just before 0200 UTC 18 February 1991 showed

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M. Chiriaco, R. Vautard, H. Chepfer, M. Haeffelin, J. Dudhia, Y. Wanherdrick, Y. Morille, and A. Protat

evaluate a microphysical scheme using in situ observations in a few intensive case studies. Nevertheless, a statistical long-term observations approach could also show biases that are more difficult to explain. In this paper, we use remote sensing measurements obtained from the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA; http://sirta.lmd.polytechnique.fr ) ground-based atmospheric observatory. Lidar and radar observations taken over 18 months are used for statistical

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