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Volker Wulfmeyer and Tijana Janjić

because of their complex vertical variability. As a consequence, detailed clear-air observations of MBL variables are essential to improve modeling and simulation of transport processes and modeling and simulation of cloud and precipitation development. In recent years, considerable progress has been made in the development, improvement, and application of active remote sensing systems such as lidar for boundary layer research (e.g., Sullivan et al. 1998 ; Wulfmeyer 1999a , b ; Grund et al. 2001

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Lei Zhang and Zhaoxia Pu

of convective initiations and evolutions. Specifically, results from Wulfmeyer et al. (2006) indicated that the assimilation of water vapor differential absorption lidar data improves the simulation of the structures of the moisture field of a convective system. Although the importance of the high-resolution moisture information in the analysis and simulation of MCS has been well recognized and addressed, and the influence of wind observations, especially the winds in the boundary layers to

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D. D. Turner

property retrieval algorithm (MIXCRA), which uses the physical iterative optimal estimation approach, was developed to retrieve cloud optical depth, ice fraction, liquid and ice water paths, and the effective radius of the water droplets and ice particles from single-layer optically thin clouds using data from ground-based AERI radiance and lidar cloud boundary observations. Simulated data and case studies were used to characterize this retrieval algorithm. The case studies demonstrated good agreement

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G. J. Nott, T. J. Duck, J. G. Doyle, M. E. W. Coffin,, C. Perro, C. P. Thackray, J. R. Drummond, P. F. Fogal, E. McCullough, and R. J. Sica

photometers ( Holben et al. 1998 ), along with the aforementioned high-spectral-resolution lidar and ozone DIAL. The high-spectral-resolution lidar and microwave radiometer are operated under the Study of Environmental Arctic Change program (SEARCH; http://www.arcus.org/search ). At 80°N, Eureka sees a high number of overpasses by the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite due to its orbit inclination of 98.2° ( Winker et al. 2009 ). This means the CRL is

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Marcus Klingebiel, Virendra P. Ghate, Ann Kristin Naumann, Florian Ditas, Mira L. Pöhlker, Christopher Pöhlker, Konrad Kandler, Heike Konow, and Bjorn Stevens

-wind cloudiness in observations and models: The major cloud components and their variability . J. Adv. Model. Earth Syst. , 7 , 600 – 616 , https://doi.org/10.1002/2014MS000390 . 10.1002/2014MS000390 O’Connor , E. J. , R. J. Hogan , and A. J. Illingworth , 2005 : Retrieving stratocumulus drizzle parameters using Doppler radar and lidar . J. Appl. Meteor. , 44 , 14 – 27 , https://doi.org/10.1175/JAM-2181.1 . 10.1175/JAM-2181.1 O’Connor , E. J. , A. J. Illingworth , I. M. Brooks , C. D

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Hyoun-Myoung Cho, Shaima L. Nasiri, and Ping Yang

or dynamical processes of cloud formation. This study focuses on the evaluation of a passive infrared-channel-based cloud thermodynamic phase retrieval using collocated lidar observations. There are numerous robust cloud-phase determination techniques based on reflected solar radiation ( Pilewskie and Twomey, 1987 ; Riédi et al. 2000 ; Knap et al. 2002 ; Platnick et al. 2003 ; Pavolonis et al. 2005 ; Chylek et al. 2006 ), but fewer techniques use only infrared observations. The main

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S. H. Melfi and Stephen P. Palm

observations from several field programs conducted to study cloud streets. The remainder of the paper is organized as follows: section 2 will present the plan of COWEX; section 3 will describe the airborne lidar system and its operation; in section 4 the lidar data will be presented and compared to the results of the Townsend (1965) study; section 5 will present the conceptual model and its implications; section 6 will compare the conceptual model estimates with several field campaign

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Matt C. Wilbanks, Sandra E. Yuter, Simon P. de Szoeke, W. Alan Brewer, Matthew A. Miller, Andrew M. Hall, and Casey D. Burleyson

stratocumulus deck. The frontal zone slants backward with height up to the maximum depth of the flow (≈400 m). Behind the head, the height of the main flow levels off and gradually descends. Consistent with lidar observations, some of the clouds behind and along the frontal zone are very low lying (<400 m). The wind profile inside the density current is shown at B in Fig. 16 . Surface layer shear is present in the lowest ≈200 m of the main density current flow. The right-bound return flow ( Simpson 1997

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B. Gasparini, A. Meyer, D. Neubauer, S. Münch, and U. Lohmann

, 5751 – 5758 , https://doi.org/10.5194/acp-9-5751-2009 . 10.5194/acp-9-5751-2009 Cziczo , D. J. , and Coauthors , 2013 : Clarifying the dominant sources and mechanisms of cirrus cloud formation . Science , 340 , 1320 – 1324 , https://doi.org/10.1126/science.1234145 . 10.1126/science.1234145 Davis , S. , and Coauthors , 2010 : In situ and lidar observations of tropopause subvisible cirrus clouds during TC4 . J. Geophys. Res. , 115 , D00J17, https://doi.org/10.1029/2009JD013093 . 10

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David B. Mechem, Yefim L. Kogan, and David M. Schultz

nonprecipitating stratocumulus. Cloud top ascended and descended over the next 4 h from 0600 to 1000 UTC. Radar estimates of cloud top are consistent with those obtained from soundings. Cloud bases obtained via micropulse lidar, however, were consistently overestimated (by over 100 m in one of the cases; Fig. 7a ). The lifting condensation level (LCL; dotted gray line in Fig. 7a ), calculated from surface observations of temperature and moisture, was lower than cloud-base estimates from sounding, lidar, or

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