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Daniel Leuenberger, Alexander Haefele, Nadja Omanovic, Martin Fengler, Giovanni Martucci, Bertrand Calpini, Oliver Fuhrer, and Andrea Rossa

( Reichardt et al. 2012 ), and the Raman Lidar for Meteorological Observations (RALMO) operated by MeteoSwiss ( Dinoev et al. 2013 ). We consider RALMO representative of state-of-the-art automated Raman lidars and a more detailed description is provided in the following section. Advances in laser technology have paved the way for commercial instruments, which are nowadays available ( Lange et al. 2019 ; Fréville et al. 2015 ). Though operational deployment of such commercial instruments is still very

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P. C. S. Devara, P. E. Raj, K. K. Dani, G. Pandithurai, M. C. R. Kalapureddy, S. M. Sonbawne, Y. J. Rao, and S. K. Saha

parameter, and not many observations are available in the literature. In the present paper, we have made an attempt to infer the aerosol shape qualitatively from the lidar depolarization ratio. Also, by utilizing the unique facility (the switching of the state of polarization of the laser pulse energy between parallel and perpendicular) available with the DPMPL, datasets are being collected to undertake detailed analyses of cloud composition [such as determination of water, ice, or mixed phase and the

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Katrina S. Virts and John M. Wallace

1. Introduction In the companion paper ( Virts et al. 2010 ; hereafter, VWFA ), we introduce an analysis protocol for relating features in the frequency of occurrence of cirrus clouds in the tropical tropopause transition layer (TTL), as observed by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), to fields of atmospheric variables throughout the tropics. The protocol involves the generation of a TTL cirrus index (cloud fraction; i.e., the

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S. Mahagammulla Gamage, R. J. Sica, G. Martucci, and A. Haefele

outlook to future work are given in sections 5 and 6 . 2. Measurements and data used in the ERA5-reRH a. RALMO For this study we use Raman lidar measurements from the Raman Lidar for Meteorological Observations (RALMO), located in Payerne (46°48′N, 6°56′E), and operated by MeteoSwiss. RALMO is a fully automated lidar, operating near continuously since 2008, with an average uptime of 50%, with the primary loss of measurements due to events of precipitation and low clouds. The transmitting system of

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Catherine M. Naud, Anthony Del Genio, Gerald G. Mace, Sally Benson, Eugene E. Clothiaux, and Pavlos Kollias

and geographical variations suggest a dependence on environmental state that could result in a contribution to cloud feedback in a climate change. Here, we use ground-based radar and lidar observations in conjunction with information on the state of the atmosphere derived from meteorological reanalyses to investigate the impact of large-scale dynamics and atmospheric state on cloud overlap. Section 2 describes the various datasets used in this study, briefly presents the method, and discusses

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I. Kolev, O. Parvanov, B. Kaprielov, E. Donev, and D. Ivanov

different scanning patterns were used for the lidar observations. Horizontal scanning along six azimuths, 336°, 6°, 30°, 60°, 90°, and 110° with respect to north, as shown in Fig. 1 . Vertical scanning in increments ( θ ° in Fig. 2 ) of 1° between the horizontal and 10° and 2°–5° between 10° and 30°. The vertical scans provide height–range images (HRI) of the vertical cross section of the aerosol backscattering coefficient field along a fixed azimuth φ, as shown in Fig. 2 . The analyses presented

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S. P. Alexander and A. R. Klekociuk

–Aerosol Lidar with Orthogonal Polarization (CALIOP) can detect cirrus clouds with τ as low as 0.01 ( Dupont et al. 2010 ). By combining data received from the CALIOP lidar with near-instantaneous observations from the Cloud Profiling Radar (CPR) aboard CloudSat , information on optically thick clouds and multiple cloud decks throughout the full vertical extent of the atmosphere becomes available ( Mace et al. 2009 ; Delanoë and Hogan 2010 ; Verlinden et al. 2011 ; Huang et al. 2015 ). The addition of

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

a ). The CAPRICORN-2016 (Phase I) experiment was conducted with the Australian Marine National Facility (MNF) Research Vessel (R/V) Investigator from 14 March to 16 April 2016. The Investigator was equipped with a suite of state-of-the-art active and passive instruments, making a comprehensive set of measurements including the first-ever concurrent observations on cloud and precipitation with a 95-GHz stabilized cloud radar, a cloud and aerosol backscatter lidar, a micro rain radar, and a

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M. Chiriaco, H. Chepfer, V. Noel, M. Haeffelin, and P. Drobinski

advanced methods ( Stubenrauch et al. 1999 ) based on infrared sounders have significantly contributed to improved observations of the vertical structure of the atmosphere in the infrared domain, most observations at these wavelengths do not allow documenting the vertical distribution of crystals in cirrus clouds. The current study explores the potential for coupling data from two lidars to infer the vertical distribution of ice and particles within midlatitude semitransparent cirrus clouds. The first

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Micheal Hicks, Ricardo Sakai, and Everette Joseph

detection methods designed for lidar observations. Lidars can continuously monitor the distributions of atmospheric tracers for the detection of ML heights ( Emeis et al. 2008 ). Automatic detection methods are commonly used to attain these heights in a timely manner (e.g., Schmid and Niyogi 2012 ; Granados-Muñoz et al. 2012 ; Luo et al. 2014 ). In general, there are no standard practices for determining ML heights ( Seibert et al. 2000 ) and the approach taken depends on what is being measured (e

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