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H. Chepfer, M. Chiriaco, R. Vautard, and J. Spinhirne

observations are not resolved vertically and hardly detect very thin cloud layers. Lidars provide powerful means of observing high clouds, especially optically thin clouds, which are difficult to detect from passive remote sensing ( Platt 1973 ; Sassen 1991 ; Bissonnette et al. 2001 ; Noel et al. 2006 ; Hart et al. 2005 ). Despite the difficulties generated by low-cloud masks, ground-based lidar studies of these clouds have allowed researchers to gain limited insight into their spatial and seasonal

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Simone Lolli, James R. Campbell, Jasper R. Lewis, Yu Gu, Jared W. Marquis, Boon Ning Chew, Soo-Chin Liew, Santo V. Salinas, and Ellsworth J. Welton

1. Motivation Campbell et al. (2016) isolate top-of-atmosphere (TOA) net cirrus cloud radiative forcing (CRF) properties for a continuous 1-yr, single-layer cloud dataset developed from NASA ground-based Micro-Pulse Lidar Network (MPLNET; ) ( Welton et al. 2001 ; Campbell et al. 2002 ; Lolli et al. 2013 ) observations collected at Greenbelt, Maryland [38.99°N, 76.84°W; 50 m above mean sea level (MSL)]. They estimate that cirrus clouds exert an absolute net

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R. M. Banta, L. D. Olivier, E. T. Holloway, R. A. Kropfli, B. W. Bartram, R. E. Cupp, and M. J. Post

1328 JOURNAL OF APPLIED METEOROLOGYSmoke-Column Observations from Two Forest FiresUsing Doppler Lidar and Doppler RadarR. M. BANTA, L. D. OLIVlER, E. T. HOLLOWAY, R. A. KROPFLI, B. W. BARTRAM, R. E. CUPP, AND M. J. POST NOAA /ERL Wave Propagation Laboratory, Boulder, Colorado (Manuscript received 28 August 1991, in final form 2 March 1992) To demonstrate the usefulness of active remote-sensing systems in observin~ form fire plume behavior

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Odran Sourdeval, Gérard Brogniez, Jacques Pelon, Laurent C.-Labonnote, Philippe Dubuisson, Frédéric Parol, Damien Josset, Anne Garnier, Michaël Faivre, and Andreas Minikin

Pathfinder Satellite Observations (CALIPSO), which carries the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) and the infrared imaging radiometer (IIR), is of great interest to the research on cirrus clouds. Indeed, it has already been shown that infrared measurements are very efficient for retrieval of ice clouds properties, such as optical thickness, cloud-top pressure, and even microphysical properties (e.g., Parol et al. 1991 ). To use these measurements to retrieve such properties, an

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Gijs de Boer, Edwin W. Eloranta, and Matthew D. Shupe

tens of meters thick and the thickest around 1000 m thick on average. The thickest clouds exist during fall and the thinnest during spring. Barrow observations show substantially thicker clouds, on average, than those observed in Eureka. Thirty-minute average lidar cloud optical depths are reviewed in Fig. 5d . These statistics are skewed by the AHSRL’s inability to penetrate deeper than an optical depth of around 5 before suffering from attenuation. As shown in Fig. 4 , a large fraction of these

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Robert J. Conzemius and Evgeni Fedorovich

for comparing the observed evolution of the sheared atmospheric CBL with large-eddy simulation (LES; Moeng and Sullivan 1994 ; Pino et al. 2003 ; Conzemius and Fedorovich 2006a ). The primary goals of the study are twofold. First, we intend to evaluate LES predictions of the sheared CBL growth against lidar observations of CBL depth evolution and compare LES output with radiometer, radar, and radiosonde data to more fully understand the evolution of the mean wind and temperature in the CBL

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M. Christian Schwartz, Virendra P. Ghate, Bruce. A. Albrecht, Paquita Zuidema, Maria P. Cadeddu, Jothiram Vivekanandan, Scott M. Ellis, Pei Tsai, Edwin W. Eloranta, Johannes Mohrmann, Robert Wood, and Christopher S. Bretherton

sampling strategies and the mean conditions observed during CSET can be found within Albrecht et al. (2019) , Mohrmann et al. (2019, manuscript submitted to Mon. Wea. Rev .), and Bretherton et al. (2019) . A notable feature of the CSET campaign was the first deployment of the HIAPER W-band Doppler cloud radar (HCR), together with the high-spectral-resolution lidar (HSRL). These systems were included on the CSET GV deployment to remotely sense cloud and precipitation. A cloud and precipitation data

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Jing-Wu Liu, Shang-Ping Xie, Joel R. Norris, and Su-Ping Zhang

Infrared Pathfinder Satellite Observations ( CALIPSO ) satellite was launched on 28 April 2006 by the National Aeronautics and Space Administration (NASA) and the French Centre National d’Études Spatiales (CNES) to study the impact of clouds and aerosols on Earth’s radiation budget and climate ( Winker et al. 2009 ). A selective, iterated boundary location algorithm is used to detect cloud layers from the lidar backscatter signals ( Vaughan et al. 2009 ). CALIPSO provides a cloud-layer product with

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Steven D. Miller, Courtney E. Weeks, Randy G. Bullock, John M. Forsythe, Paul A. Kucera, Barbara G. Brown, Cory A. Wolff, Philip T. Partain, Andrew S. Jones, and David B. Johnson

-profile information from the CloudSat ( Stephens et al. 2002 ) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ; Winker et al. 2010 ) active sensors is combined with traditional two-dimensional (2D) observations of cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS, carried on the Aqua satellite) to provide an ability to evaluate 3D model cloud fields. This evaluation requires innovations to existing MET tools as well as the introduction of new

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Robert Atlas, Ross N. Hoffman, Zaizhong Ma, G. David Emmitt, Sidney A. Wood Jr., Steven Greco, Sara Tucker, Lisa Bucci, Bachir Annane, R. Michael Hardesty, and Shirley Murillo

). The OAWL sensor is described in section 2a . c. Summary of past global OSSEs The basic methodology for OSSEs, as modified by Atlas and others in the early 1980s ( Atlas et al. 1985a ), is illustrated in Fig. 1 . An OSSE begins with an NR generated by a state-of-the-art atmospheric model. From the NR all currently available observations, as well as any new observations to be evaluated, are simulated. In the experiments reported here, for the wind lidar, a very detailed lidar simulation model is

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