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Hubert Luce, Takuji Nakamura, Masayuki K. Yamamoto, Mamoru Yamamoto, and Shoichiro Fukao

–stratosphere–troposphere/incoherent scatter (MST/IS) radar mainly sensitive to humidity and temperature irregularities in the neutral atmosphere and to electron density fluctuations in the ionized atmosphere. During observations performed in 7–8 June 2006, the MU radar revealed 0.5–2-km-deep turbulent layers with roll-like appearance developing downward below 8.0 km above mean sea level (MSL). Coincident observations with a Rayleigh–Mie–Raman (RMR) lidar ( Behrendt et al. 2004 ) showed a 4-km-deep layer of cirrus above 8.0 km. The

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Maike Ahlgrimm, David A. Randall, and Martin Köhler

1. Introduction Observations from spaceborne lidar provide a novel perspective on clouds. The lidar is able to directly measure the height of multiple cloud layers, provided the upper layers are not optically thick. A variety of studies have examined overlap statistics ( Wang and Dessler 2006 ), distributions of cloud-top and -base heights ( Hart et al. 2005 ; Dessler et al. 2006b ), and the occurrence and backscatter properties of optically thin clouds ( Dessler et al. 2006a ). There are also

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Christian Kühnlein, Andreas Dörnbrack, and Martin Weissmann

strong and possibly severe turbulence. However, as already pictured by Förchgott (1949) and impressively shown by Hertenstein and Kuettner (2005) using numerical simulations, a variation of the upstream shear at mountaintop level results in a totally different downslope flow and rotor types. Moreover, observations of downslope flows and rotors revealing the spatial structure in the boundary layer and the temporal evolution were missing. Some ground-based coherent Doppler lidar observations were

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Brian J. Carroll, Belay B. Demoz, David D. Turner, and Ruben Delgado

reanalysis datasets reveal the LLJ patterns already discussed but are limited in their ability to resolve some important details of the spatiotemporal evolution of the LLJ throughout its domain (e.g., Whiteman et al. 1997 ; Song et al. 2005 ; Walters et al. 2008 , 2014 ). Targeted observations have been utilized over the years to improve our understanding of LLJs, MCSs, NCI, and their interplay. Wind, water vapor, and elastic backscatter lidars have been some of the key tools in these advanced

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Florian Pantillon, Bianca Adler, Ulrich Corsmeier, Peter Knippertz, Andreas Wieser, and Akio Hansen

models. Several cases were sampled with a Doppler lidar during the Wind and Storms Experiment (WASTEX) that took place in winter 2016–17 on a former waste deposit topping at 50-m height and located in the Upper Rhine Valley near Karlsruhe in southwestern Germany ( Pantillon et al. 2018b ). Doppler lidar measurements are challenging in extratropical cyclones due to the low aerosol load, which hinders observations after the passage of fronts. This was the case during the extreme windstorm “Egon” on 12

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Philip T. Bergmaier, Bart Geerts, Zhien Wang, Bo Liu, and Patrick C. Campbell

, hereafter referred to as Part I ), using operational observations and a Weather Research and Forecasting Model (WRF) simulation with inner domain resolution of 1 km. This paper (Part II) examines airborne Raman lidar and flight-level data obtained across the same dryline by the University of Wyoming King Air (UWKA) research aircraft. This dryline developed under “synoptically active” conditions, whereby the development, intensity, and motion of the dryline are heavily influenced by the synoptic

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Jonathan M. Wilkinson, Robin J. Hogan, Anthony J. Illingworth, and Angela Benedetti

) , they deduced that the ECMWF model was capable of representing low clouds quite well but often produced too much high cloud, particularly evident from long (48 h) forecasts. Model skill scores also decreased with increasing forecast length. However, one must be very careful when comparing observations made by lidar instruments directly to clouds as there will be a loss of signal power (attenuation) as the beam passes through clouds, and often there will be a total extinction of the signal in liquid

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Maike Ahlgrimm and Martin Köhler

limitations either (as will be discussed in detail), the lidar’s small footprint of 100-m diameter is much more suitable for observing broken cloud cover, as is found in the trade cumulus (TCu) regions. Previous studies ( Chepfer et al. 2008 ; Wilkinson et al. 2008 ) have shown the value of these observations for model assessment but did not take full advantage of the high vertical resolution available from the lidar. Over 10 years ago, Jakob (1999) determined that the Integrated Forecasting System

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Karoliina Hämäläinen, Elena Saltikoff, Otto Hyvärinen, Ville Vakkari, and Sami Niemelä

calibration methods combined with a new type of ground-based remote sensing observations.The calibration method (BCT) was chosen based on our previous experiences in the GLAMEPS consortium ( GLAMEPS 2015 ), in which the chosen method has been used for the calibration of the operational forecasting model. The method used in this paper has been found to be efficient in correcting the 10-m winds. The lidar network of the Finnish Meteorological Institute (FMI) includes four observation locations operating in

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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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