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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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Tammy M. Weckwerth, Kristy J. Weber, David D. Turner, and Scott M. Spuler

forecasting skill and for obtaining improved accuracy in QPF skill. This latter report noted that “moisture and BL wind field observations are likely to be even more important on the mesoscale.” ( NRC 2012 , p. 87). Optimal temporal resolution requirements for profiling water vapor ranges from better than 1 h for monitoring purposes to better than 1 min for turbulence studies (e.g., Weckwerth et al. 1999 ; Turner et al. 2014 ; Wulfmeyer et al. 2015 ). The differential absorption lidar (DIAL) validated

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Guo Lin, Coltin Grasmick, Bart Geerts, Zhien Wang, and Min Deng

) sampled the collision between the cold front and outflow boundary, around 0220 UTC. The airborne lidars provide information about the vertical structure of WVMR, LSR, and virtual potential temperature θ υ ( Figs. 7a–c ). The profiling observations show 850-m-deep warmer, moister air (~15 g kg −1 of WVMR below 1.4 km MSL, Fig. 7a ) moving southward behind the cold front approaching the MCS. At the same time, cooler, drier (~8 g kg −1 ) outflow from the parent MCS flowed northward. The MP1

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Elizabeth N. Smith, Joshua G. Gebauer, Petra M. Klein, Evgeni Fedorovich, and Jeremy A. Gibbs

, and NLLJs. Primary data sources for NLLJ cases were the boundary layer profiles measured by mobile and fixed PECAN Integrated Sounding Arrays (PISAs). These datasets included profiles of dynamic and thermodynamic parameters obtained at high temporal and vertical resolution using Doppler lidars, Atmospheric Emitted Radiance Interferometers (AERIs), radar wind profilers, radiosondes, and microwave radiometers (MWRs), providing observations to describe the SBL and NLLJ evolution. There were four

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Coltin Grasmick, Bart Geerts, David D. Turner, Zhien Wang, and T. M. Weckwerth

lidars aboard the UWKA. These observations are used in an effort to understand the lifting mechanism and effective source level for CI in LC regions. Specifically, we use the airborne lidar data to determine the actual vertical displacement for parcels at all possible source levels and compare this against the vertical distance to the parcel’s LFC. The UWKA completed a total of 20 transects across three convergent boundary zones leading this MCS. The first zone, referred to as Region I ( Fig. 2 ), is

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Dana Mueller, Bart Geerts, Zhien Wang, Min Deng, and Coltin Grasmick

associated with a nocturnal MCS. This observational case study is unique in that it explores the evolution of the vertical structure of a bore. While other fixed, surface-based profiling observations depict a blend of distance and time evolution, this study uses airborne profiling lidar data, providing a series of quasi-instantaneous vertical transects, at a time resolution corresponding with the frequency of flight traverses across the bore (in this case 10–15 min). Another advantage to using an

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Hristo G. Chipilski, Xuguang Wang, David B. Parsons, Aaron Johnson, and Samuel K. Degelia

wind lidar (DWL), whose ability to capture the fine-scale structure of the wind field makes it particularly suitable for use in high-resolution numerical models. The forecast potential of DWL retrievals was first demonstrated by Zhang and Pu (2011) on a warm-season mesoscale convective system (MCS). Kawabata et al. (2014) confirmed the NWP value of this instrument and further discussed the important synergy between lidar and radar observations in improving the overall convective skill. The

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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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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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Bore-ing into Nocturnal Convection

Kevin R. Haghi, Bart Geerts, Hristo G. Chipilski, Aaron Johnson, Samuel Degelia, David Imy, David B. Parsons, Rebecca D. Adams-Selin, David D. Turner, and Xuguang Wang

the elevated convective available potential energy (CAPE) and convective inhibition for specific source layers within/above the SBL (e.g., Grasmick et al. 2018 ) requires accurate temperature and water vapor profile observations. AERIs are able to make reasonably accurate water vapor measurements in the lowest 1–2 km above the ground, but the information content decreases markedly above this level ( Turner and Löhnert 2014 ). Additional observations, such as those from a water vapor lidar, can be

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