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Hristo G. Chipilski, Xuguang Wang, and David B. Parsons

et al. 2017 ). The dynamical significance of convective outflow boundaries has prompted the scientific community to create automated algorithms for identifying and tracking these features. The earliest algorithm developed for this purpose was entirely based on observational data and closely connected to the procurement plans for the Next Generation Weather Radar (NEXRAD) system (e.g., Crum and Alberty 1993 ). In particular, Uyeda and Zrnić (1986) as well as Smith et al. (1989) were the first

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J. C. Hubbert

, Richardson et al. (2017) examine Bragg scatter via an automated identification algorithm as a means to assess the bias of the NEXRADs. The analysis spans a year, but the Bragg estimates are daily averages. Another technique uses the principle of radar reciprocity that states that the two cross-polar backscatter cross sections are equal ( Saxon 1955 ; Hubbert et al. 2003 ). This has been termed the cross-polar power (CP) technique, which uses the integrated ratios of the two CP powers, along with

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

algorithm . IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. , 12 , 1339 – 1354 , . 10.1109/JSTARS.2018.2874968 Turner , D. D. , J. E. M. Goldsmith , and R. A. Ferrare , 2016 : Development and applications of the ARM Raman Lidar. The Atmospheric Radiation Measurement (ARM) Program: The First 20 Years, Meteor. Monogr. , No. 57 , . 10.1175/AMSMONOGRAPHS-D-15-0026.1 UCAR/NCAR–EOL , 2015 : FP1 ARM

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Thomas R. Parish


Detailed ground-based and airborne measurements were conducted of the summertime Great Plains low-level jet (LLJ) in central Kansas during the Plains Elevated Convection at Night (PECAN) campaign. Airborne measurements using the University of Wyoming King Air were made to document the vertical wind profile and the forcing of the jet during the nighttime hours on 3 June 2015. Two flights were conducted that document the evolution of the LLJ from sunset to dawn. Each flight included a series of vertical sawtooth and isobaric legs along a fixed track at 38.7°N between longitudes 98.9° and 100°W.

Comparison of the 3 June 2015 LLJ was made with a composite LLJ case obtained from gridded output from the North American Mesoscale Forecast System for June and July of 2008 and 2009. Forcing of the LLJ was detected using cross sections of D values that allow measurement of the vertical profile of the horizontal pressure gradient force and the thermal wind. Combined with observations of the actual wind, ageostrophic components normal to the flight track can be detected. Observations show that the 3 June 2015 LLJ displayed classic features of the LLJ, including an inertial oscillation of the ageostrophic wind. Oscillations in the geostrophic wind as a result of diurnal heating and cooling of the sloping terrain are not responsible for the nocturnal wind maximum. Net daytime heating of the sloping Great Plains, however, is responsible for the development of a strong background geostrophic wind that is critical to formation of the LLJ.

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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

deemed the potential for bore development medium-to-high based on a theory-based algorithm that characterized the conditions necessary to develop and sustain a bore. The theoretical parameters of the algorithm were calculated from data produced in multiple convection-allowing models, including experimental ones. Urgently, the bore lead scientist disseminated the travel plans for the mobile PECAN Integrated Sounding Arrays (mPISAs), the mobile sounding vehicles, and the mobile radar trucks through

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W. G. Blumberg, T. J. Wagner, D. D. Turner, and J. Correia Jr.

the diurnal cycle. After that, a comparison of the convection indices derived from the AERI instrument to those derived from radiosondes is performed. In section 2 , we describe the AERI instrument and the retrieval algorithm used to derive the thermodynamic profiles from the AERI observations. In section 3 , we describe the data and methods used to develop this comparison. In section 4 , comparisons of the AERI profiles to radiosondes released over the diurnal cycle will reveal where the

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

observations ( Turner et al. 2006 ). The retrieval of temperature and water vapor profiles, as well as cloud properties, is possible via the principle that channels that are close to the center of the absorption lines are more opaque and thus more sensitive to low altitudes, while channels away from the center of the absorption lines are more transparent and can thus provide information from higher altitudes ( Wulfmeyer et al. 2015 ). The AERI retrieval algorithm used in this analysis is the advanced

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Tammy M. Weckwerth and Ulrike Romatschke

was termed the radar domain , which encompassed the U.S. Great Plains and extended west to the Rocky Mountains. The radar domain was the entire area shown in Fig. 1 , whereas the core PECAN domain is denoted by the black rectangle. QPE was derived using a hydrometeor classification algorithm to determine which rain-rate relationship was appropriate at each location ( Giangrande and Ryzhkov 2008 ; Berkowitz et al. 2013 ; Dixon et al. 2015 ). The NCAR particle identification (PID) algorithm

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Stacey M. Hitchcock, Russ S. Schumacher, Gregory R. Herman, Michael C. Coniglio, Matthew D. Parker, and Conrad L. Ziegler

made it to 3 km is listed in Table 1 by case. A cluster analysis is a method of objectively grouping a set of objects based on shared characteristics. Different clustering algorithms have different advantages and disadvantages. The Shared Nearest Neighbors (SSN) method was selected for this application because unlike other methods, it handles variable density clusters, does not require the number of clusters to be specified in advance, and does not force every point into a cluster (i.e., outliers

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Samuel K. Degelia, Xuguang Wang, and David J. Stensrud

collected during both IOP 15 (25 June) and IOP 16 (26 June) and varied throughout the assimilation period ( Figs. 3a–c ). These include AERIs [~5-min thermodynamic profiles produced by the AERIoe retrieval algorithm in Turner and Löhnert (2014 )], Doppler lidars, radio wind profilers, rawinsondes, and surface observations ( Table 1 ; Fig. 2 ). The PECAN observations were obtained from the PECAN field catalog (available online at ) in June 2018. Each instrument within

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