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Leah S. Campbell, W. James Steenburgh, Peter G. Veals, Theodore W. Letcher, and Justin R. Minder

is designed to work with both rain and snow and offers an improvement over the rain-specific algorithm used in generic Metek processing. The method includes noise removal, dealiasing, the calculation of equivalent radar reflectivity factor (hereafter simply “reflectivity”), removal of the top one and bottom two range gates, and averaging of the data to 60-s intervals. As discussed in Minder et al. (2015) , a brief intercomparison of the MRRs used at SIB, SC, and NR revealed only a small (<3 dB Z

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Justin R. Minder, Theodore W. Letcher, Leah S. Campbell, Peter G. Veals, and W. James Steenburgh

; Löffler-Mang et al. 1999 ; Peters et al. 2002 ). Maahn and Kollias (2012) developed a postprocessing algorithm for the MRR raw data that improves noise removal, velocity dealiasing, and sensitivity. Importantly, this algorithm allows for the collection of high-quality profiles of equivalent radar reflectivity factor , Doppler radial velocity , and spectral width in both rain and snow. We apply the Maahn and Kollias (2012) algorithm to all of our MRR data. Data are also averaged to 60-s time

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Philip T. Bergmaier and Bart Geerts


The vast majority of lake-effect snow research throughout the years has focused on the North American Great Lakes since they are often associated with strong lake-effect events that produce heavy downstream snowfall. This study investigates a lake-effect snow event that instead occurred over two smaller lakes, the New York Finger Lakes, which are just O(5) km wide and O(50) km long. A pair of well-defined snowbands that formed over Seneca and Cayuga Lakes, the two largest of the Finger Lakes, were sampled from above by a vertically pointing Doppler radar and lidar on board the University of Wyoming King Air (UWKA). With typical widths matching the widths of the lakes, and depths of less than 1000 m, the long-lake-axis-parallel bands were actually quite intense for their size. For example, updrafts of 2–3 m s−1 or greater within the band cores were common, and reflectivity occasionally exceeded 5 dBZ. Airborne dual-Doppler data show that both bands were sometimes accompanied by a well-defined thermally driven secondary circulation. Lidar data reveal that the Cayuga Lake band contained significantly more liquid water than the band over Seneca Lake, which was composed mainly of ice. Dissipating lake-effect ice clouds, carried downstream from Lake Ontario toward Seneca Lake, likely seeded the emerging convection over Seneca Lake, resulting in an accelerated depletion of liquid in the Seneca Lake band via more efficient snow growth.

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Dan Welsh, Bart Geerts, Xiaoqin Jing, Philip T. Bergmaier, Justin R. Minder, W. James Steenburgh, and Leah S. Campbell

). The DOW is a 9.35/9.50-GHz (~3-cm wavelength, X band) scanning dual-polarization, dual-frequency radar with a beamwidth of 0.93°. At low-elevation scans, the return power at many DOW radar gates is suspect because of ground clutter, contamination from radar side lobes, anomalous propagation of the radar beam, and other interferences with the underlying surface. Removal of ground clutter is performed using a fuzzy logic algorithm based on the density function for snow and ground clutter ( Gourley

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Scott M. Steiger, Tyler Kranz, and Theodore W. Letcher

more than 90% and a median location accuracy of better than 300 m. We do not know if the FDE is different for lake-effect storms versus other storms. Cloud FDE is estimated to be 50%–60% ( Murphy and Nag 2015 ). No minimum peak current thresholds were used in CG definitions for this study (e.g., Cummins and Murphy 2009 ). We manually grouped the NLDN CG stroke and IC pulse data (provided by Vaisala, Inc.) into CG/IC flashes via the grouping algorithm described in Cummins et al. (1998) . Assuming

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David A. R. Kristovich, Richard D. Clark, Jeffrey Frame, Bart Geerts, Kevin R. Knupp, Karen A. Kosiba, Neil F. Laird, Nicholas D. Metz, Justin R. Minder, Todd D. Sikora, W. James Steenburgh, Scott M. Steiger, Joshua Wurman, and George S. Young

hydrometeor classification algorithms. When the X-band (3-cm wavelength) DOWs were deployed along the east and east-southeast coast of Lake Ontario, contemporaneous data from the dual-polarization S-band (10-cm wavelength) Weather Surveillance Radar-1988 Doppler (WSR-88D) radar in Montague, New York (KTYX), permitted comparisons between dual-polarization fields and particle identification schemes. Inland convective transition of lake-effect plumes. Boundary layer convection is a frequent occurrence within

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Peter G. Veals and W. James Steenburgh

during the LEPs. Although the time between volume scans can vary from 5–12 min due to changes in radar scan mode, no attempt was made to apply a time-weighting algorithm because the events with longer-interval scans typically feature weaker, less persistent echoes and have a correspondingly small influence on the results. c. Additional datasets Conditional radar statistics were generated using data from the North American Regional Reanalysis (NARR; Mesinger et al. 2006 ), which was obtained from the

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Karen A. Kosiba, Joshua Wurman, Kevin Knupp, Kyle Pennington, and Paul Robinson

frozen precipitation was expected. Comparison to closest WSR-88D (Montague, NY; KTYX) revealed a similar Z DR and ρ hv fields. Using the SUNY sounding from 0459 UTC, hydrometeor identification algorithms ( Vivekanandan et al. 1999 ), specific to the radar frequency, were applied to both the X-band DOW and the S-band KTYX data. Retrievals from the DOWs and KTYX indicated that the band was comprised mainly of “dry snow” at all times, with the periphery of the band comprising snow crystals ( Fig. 19

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Jake P. Mulholland, Jeffrey Frame, Stephen W. Nesbitt, Scott M. Steiger, Karen A. Kosiba, and Joshua Wurman

. Rev. , 134 , 311 – 335 , doi: 10.1175/MWR3065.1 . 10.1175/MWR3065.1 Barnes , S. L. , 1964 : A technique for maximizing details in numerical weather map analysis . J. Appl. Meteor. , 3 , 396 – 409 , doi: 10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2 . 10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2 Bell , M. M. , W. C. Lee , C. A. Wolff , and H. Cai , 2013 : A Solo-based automated quality control algorithm for airborne tail Doppler radar data . J. Appl. Meteor. Climatol

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Seth Saslo and Steven J. Greybush

forecast lead time. To further investigate this, the LES band is identified in simulated and observed radar imagery. This is accomplished by applying an edge detection algorithm to identify strong reflectivity gradients and classifying closed contours of these gradients as LES band objects. An example of an identified object is given by the black contour in Fig. 1 . Once an object is identified, attributes such as centroid, length, area, and orientation can be calculated. Table 3 presents a

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