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H. Reed Ogrosky and Samuel N. Stechmann

average is removed from each dynamical field prior to the regression in order to remove the effects of low-frequency variability due to, for example, ENSO. A separate regression equation is solved for each variable at each longitude, latitude, pressure level, and time lag. The resulting linear regression coefficients are then used to produce a composite picture of the evolution of each wave type. In these composites, the winds are plotted only at locations where they are deemed to be significant at

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Ronald L. Holle, Kenneth L. Cummins, and William A. Brooks

stroke location accuracy (LA) have been validated over Florida ( Mallick et al. 2014 ). The validation showed a GLD360 CG flash DE (relative to the NLDN in Florida) of 67%, a CG stroke DE of 37%, and a CG stroke median LA of 2.0 km. The performance of GLD360 over North America is estimated to be a CG flash DE of 70% and a median CG stroke LA of 2–5 km. GLD360 stroke densities in the second portion of this study are also in 20 km by 20 km grid squares within geographical boundaries extending beyond

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Abdullah Kahraman, Şeyda Tilev-Tanriover, Mikdat Kadioglu, David M. Schultz, and Paul M. Markowski

most likely in interior Turkey, although the maximum in hail days lies in northeastern Turkey, where peak frequencies approach 2 hail days per month. As hail frequencies decline in late summer and fall toward the winter minimum, hail probabilities decline most slowly in extreme northeastern Turkey. Fig . 6. Locations of large and very large hail cases in Turkey and topography. Fig . 7. Geographical distribution of all hail days (shaded) and locations of severe hail (red triangles) per month. All

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Eric B. Wendoloski, David R. Stauffer, and Astrid Suarez

model with terrain-following vertical coordinates and Arakawa C horizontal gridpoint staggering ( Skamarock et al. 2008 ). The WRF configuration includes four one-way nested domains of 12-, 4-, 1.3-, and 0.4-km horizontal grid spacing with the 1.3- and 0.4-km nests centered over central Pennsylvania and the Nittany Valley ( Fig. 1a ). The location and topography of the 0.4-km domain with respect to the topography of the 1.3-km domain are shown in Figs. 1b and 1c . Initial and lateral boundary

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Anthony G. Barnston, Nicolas Vigaud, Lindsey N. Long, Michael K. Tippett, and Jae-Kyung E. Schemm

still needs a more convincing demonstration in the observations and/or the model, and we return to Table 4 . Table 4. (top) Location key of seven geographical subsectors. (bottom) The percentage of the average over the eight MJO phases, of ACE in (left) observations and (right) in the T382 CFS model, by the MJO phase for each of the seven geographical subsectors in the North Atlantic. The average entry for any subsector, over the 8 phases, is 100. The location of the cells within each table

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George R. Alvey III, Jonathan Zawislak, and Edward Zipser

intensifying TCs [particularly those that undergo rapid intensification (RI)] have a higher proportion of convective bursts within the inner core? 3) If important, is there a favored location for these bursts during intensification? Rodgers et al. (1998 , 2000) and Guimond et al. (2010) analyzed several TCs in which intense convective bursts precede or are coincident with the start of RI. One hypothesis for how convective bursts are favorable for intensification is that they moisten the middle

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Joshua Aikins, Katja Friedrich, Bart Geerts, and Binod Pokharel

and rotated with the mean axis of the Sierra Madre Range is also overlaid with a red box, which is the analysis region used in the Hovmöller diagram in Fig. 2 . The purple wedge starting from the DOW location at Battle Pass indicates the range of targeted DOW RHI azimuths used to calculate median profiles of Z and Z DR ( Fig. 7 ). (b) The DOW radar beam height (km AGL) calculated for an elevation scan of 0°. (c) The calculated fraction of the DOW radar beam that is blocked for a 0° elevation

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Claire L. Vincent and Todd P. Lane

active period as 5–17 January. Fig . 2. MJO indices from Wheeler–Hendon method (solid) and OMI method (dashed). Each dot represents one day in January 2010. Colors on both curves indicate the movement of the MJO through the Maritime Continent as defined by the WH index: lead up 1–9 Jan (green), active 10–19 Jan (red), and follow-on 20–31 Jan (blue). Geographic locations follow the convention of Wheeler and Hendon (2004) . Fig . 3. (a) Time series of relative humidity, (b) cloud fraction, (c) CAPE

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Clémence Macron, Yves Richard, Thomas Garot, Miloud Bessafi, Benjamin Pohl, Adolphe Ratiarison, and Andrianaharimanana Razafindrabe

. Fig . 1. (a) Location of the 37 daily rainfall stations and percentage of missing values. The dot size is proportional to the percentage of missing values (stations with fewer missing values are larger); the colors also represent the percentage of missing values (see color scale for legend). Names cited in the text appear in red for stations, in blue for ocean sectors, and in brown for mountains. (b) Temporal distribution of the missing values for each of the 37 stations for NDJF 1971–99. (c) Mean

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Andrew I. Barrett, Suzanne L. Gray, Daniel J. Kirshbaum, Nigel M. Roberts, David M. Schultz, and Jonathan G. Fairman Jr.

of potentially high-impact weather: terrain-locked convective bands. In particular, we study four recent such events in the United Kingdom to determine whether convection-permitting ensemble simulations succeed in accurately representing the bands. Specifically, we address the following questions: Do convection-permitting ensembles capture the structure, location, timing, intensity, and duration of quasi-stationary convective bands? What evaluation methods provide useful insights into forecast

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