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Jeremiah O. Piersante, Russ. S. Schumacher, and Kristen L. Rasmussen

America), and timeframe, as South America runs were initially used for forecasting during the RELAMPAGO field campaign from 4 October 2018 to 23 February 2019. The atmospheric profile forecast analysis in section 4 focuses on the comparison of this 2018/19 South America warm season to the 2016 North America warm season (initializations from 1 May to 30 September). Forecasts were verified against operational radiosonde observations occurring daily at 0000 and 1200 UTC ( Satellite Services Division

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T. Connor Nelson, James Marquis, Adam Varble, and Katja Friedrich

1. Introduction Incorrect forecasts of the specific timing and location of the initiation of deep moist convection in operational models are a major factor limiting the predictability of severe weather, hydrology, and accuracy of quantitative precipitation forecasting (e.g., Davis et al. 2003 ; Weisman et al. 2008 ; Duda and Gallus 2013 ). Operational predictability of deep moist convection initiation (CI) is limited by a number of factors, including our ability to routinely sample

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Robert J. Trapp, Karen A. Kosiba, James N. Marquis, Matthew R. Kumjian, Stephen W. Nesbitt, Joshua Wurman, Paola Salio, Maxwell A. Grover, Paul Robinson, and Deanna A. Hence

south of the observing domain. The low-level flow north of the boundary had a northeasterly (i.e., upslope) component, which was also expected to aid CI, as was a northward surging cold pool generated by convective storms ongoing during the morning. However, the forecasted evolution of the boundary, terrain, and cold pool interactions relative to the evolution of CAPE and CIN was rather complex. The observational-strategy planning for IOP4 was further complicated by the operational constraint that

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Russ S. Schumacher, Deanna A. Hence, Stephen W. Nesbitt, Robert J. Trapp, Karen A. Kosiba, Joshua Wurman, Paola Salio, Martin Rugna, Adam C. Varble, and Nathan R. Kelly

situ or ground-based observations of storms and their environments are available. Operational radiosonde sites in Argentina are widely spaced, and often only take one sounding per day at 1200 UTC. Radar measurements in Argentina were driven by local efforts with very few sites available until 2015 when a new network started to slowly fill the gaps ( de Elía et al. 2017 ). The radar data, though, have limitations for studying convective storms (e.g., Mulholland et al. 2018 ). These gaps in data

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Sujan Pal, Francina Dominguez, María Eugenia Dillon, Javier Alvarez, Carlos Marcelo Garcia, Stephen W. Nesbitt, and David Gochis

et al. 2019 ) and also used for twice-daily operational forecasting during RELAMPAGO IOPs. We used different meteorological data as initial and boundary conditions to force WRF ( Table 1 ). For example, ERA5–WRF uses ERA5 data as forcing for WRF. Similarly, GFS–WRF uses GFS while local ensemble transform Kalman filter (LETKF)–WRF uses GFS+GEFS (Global Ensemble Forecast System) for the boundary conditions (see Table 1 ). WRF generated precipitation was interpolated to WRF-Hydro grid (1 km) to

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Matthew R. Kumjian, Rachel Gutierrez, Joshua S. Soderholm, Stephen W. Nesbitt, Paula Maldonado, Lorena Medina Luna, James Marquis, Kevin A. Bowley, Milagros Alvarez Imaz, and Paola Salio

be supercells that form in environments that do not stand out among those associated with more “typical” supercells producing smaller hail. The radar signatures of storms with gargantuan or giant hail often are not particularly noteworthy, either, except perhaps stronger mesocyclonic rotation and divergence aloft. This implies that features commonly used by operational meteorologists to forecast and monitor severe storms may only be subtly different for extreme-hail-producing storms, making

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Jake P. Mulholland, Stephen W. Nesbitt, Robert J. Trapp, Kristen L. Rasmussen, and Paola V. Salio

. (2012) , have revealed that most tornado and large hail reports originate from supercellular convection, whereas damaging straight-line wind gusts predominantly occur with larger mesoscale convective systems. Similar studies have been largely absent across Argentina, however, as high spatiotemporal radar, surface, and upper-air observations are sparse, and a standard severe weather reporting procedure has not yet been implemented operationally at the time of this publication. The aim of the current

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Zachary S. Bruick, Kristen L. Rasmussen, and Daniel J. Cecil

, including increased instability through low-level temperature and moisture increases, an enhanced lee trough and SALLJ, and stronger upper-level jet streams. While hailstorms in subtropical South America are supported synoptically by similar conditions to those found in the United States, the storm mode and diurnal cycles of these storms are very different. As a result, they prove to be a challenge to forecast from numerical and operational perspectives, as the knowledge gained by studying U

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Jake P. Mulholland, Stephen W. Nesbitt, and Robert J. Trapp

models and human-driven ingredients-based approach forecasting of when storms grow upscale into MCSs have been shown to have low skill (e.g., Done et al. 2004 ; Hawblitzel et al. 2007 ; Weisman et al. 2013 ; Peters et al. 2017 ). Previous studies on UCG, such as Coniglio et al. (2010 , 2011) , have found that steep low-level lapse rates, high precipitable water, large convective available potential energy (CAPE), strengthening low-level horizontal convergence at the terminus of a low-level jet

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Zachary S. Bruick, Kristen L. Rasmussen, Angela K. Rowe, and Lynn A. McMurdie

frequency for storms during El Niño. The number of pixels that went into each composite is listed in the top right corner of each panel. ENSO also modifies the synoptic environment which supports deeper convective storms during El Niño ( Figs. 4 – 5 ). While differences in the synoptic environment associated with El Niño and La Niña were discussed earlier, a more detailed look at changes to instability yields new insights. Convective available potential energy (CAPE) is often used as a forecasting

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