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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

, and D. J. Cecil , 2019 : Subtropical South American hailstorm characteristics and environments . Mon. Wea. Rev. , 147 , 4289 – 4304 , . 10.1175/MWR-D-19-0011.1 Bunkers , M. J. , B. A. Klimowski , J. W. Zeitler , R. L. Thompson , and M. L. Weisman , 2000 : Predicting supercell motion using a new hodograph technique . Wea. Forecasting , 15 , 61 – 79 ,<0061:PSMUAN>2.0.CO;2 . 10

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

the RMA1 data. ERA-Interim is a global (atmospheric) reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA-Interim includes 3-h surface data and 6-h upper-air data and utilizes four-dimensional variational data assimilation techniques. The horizontal grid spacing is 0.7° (approximately 80 km) with 37 vertical model levels ( P top = 1 hPa); pressure-level interpolated data were used for this analysis. Reanalysis data were used to construct composites of

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James N. Marquis, Adam C. Varble, Paul Robinson, T. Connor Nelson, and Katja Friedrich

1. Introduction Correctly representing moist convective processes is critical to accurately predicting regional and global weather and climate, and accompanying operational forecasting of near- and long-term hydrology and severe weather. Numerical simulations rely on a mix of cumulus, turbulence, microphysics, and planetary boundary layer parameterization schemes to represent the generation of shallow and deep moist updrafts and precipitation (e.g., Tiedtke 1989 ; Kain and Fritsch 1990

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Hernán Bechis, Paola Salio, and Juan José Ruiz

Regional Reanalysis (NARR). A detection procedure suitable for high-resolution numerical models is presented by Clark et al. (2015) . Their algorithm includes image processing and pattern recognition techniques applied to various meteorological fields, and machine learning algorithms to further refine the results. This last step helps to better distinguish drylines from other low-level boundaries such as cold fronts intersecting a dryline, weak cold fronts, among others. In the literature, several

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