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

between ENSO phases. The TRMM PR rainfall algorithm is known to underestimate precipitation produced by deep convection over land ( Iguchi et al. 2009 ; Rasmussen et al. 2013 ). Therefore, rainfall was instead estimated with the Z – R relationship used by Rasmussen et al. (2013) , Z = aR b , where Z is the radar reflectivity factor (mm 6 m −3 ) and R is the corrected rain rate (mm h −1 ). The parameters a and b are constants based on rain type. The values used to calculate rainfall in

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

semislip with the surface exchange coefficient for momentum ( C D ) based on Fairall et al. (2003) at low-to-mid wind speeds, and Donelan et al. (2004) at higher wind speeds (the default option in CM1), while the (constant) surface exchange coefficient for enthalpy ( C E ) was based on the specified land-use index. The top boundary condition was rigid and free slip. A Rayleigh damping layer (coefficient = 3.33 × 10 −3 s −1 ) was applied above 15 km to minimize the artifacts of the rigid top

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Hernán Bechis, Paola Salio, and Juan José Ruiz

distribution, relation to diurnal and seasonal cycles, and associated synoptic conditions) have also been studied over different regions using different datasets and detection criteria. Usually, a conservative moisture variable is utilized to identify the dryline—either specific humidity or mixing ratio—although some works use near-surface dewpoint temperature. In addition, conditions over the temperature field are often included to eliminate surface fronts, and on occasions, a wind shift across the

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Adam C. Varble, Stephen W. Nesbitt, Paola Salio, Joseph C. Hardin, Nitin Bharadwaj, Paloma Borque, Paul J. DeMott, Zhe Feng, Thomas C. J. Hill, James N. Marquis, Alyssa Matthews, Fan Mei, Rusen Öktem, Vagner Castro, Lexie Goldberger, Alexis Hunzinger, Kevin R. Barry, Sonia M. Kreidenweis, Greg M. McFarquhar, Lynn A. McMurdie, Mikhail Pekour, Heath Powers, David M. Romps, Celeste Saulo, Beat Schmid, Jason M. Tomlinson, Susan C. van den Heever, Alla Zelenyuk, Zhixiao Zhang, and Edward J. Zipser

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was recently completed over a 7-month period from October 2018 through April 2019 in the Sierras de Córdoba (SDC) range of central Argentina. A primary goal was to use the high frequency of orographically initiated convective clouds to comprehensively study the complex interactions between meteorology, aerosols, complex terrain, and convective

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Stephen W. Nesbitt, Paola V. Salio, Eldo Ávila, Phillip Bitzer, Lawrence Carey, V. Chandrasekar, Wiebke Deierling, Francina Dominguez, Maria Eugenia Dillon, C. Marcelo Garcia, David Gochis, Steven Goodman, Deanna A. Hence, Karen A. Kosiba, Matthew R. Kumjian, Timothy Lang, Lorena Medina Luna, James Marquis, Robert Marshall, Lynn A. McMurdie, Ernani de Lima Nascimento, Kristen L. Rasmussen, Rita Roberts, Angela K. Rowe, Juan José Ruiz, Eliah F.M.T. São Sabbas, A. Celeste Saulo, Russ S. Schumacher, Yanina Garcia Skabar, Luiz Augusto Toledo Machado, Robert J. Trapp, Adam C. Varble, James Wilson, Joshua Wurman, Edward J. Zipser, Ivan Arias, Hernán Bechis, and Maxwell A. Grover

cataloged during RELAMPAGO included sites contributed by agricultural, livestock, and water agencies as well as the private sector in Argentina, southern Brazil, and Uruguay. During the RELAMPAGO operations, fixed and mobile platforms were used to collect observations of the thermodynamic and kinematic environment, storm structures, lightning, precipitation, and land surface states and fluxes. Some of these observations were continuous, while others targeted phenomena during the campaign based on

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

( Morrison 2017 ); thus, initial updraft width could be one factor governing CI. Numerical representation of updraft size and vertical mass flux is sensitive to the model grid resolution (e.g., Bryan et al. 2003 ; Varble et al. 2014 ; Varble et al. 2020 ; Hirt et al. 2020 ), as well as other physical parameterizations, limiting what can be ascertained about updraft-environment interactions using convection-allowing mesoscale models. A more complete understanding of CI requires synchronized

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Kristen L. Rasmussen, Melissa A. Burt, Angela Rowe, Rebecca Haacker, Deanna Hence, Lorena Medina Luna, Stephen W. Nesbitt, and Julie Maertens

Ocean (RICO) field campaign developed a robust educational plan that included having students direct a complete scientific mission for the first time (Rauber et al. 2007). Airborne, ground-based, and ship-based platforms have all been used in educational efforts to include graduate and undergraduate students in field research ( Fabry et al. 1995 ; Hallett et al. 1993 ; Borys and Wetzel 1997 ; Wetzel et al. 2003 ; Rauber et al. 2007; Kristovich et al. 2017 ). Building on this tradition, the

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

-to-bowing MCS transition has been documented before in the USA by Moller et al. (1990) , Conway et al. (1996) , Finley et al. (2001) , Klimowski et al. (2004) , among others, although not explicitly orographic supercell-to-bowing MCS transitions. Finley et al. (2001) used a numerical modeling approach to analyze a supercell-to-bowing MCS transition, determining that just prior to UCG, the low-level cold pool deepened and intensified, which resulted in a strong low-level horizontal pressure gradient

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