• Alvarez Imaz, M., P. Salio, M. E. Dillon, and L. Fita, 2021: The role of atmospheric forcings and WRF physical set-up on convective initiation over Córdoba, Argentina. Atmos. Res., 250, 105335, https://doi.org/10.1016/j.atmosres.2020.105335.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Amitai, E., W. Petersen, X. Llort, and S. Vasiloff, 2012: Multiplatform comparisons of rain intensity for extreme precipitation events. IEEE Trans. Geosci. Remote Sens., 50, 675686, https://doi.org/10.1109/TGRS.2011.2162737

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baldauf, M., A. Seifert, J. Forstner, D. Majewski, M. Raschendorfer, and T. Reinhardt, 2011: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Wea. Rev., 139, 38873905, https://doi.org/10.1175/MWR-D-10-05013.1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barthlott, C., U. Corsmeier, C. Meißner, F. Braun, and C. Kottmeier, 2006: The influence of mesoscale circulation systems on triggering convective cells over complex terrain. Atmos. Res., 81, 150175, https://doi.org/10.1016/j.atmosres.2005.11.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buehner, M., J. Morneau, and C. Charette, 2013: Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction. Nonlinear Processes Geophys., 20, 669682, https://doi.org/10.5194/npg-20-669-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Casanovas, C., P. Salio, V. Galligani, B. Dolan, and S. W. Nesbitt, 2021: Drop size distribution variability in Central Argentina during RELAMPAGO-CACTI. Remote Sensing, 13, 2026, https://doi.org/10.3390/rs13112026.

    • Search Google Scholar
    • Export Citation
  • Clark, P., N. Roberts, H. Lean, S. P. Ballard, and C. Charlton-Perez, 2016: Convection-permitting models: A step-change in rainfall forecasting. Meteor. Appl., 23, 165181, https://doi.org/10.1002/met.1538.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dillon, M. E., and Coauthors, 2016: Application of the WRF-LETKF data assimilation system over southern South America: Sensitivity to model physics. Wea. Forecasting, 31, 217236, https://doi.org/10.1175/WAF-D-14-00157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dillon, M. E., and Coauthors, 2021: A Rapid Refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign. Atmos. Res., 264, 105858, https://doi.org/10.1016/j.atmosres.2021.105858.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and J. A. Weyn, 2016: Thunderstorms do not get butterflies. Bull. Amer. Meteor. Soc., 97, 237243, https://doi.org/10.1175/BAMS-D-15-00070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., 2009: Neighborhood verification: A strategy for rewarding close forecasts. Wea. Forecasting, 24, 14981510, https://doi.org/10.1175/2009WAF2222251.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • García Skabar, Y., C. Matsudo, M. P. Hobouchian, M. Sacco, J. Ruiz, and S. Righetti, 2018: Implementación del modelo WRF en alta resolución en el Servicio Meteorológico Nacional. CONGREMET XIII, Rosario, Argentina, CAM, http://cenamet.org.ar/congremet/wp-content/uploads/2018/11/T0135_GARCÍASKABAR.pdf.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., and J. Juras, 2006: Measuring forecast skill: Is it real skill or is it the varying climatology? Quart. J. Roy. Meteor. Soc., 132, 29052923, https://doi.org/10.1256/qj.06.25.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945, https://doi.org/10.1175/1520-0493(1994)122≤0927:TSMECM≥2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, B., 2014: How does the skill of global model precipitation forecasts over Europe depend on spatial scale? University of Reading, 88 pp., http://www.met.reading.ac.uk/∼sws00rsp/teaching/postgrad/jones.pdf.

    • Search Google Scholar
    • Export Citation
  • Kacan, K. G., and Z. J. Levo, 2019: Microphysical and dynamical effects of mixed-phase hydrometeors in convective storms using a bin microphysics model: Melting. Mon. Wea. Rev., 147, 44374460, https://doi.org/10.1175/MWR-D-18-0032.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Coauthors, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931952, https://doi.org/10.1175/WAF2007106.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and Coauthors, 2020: Gargantuan hail in Argentina. Bull. Amer. Meteor. Soc., 101, E1241E1258, https://doi.org/10.1175/BAMS-D-19-0012.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., K. Ikeda, G. Thompson, R. Rasmussen, and J. Dudhia, 2011: High-resolution simulations of wintertime precipitation in the Colorado headwaters region: Sensitivity to physics parameterizations. Mon. Wea. Rev., 139, 35333553, https://doi.org/10.1175/MWR-D-11-00009.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loken, E. D., A. J. Clark, M. Xue, and F. Kong, 2017: Comparison of next-day probabilistic severe weather forecasts from coarse- and fine-resolution CAMs and a convection-allowing ensemble. Wea. Forecasting, 32, 14031421, https://doi.org/10.1175/WAF-D-16-0200.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahoney, K. M., 2016: The representation of cumulus convection in high-resolution simulations of the 2013 Colorado Front Range flood. Mon. Wea. Rev., 144, 42654278, https://doi.org/10.1175/MWR-D-16-0211.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matsudo, C., and P. Salio, 2011: Severe weather reports and proximity to deep convection over Northern Argentina. Atmos. Res., 100, 523537, https://doi.org/10.1016/j.atmosres.2010.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matsudo, C., Y. García Skabar, J. Ruiz, L. Vidal, and P. Salio, 2015: Verification of WRF-ARW convective-resolving forecasts over southern South America. Mausam, 66, 4454567.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McBride, J. L., and E. E. Ebert, 2000: Verification of quantitative precipitation forecasts from operational numerical weather prediction models over Australia. Wea. Forecasting, 15, 103121, https://doi.org/10.1175/1520-0434(2000)015≤0103:VOQPFF≥2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mezher, R. N., M. Doyle, and V. Barros, 2012: Climatology of hail in Argentina. Atmos. Res., 114–115, 7082, https://doi.org/10.1016/j.atmosres.2012.05.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 9911007, https://doi.org/10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mulholland, J. P., S. W. Nesbitt, R. J. Trapp, K. L. Rasmussen, and P. V. Salio, 2018: Convective storm life cycle and environments near the Sierras de Córdoba, Argentina. Mon. Wea. Rev., 146, 25412557, https://doi.org/10.1175/MWR-D-18-0081.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mulholland, J. P., S. W. Nesbitt, and R. J. Trapp, 2019: A case study of terrain influences on upscale convective growth of a supercell. Mon. Wea. Rev., 147, 43054324, https://doi.org/10.1175/MWR-D-19-0099.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nesbitt, S. W., and Coauthors, 2021: A storm safari in subtropical South America: Proyecto RELAMPAGO. Bull. Amer. Meteor. Soc., 102, E1621E1644, https://doi.org/10.1175/BAMS-D-20-0029.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penalba, O. C., and W. M. Vargas, 2004: Interdecadal and interannual variations of annual and extreme precipitation over central-northeastern Argentina. Int. J. Climatol., 24, 15651580, https://doi.org/10.1002/joc.1069.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Piersante, O. J., R. S. Schumacher, and K. L. Rasmussen, 2021: Comparison of biases in warm-season WRF forecasts in North and South America. Wea. Forecasting, 36, 9791001, https://doi.org/10.1175/WAF-D-20-0062.1.

    • Search Google Scholar
    • Export Citation
  • Potvin, C. K., and M. L. Flora, 2015: Sensitivity of idealized supercell simulations to horizontal grid spacing: Implications for warn-on-forecast. Mon. Wea. Rev., 143, 29983024, https://doi.org/10.1175/MWR-D-14-00416.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powers, J. G., and Coauthors, 2017: The Weather Research and Forecasting Model: Overview, system efforts, and future directions. Bull. Amer. Meteor . Soc., 98, 17171737, https://doi.org/10.1175/BAMS-D-15-00308.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmussen, K. L., M. D. Zuluaga, and R. A. Houze Jr., 2014: Severe convection and lightning in subtropical South America. Geophys. Res. Lett., 41, 73597366, https://doi.org/10.1002/2014GL061767.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rivelli Zea, L., S. W. Nesbitt, A. Ladino, J. C. Hardin, and A. Varble, 2021: Raindrop size spectrum in deep convective regions of the Americas. Atmosphere, 12, 979, https://doi.org/10.3390/atmos12080979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, N. M., and H. W. Lean, 2008: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon. Wea. Rev., 136, 7897, https://doi.org/10.1175/2007MWR2123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robinson, T. E., and S. Businger, 2019: A novel method for modeling lowest-level vertical motion. Wea. Forecasting, 34, 943957, https://doi.org/10.1175/WAF-D-18-0064.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salio, P., M. Nicolini, and E. J. Zipser, 2007: Mesoscale convective systems over southeastern South America and their relationship with the South American low-level jet. Mon. Wea. Rev., 135, 12901309, https://doi.org/10.1175/MWR3305.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, R. S., and Coauthors, 2021: Convective-storm environments in subtropical South America from high-frequency soundings during RELAMPAGO-CACTI. Mon .Wea. Rev., 149, 14391458, https://doi.org/10.1175/MWR-D-20-0293.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., 2014: Reproducing the September 2013 record-breaking rainfall over the Colorado Front Range with high-resolution WRF forecasts. Wea. Forecasting, 29, 393402, https://doi.org/10.1175/WAF-D-13-00136.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Search Google Scholar
    • Export Citation
  • Skok, G., and N. Roberts, 2016: Analysis of Fractions Skill Score properties for random precipitation fields and ECMWF forecasts. Quart. J. Roy. Meteor. Soc., 142, 25992610, https://doi.org/10.1002/qj.2849.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobash, R. A., J. S. Kain, D. R. Bright, A. R. Dean, M. C. Coniglio, and S. J. Weiss, 2011: Probabilistic forecast guidance for severe thunderstorms based on the identification of extreme phenomena in convection-allowing model forecasts. Wea. Forecasting, 26, 714728, https://doi.org/10.1175/WAF-D-10-05046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and Coauthors, 2009: Convective-scale warn-on-forecast system: A vision for 2020. Bull. Amer. Meteor. Soc., 90, 14871500, https://doi.org/10.1175/2009BAMS2795.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takemi, T., 2018: Importance of terrain representation in simulating a stationary convective system for the July 2017 northern Kyushu heavy rainfall case. SOLA, 14, 153158, https://doi.org/10.2151/sola.2018-027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Varble, A., and Coauthors, 2021: Utilizing a storm-generating hotspot to study convective cloud transitions: The CACTI experiment. Bull. Amer. Meteor. Soc., 102, E1597E1620, https://doi.org/10.1175/BAMS-D-20-0030.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vila, D. A., L. A. Toledo Machado, H. Laurent, and I. Velasco, 2008: Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) using satellite infrared imagery: Methodology and validation. Wea. Forecasting, 23, 233245, https://doi.org/10.1175/2007WAF2006121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Virtanen, P., and Coauthors, 2020: SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods, 17, 261272, https://doi.org/10.1038/s41592-019-0686-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ward, E., W. Buytaert, L. Peaver, and H. Wheater, 2011: Evaluation of precipitation products over complex mountainous terrain: A water resources perspective. Adv. Water Resour., 34, 12221231, https://doi.org/10.1016/j.advwatres.2011.05.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0–36-h explicit convective forecasts with the WRF-ARW model. Wea. Forecasting, 23, 407437, https://doi.org/10.1175/2007WAF2007005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whiteman, C., 2000: Mountain Meteorology: Fundamentals and Applications. Oxford University Press, 376 pp.

  • Yáñez-Morroni, G., J. Gironás, M. Caneo, R. Delgado, and R. Garreaud, 2018: Using the Weather Research and Forecasting (WRF) model for precipitation forecasting in an Andean region with complex topography. Atmosphere, 9, 304, https://doi.org/10.3390/atmos9080304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, K., and Coauthors, 2018: Evaluation of real-time convection-permitting precipitation forecasts in China during the 2013–2014 summer season. J. Geophys. Res. Atmos., 123, 10371064, https://doi.org/10.1002/2017JD027445.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zipser, E. J., D. J. Cecil, C. Liu, S. W. Nesbitt, and D. P. Yorty, 2006: Where are the most intense thunderstorms on Earth? Bull. Amer. Meteor. Soc., 87, 10571072, https://doi.org/10.1175/BAMS-87-8-1057.

    • Crossref
    • Search Google Scholar
    • Export Citation
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High-Resolution NWP Forecast Precipitation Comparison over Complex Terrain of the Sierras de Córdoba during RELAMPAGO-CACTI

Gimena CasarettoaServicio Meteorológico Nacional and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
bDepartamento de Ciencias de la Atmósfera y los Océanos, FCEN, Universidad de Buenos Aires, Buenos Aires, Argentina

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https://orcid.org/0000-0002-0963-8225
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Maria Eugenia DillonaServicio Meteorológico Nacional and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

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Paola SaliobDepartamento de Ciencias de la Atmósfera y los Océanos, FCEN, Universidad de Buenos Aires, Buenos Aires, Argentina
cCentro de Investigaciones del Mar y la Atmósfera (CIMA/CONICET-UBA), Buenos Aires, Argentina
dInstituto Franco-Argentino para el Estudio del Clima y sus Impactos (UMI IFAECI/CNRS-CONICET-UBA), Buenos Aires, Argentina

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Yanina García SkabaraServicio Meteorológico Nacional and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
dInstituto Franco-Argentino para el Estudio del Clima y sus Impactos (UMI IFAECI/CNRS-CONICET-UBA), Buenos Aires, Argentina

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Stephen W. NesbitteDepartment of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois

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Russ S. SchumacherfDepartment of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Carlos Marcelo GarcíagInstituto de Estudios Avanzados en Ingeniería y Tecnología (IDIT CONICET/UNC), Córdoba, Argentina
hFacultad de Ciencias Exactas, Físicas y Naturales, Universidad Católica de Córdoba, Córdoba, Argentina

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Carlos CataliniiInstituto Nacional del Agua-Subgerencia Centro de la Región Semiárida (INA-CIRSA), Córdoba, Argentina
jFacultad de Ingeniería, Universidad Católica de Córdoba (UNC), Córdoba, Argentina

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Abstract

Sierras de Córdoba (Argentina) is characterized by the occurrence of extreme precipitation events during the austral warm season. Heavy precipitation in the region has a large societal impact, causing flash floods. This motivates the forecast performance evaluation of 24-h accumulated precipitation and vertical profiles of atmospheric variables from different numerical weather prediction (NWP) models with the final aim of helping water management in the region. The NWP models evaluated include the Global Forecast System (GFS), which parameterizes convection, and convection-permitting simulations of the Weather Research and Forecasting (WRF) Model configured by three institutions: University of Illinois at Urbana–Champaign (UIUC), Colorado State University (CSU), and National Meteorological Service of Argentina (SMN). These models were verified with daily accumulated precipitation data from rain gauges and soundings during the RELAMPAGO-CACTI field campaign. Generally all configurations of the higher-resolution WRFs outperformed the lower-resolution GFS based on multiple metrics. Among the convection-permitting WRF Models, results varied with respect to rainfall threshold and forecast lead time, but the WRFUIUC mostly performed the best. However, elevation-dependent biases existed among the models that may impact the use of the data for different applications. There is a dry (moist) bias in lower (upper) pressure levels which is most pronounced in the GFS. For Córdoba an overestimation of the northern flow forecasted by the NWP configurations at lower levels was encountered. These results show the importance of convection-permitting forecasts in this region, which should be complementary to the coarser-resolution global model forecasts to help various users and decision-makers.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the RELAMPAGO-CACTI Special Collection.

Corresponding author: Gimena Casaretto, gcasaretto@smn.gob.ar

Abstract

Sierras de Córdoba (Argentina) is characterized by the occurrence of extreme precipitation events during the austral warm season. Heavy precipitation in the region has a large societal impact, causing flash floods. This motivates the forecast performance evaluation of 24-h accumulated precipitation and vertical profiles of atmospheric variables from different numerical weather prediction (NWP) models with the final aim of helping water management in the region. The NWP models evaluated include the Global Forecast System (GFS), which parameterizes convection, and convection-permitting simulations of the Weather Research and Forecasting (WRF) Model configured by three institutions: University of Illinois at Urbana–Champaign (UIUC), Colorado State University (CSU), and National Meteorological Service of Argentina (SMN). These models were verified with daily accumulated precipitation data from rain gauges and soundings during the RELAMPAGO-CACTI field campaign. Generally all configurations of the higher-resolution WRFs outperformed the lower-resolution GFS based on multiple metrics. Among the convection-permitting WRF Models, results varied with respect to rainfall threshold and forecast lead time, but the WRFUIUC mostly performed the best. However, elevation-dependent biases existed among the models that may impact the use of the data for different applications. There is a dry (moist) bias in lower (upper) pressure levels which is most pronounced in the GFS. For Córdoba an overestimation of the northern flow forecasted by the NWP configurations at lower levels was encountered. These results show the importance of convection-permitting forecasts in this region, which should be complementary to the coarser-resolution global model forecasts to help various users and decision-makers.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the RELAMPAGO-CACTI Special Collection.

Corresponding author: Gimena Casaretto, gcasaretto@smn.gob.ar
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