• Adlerman, E. J., and K. K. Droegemeier, 2002: The sensitivity of numerically simulated cyclic mesocyclogenesis to variations in model physical and computational parameters. Mon. Wea. Rev., 130, 26712691, https://doi.org/10.1175/1520-0493(2002)130<2671:TSONSC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berner, J., S. Ha, J. P. Hacker, A. Fournier, and C. Snyder, 2011: Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations. Mon. Wea. Rev., 139, 19721995, https://doi.org/10.1175/2010MWR3595.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berner, J., K. R. Fossell, S. Ha, J. P. Hacker, and C. Snyder, 2015: Increasing the skill of probabilistic forecasts: Understanding performance improvements from model-error representations. Mon. Wea. Rev., 143, 12951320, https://doi.org/10.1175/MWR-D-14-00091.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berner, J., and et al. , 2017: Stochastic parameterization: Toward a new view of weather and climate models. Bull. Amer. Meteor. Soc., 98, 565588, https://doi.org/10.1175/BAMS-D-15-00268.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., 2005: Spurious convective organization in simulated squall lines owing to moist absolutely unstable layers. Mon. Wea. Rev., 133, 19781997, https://doi.org/10.1175/MWR2952.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and H. Morrison, 2012: Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon. Wea. Rev., 140, 202225, https://doi.org/10.1175/MWR-D-11-00046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., J. C. Wyngaard, and J. M. Fritsch, 2003: Resolution requirements for the simulation of deep moist convection. Mon. Wea. Rev., 131, 23942416, https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dawson, D. T., M. Xue, J. A. Milbrandt, and M. K. Yau, 2010: Comparison of evaporation and cold pool development between single-moment and multimoment bulk microphysics schemes in idealized simulations of tornadic thunderstorms. Mon. Wea. Rev., 138, 11521171, https://doi.org/10.1175/2009MWR2956.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1994: Atmospheric Convection. Oxford University Press, 580 pp.

  • Fan, J., and et al. , 2017: Cloud-resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts. J. Geophys. Res. Atmos., 122, 93519378, http://doi.org/10.1002/2017jd026622.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fiori, E., A. Parodi, and F. Siccardi, 2010: Turbulence closure parameterization and grid spacing effects in simulated supercell storms. J. Atmos. Sci., 67, 38703890, https://doi.org/10.1175/2010JAS3359.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gentry, M. S., and G. M. Lackmann, 2010: Sensitivity of simulated tropical cyclone structure and intensity to horizontal resolution. Mon. Wea. Rev., 138, 688704, https://doi.org/10.1175/2009MWR2976.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, B., and et al. , 2019: Cloud-resolving model intercomparison of an MC3E squall line case. Part II: Stratiform precipitation properties. J. Geophys. Res. Atmos., 124, 10901117, http://doi.org/10.1029/2018jd029596.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanley, K. E., R. S. Plant, T. H. M. Stein, R. J. Hogan, J. C. Nicol, H. W. Lean, C. Halliwell, and P. A. Clark, 2015: Mixing-length controls on high-resolution simulations of convective storms. Quart. J. Roy. Meteor. Soc., 141, 272284, https://doi.org/10.1002/qj.2356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Helmus, J. J., and S. M. Collis, 2016: The Python ARM Radar Toolkit (Py-ART), a library for working with weather radar data in the Python programming language. J. Open Res. Software, 4, e25, https://doi.org/10.5334/jors.119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hernandez-Deckers, D., and S. C. Sherwood, 2018: On the role of entrainment in the fate of cumulus thermals. J. Atmos. Sci., 75, 39113924, https://doi.org/10.1175/JAS-D-18-0077.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hill, K. A., and G. M. Lackmann, 2009: Analysis of idealized tropical cyclone simulations using the Weather Research and Forecasting Model: Sensitivity to turbulence parameterization and grid spacing. Mon. Wea. Rev., 137, 745765, https://doi.org/10.1175/2008MWR2220.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirt, M., S. Rasp, U. Blahak, and G. C. Craig, 2019: Stochastic parameterization of processes leading to convective initiation in kilometer-scale models. Mon. Wea. Rev., 147, 39173934, https://doi.org/10.1175/MWR-D-19-0060.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jankov, I., and et al. , 2017: A performance comparison between multiphysics and stochastic approaches within a North American RAP ensemble. Mon. Wea. Rev., 145, 11611179, https://doi.org/10.1175/MWR-D-16-0160.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jankov, I., J. Beck, J. Wolff, M. Harrold, J. B. Olson, T. Smirnova, C. Alexander, and J. Berner, 2019: Stochastically perturbed parameterizations in an HRRR-based ensemble. Mon. Wea. Rev., 147, 153173, https://doi.org/10.1175/MWR-D-18-0092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jensen, M. P., and et al. , 2016: The Midlatitude Continental Convective Clouds Experiment (MC3E). Bull. Amer. Meteor. Soc., 97, 16671686, https://doi.org/10.1175/BAMS-D-14-00228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M., and D. Randall, 2006: High-resolution simulation of shallow-to-deep convection transition over land. J. Atmos. Sci., 63, 34213436, https://doi.org/10.1175/JAS3810.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knievel, J. C., G. H. Bryan, and J. P. Hacker, 2007: Explicit numerical diffusion in the WRF Model. Mon. Wea. Rev., 135, 38083824, https://doi.org/10.1175/2007MWR2100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kober, K., and G. C. Craig, 2016: Physically based stochastic perturbations (PSP) in the boundary layer to represent uncertainty in convective initiation. J. Atmos. Sci., 73, 28932911, https://doi.org/10.1175/JAS-D-15-0144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kolmogorov, A. N., 1941: Dissipation of energy in locally isotropic turbulence. Dokl. Akad. Nauk SSSR, 434, 1517.

  • Langhans, W., J. Schmidli, and C. Schär, 2012: Mesoscale impacts of explicit numerical diffusion in a convection-permitting model. Mon. Wea. Rev., 140, 226244, https://doi.org/10.1175/2011MWR3650.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lean, H. W., P. A. Clark, M. Dixon, N. M. Roberts, A. Fitch, R. Forbes, and C. Halliwell, 2008: Characteristics of high-resolution versions of the Met Office Unified Model for forecasting convection over the United Kingdom. Mon. Wea. Rev., 136, 34083424, https://doi.org/10.1175/2008MWR2332.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebo, Z. J., and H. Morrison, 2015: Effects of horizontal and vertical grid spacing on mixing in simulated squall lines and implications for convective strength and structure. Mon. Wea. Rev., 143, 43554375, https://doi.org/10.1175/MWR-D-15-0154.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Machado, L. A., and J. Chaboureau, 2015: Effect of turbulence parameterization on assessment of cloud organization. Mon. Wea. Rev., 143, 32463262, https://doi.org/10.1175/MWR-D-14-00393.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., 2017: An analytic description of the structure and evolution of growing deep cumulus updrafts. J. Atmos. Sci., 74, 809834, https://doi.org/10.1175/JAS-D-16-0234.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., and J. A. Milbrandt, 2015: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part I: Scheme description and idealized tests. J. Atmos. Sci., 72, 287311, https://doi.org/10.1175/JAS-D-14-0065.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., A. Morales, and C. Villanueva-Birriel, 2015a: Concurrent sensitivities of an idealized deep convective storm to parameterization of microphysics, horizontal grid resolution, and environmental static stability. Mon. Wea. Rev., 143, 20822104, https://doi.org/10.1175/MWR-D-14-00271.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., J. A. Milbrandt, G. H. Bryan, K. Ikeda, S. A. Tessendorf, and G. Thompson, 2015b: Parameterization of cloud microphysics based on the prediction of bulk ice particle properties. Part II: Case study comparisons with observations and other schemes. J. Atmos. Sci., 72, 312339, https://doi.org/10.1175/JAS-D-14-0066.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., J. M. Peters, A. C. Varble, W. M. Hannah, and S. E. Giangrande, 2020: Thermal chains and entrainment in cumulus updrafts. Part I: Theoretical description. J. Atmos. Sci., 77, 3637–3660,https://doi.org/10.1175/JAS-D-19-0243.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morton, B. R., G. Taylor, and J. S. Turner, 1956: Turbulent gravitational convection from maintained and instantaneous sources. Proc. Roy. Soc. London, 234A, 123, http://doi.org/10.1098/rspa.1956.0011.

    • Search Google Scholar
    • Export Citation
  • Nicol, J. C., R. J. Hogan, T. H. M. Stein, K. E. Hanley, P. A. Clark, C. E. Halliwell, H. W. Lean, and R. S. Plant, 2015: Convective updraught evaluation in high-resolution NWP simulations using single-Doppler radar measurements. Quart. J. Roy. Meteor. Soc., 141, 31773189, https://doi.org/10.1002/qj.2602.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nie, J., and Z. Kuang, 2012: Responses of shallow cumulus convection to large-scale temperature and moisture perturbations: A comparison of large-eddy simulations and a convective parameterization based on stochastically entraining parcels. J. Atmos. Sci., 69, 19361956, https://doi.org/10.1175/JAS-D-11-0279.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., 2001: A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models. Quart. J. Roy. Meteor. Soc., 127, 279304, http://doi.org/10.1002/qj.49712757202.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., R. Buizza, F. Doblas-Reyes, T. Jung, M. Leutbecher, G. Shutts, M. Steinheimer, and A. Weisheimer, 2009: Stochastic parametrization and model uncertainty. ECMWF Tech. Memo. 598, 42 pp., https://www2.physics.ox.ac.uk/sites/default/files/2011-08-15/techmemo598_stochphys_2009_pdf_50419.pdf.

  • Petch, J. C., A. R. Brown, and M. E. Gray, 2002: The impact of horizontal resolution on the simulations of convective development over land. Quart. J. Roy. Meteor. Soc., 128, 20312044, https://doi.org/10.1256/003590002320603511.

    • Crossref
    • 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
  • Qiao, X., S. Wang, and J. Min, 2017: A Stochastic Perturbed Parameterization Tendency Scheme for Diffusion (SPPTD) and its application to an idealized supercell simulation. Mon. Wea. Rev., 145, 21192139, https://doi.org/10.1175/MWR-D-16-0307.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qiao, X., S. Wang, and J. Min, 2018: The impact of a stochastically perturbing microphysics scheme on an idealized supercell storm. Mon. Wea. Rev., 146, 95118, https://doi.org/10.1175/MWR-D-17-0064.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raymond, D. J., and A. M. Blyth, 1986: A stochastic mixing model for nonprecipitating cumulus clouds. J. Atmos. Sci., 43, 27082718, https://doi.org/10.1175/1520-0469(1986)043<2708:ASMMFN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romine, G. S., C. S. Schwartz, J. Berner, K. R. Fossell, C. Snyder, J. L. Anderson, and M. L. Weisman, 2014: Representing forecast error in a convection-permitting ensemble system. Mon. Wea. Rev., 142, 45194541, https://doi.org/10.1175/MWR-D-14-00100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romps, D. M., 2016: The stochastic parcel model: A deterministic parameterization of stochastically entraining convection. J. Adv. Model. Earth Syst., 8, 319344, https://doi.org/10.1002/2015MS000537.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romps, D. M., and Z. Kuang, 2010a: Do undiluted convective plumes exist in the upper tropical troposphere? J. Atmos. Sci., 67, 468484, https://doi.org/10.1175/2009JAS3184.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romps, D. M., and Z. Kuang, 2010b: Nature versus nurture in shallow convection. J. Atmos. Sci., 67, 16551666, https://doi.org/10.1175/2009JAS3307.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rotunno, R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J. Atmos. Sci., 45, 463485, https://doi.org/10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rotunno, R., Y. Chen, W. Wang, C. Davis, J. Dudhia, and G. J. Holland, 2009: Large-eddy simulation of an idealized tropical cyclone. Bull. Amer. Meteor. Soc., 90, 17831788, https://doi.org/10.1175/2009BAMS2884.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rousseau-Rizzi, R., D. J. Kirshbaum, and M. K. Yau, 2017: Initiation of deep convection over an idealized mesoscale convergence line. J. Atmos. Sci., 74, 835853, https://doi.org/10.1175/JAS-D-16-0221.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Savre, J., and M. Herzog, 2019: A general description of entrainment in buoyant cloudy plumes including the effects of mixing-induced evaporation. J. Atmos. Sci., 76, 479496, https://doi.org/10.1175/JAS-D-17-0326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scorer, R. S., 1957: Experiments on convection of isolated masses of buoyant fluid. J. Fluid Mech., 2, 583, https://doi.org/10.1017/S0022112057000397.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, J., and V. Wiggert, 1969: Models of precipitating cumulus towers. Mon. Wea. Rev., 97, 471489, https://doi.org/10.1175/1520-0493(1969)097<0471:MOPCT>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 30193032, https://doi.org/10.1175/MWR2830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, https://doi.org/10.1016/j.jcp.2007.01.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and et al. , 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.

    • Crossref
    • Export Citation
  • Smagorinsky, J., 1963: General circulation experiments with the primitive equations. Mon. Wea. Rev., 91, 99164, https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stanford, M. W., H. Morrison, A. Varble, J. Berner, W. Wu, G. McFarquhar, and J. Milbrandt, 2019: Sensitivity of simulated deep convection to a stochastic ice microphysics framework. J. Adv. Model. Earth Syst., 11, 33623389, https://doi.org/10.1029/2019MS001730.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stein, T. H., R. J. Hogan, P. A. Clark, C. E. Halliwell, K. E. Hanley, H. W. Lean, J. C. Nicol, and R. S. Plant, 2015: The DYMECS Project: A statistical approach for the evaluation of convective storms in high-resolution NWP models. Bull. Amer. Meteor. Soc., 96, 939951, https://doi.org/10.1175/BAMS-D-13-00279.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strauss, C., D. Ricard, C. Lac, and A. Verrelle, 2019: Evaluation of turbulence parametrizations in convective clouds and their environment based on a large-eddy simulation. Quart. J. Roy. Meteor. Soc., 145, 31953217, https://doi.org/10.1002/qj.3614.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sušelj, K., J. Teixeira, and D. Chung, 2013: A unified model for moist convective boundary layers based on a stochastic eddy-diffusivity/mass-flux parameterization. J. Atmos. Sci., 70, 19291953, https://doi.org/10.1175/JAS-D-12-0106.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takemi, T., and R. Rotunno, 2003: The effects of subgrid model mixing and numerical filtering in simulations of mesoscale cloud systems. Mon. Wea. Rev., 131, 20852101, https://doi.org/10.1175/1520-0493(2003)131<2085:TEOSMM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Varble, A., and et al. , 2014a: Evaluation of cloud-resolving and limited area model intercomparison simulations using TWP-ICE observations: 1. Deep convective updraft properties. J. Geophys. Res. Atmos., 119, 13 89113 918, https://doi.org/10.1002/2013JD021371.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Varble, A., and et al. , 2014b: Evaluation of cloud-resolving and limited area model intercomparison simulations using TWP-ICE observations: 2. Precipitation microphysics. J. Geophys. Res. Atmos., 119, 13 91913 945, https://doi.org/10.1002/2013JD021372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Varble, A., H. Morrison, and E. Zipser, 2020: Effects of under-resolved convective dynamics on the evolution of a squall line. Mon. Wea. Rev., 148, 289311, https://doi.org/10.1175/MWR-D-19-0187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verrelle, A., D. Ricard, and C. Lac, 2015: Sensitivity of high-resolution idealized simulations of thunderstorms to horizontal resolution and turbulence parametrization. Quart. J. Roy. Meteor. Soc., 141, 433448, https://doi.org/10.1002/qj.2363.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verrelle, A., D. Ricard, and C. Lac, 2017: Evaluation and improvement of turbulence parameterization inside deep convective clouds at kilometer-scale resolution. Mon. Wea. Rev., 145, 39473967, https://doi.org/10.1175/MWR-D-16-0404.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S., X. Qiao, J. Min, and X. Zhuang, 2019: The impact of stochastically perturbed parameterizations on tornadic supercell cases in east China. Mon. Wea. Rev., 147, 199220, https://doi.org/10.1175/MWR-D-18-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., 1992: The role of convectively generated rear-inflow jets in the evolution of long-lived mesoconvective systems. J. Atmos. Sci., 49, 18261847, https://doi.org/10.1175/1520-0469(1992)049<1826:TROCGR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and R. Rotunno, 2004: “A theory for strong long-lived squall lines” revisited. J. Atmos. Sci., 61, 361382, https://doi.org/10.1175/1520-0469(2004)061<0361:ATFSLS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., J. B. Klemp, and R. Rotunno, 1988: Structure and evolution of numerically simulated squall lines. J. Atmos. Sci., 45, 19902013, https://doi.org/10.1175/1520-0469(1988)045<1990:SAEONS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., W. C. Skamarock, and J. B. Klemp, 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527548, https://doi.org/10.1175/1520-0493(1997)125<0527:TRDOEM>2.0.CO;2.

    • 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
  • Wicker, L. J., and W. C. Skamarock, 2002: Time-splitting methods for elastic models using forward time schemes. Mon. Wea. Rev., 130, 20882097, https://doi.org/10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyngaard, J. C., 2010: Turbulence in the Atmosphere. Cambridge University Press, 393 pp.

  • Xue, L., and et al. , 2017: Idealized simulations of a squall line from the MC3E field campaign applying three bin microphysics schemes: Dynamic and thermodynamic structure. Mon. Wea. Rev., 145, 47894812, https://doi.org/10.1175/MWR-D-16-0385.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 170 170 12
Full Text Views 74 74 9
PDF Downloads 83 83 10

Impacts of Stochastic Mixing in Idealized Convection-Permitting Simulations of Squall Lines

View More View Less
  • 1 University of Utah, Salt Lake City, Utah
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
  • | 3 Pacific Northwest National Laboratory, Richland, Washington
© Get Permissions
Restricted access

Abstract

This study investigates impacts of altering subgrid-scale mixing in “convection-permitting” kilometer-scale horizontal-grid-spacing (Δh) simulations by applying either constant or stochastic multiplicative factors to the horizontal mixing coefficients within the Weather Research and Forecasting Model. In quasi-idealized 1-km Δh simulations of two observationally based squall-line cases, constant enhanced mixing produces larger updraft cores that are more dilute at upper levels, weakens the cold pool, rear-inflow jet, and front-to-rear flow of the squall line, and degrades the model’s effective resolution. Reducing mixing by a constant multiplicative factor has the opposite effect on all metrics. Completely turning off parameterized horizontal mixing produces bulk updraft statistics and squall-line mesoscale structure closest to an LES “benchmark” among all 1-km simulations, although the updraft cores are too undilute. The stochastic mixing scheme, which applies a multiplicative factor to the mixing coefficients that varies stochastically in time and space, is employed at 0.5-, 1-, and 2-km Δh. It generally reduces midlevel vertical velocities and enhances upper-level vertical velocities compared to simulations using the standard mixing scheme, with more substantial impacts at 1- and 2-km Δh compared to 0.5-km Δh. The stochastic scheme also increases updraft dilution to better agree with the LES for one case, but has less impact on the other case. Stochastic mixing acts to weaken the cold pool but without a significant impact on squall-line propagation. It also does not affect the model’s overall effective resolution unlike applying constant multiplicative factors to the mixing coefficients.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hugh Morrison, morrison@ucar.edu

Abstract

This study investigates impacts of altering subgrid-scale mixing in “convection-permitting” kilometer-scale horizontal-grid-spacing (Δh) simulations by applying either constant or stochastic multiplicative factors to the horizontal mixing coefficients within the Weather Research and Forecasting Model. In quasi-idealized 1-km Δh simulations of two observationally based squall-line cases, constant enhanced mixing produces larger updraft cores that are more dilute at upper levels, weakens the cold pool, rear-inflow jet, and front-to-rear flow of the squall line, and degrades the model’s effective resolution. Reducing mixing by a constant multiplicative factor has the opposite effect on all metrics. Completely turning off parameterized horizontal mixing produces bulk updraft statistics and squall-line mesoscale structure closest to an LES “benchmark” among all 1-km simulations, although the updraft cores are too undilute. The stochastic mixing scheme, which applies a multiplicative factor to the mixing coefficients that varies stochastically in time and space, is employed at 0.5-, 1-, and 2-km Δh. It generally reduces midlevel vertical velocities and enhances upper-level vertical velocities compared to simulations using the standard mixing scheme, with more substantial impacts at 1- and 2-km Δh compared to 0.5-km Δh. The stochastic scheme also increases updraft dilution to better agree with the LES for one case, but has less impact on the other case. Stochastic mixing acts to weaken the cold pool but without a significant impact on squall-line propagation. It also does not affect the model’s overall effective resolution unlike applying constant multiplicative factors to the mixing coefficients.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hugh Morrison, morrison@ucar.edu
Save