Dynamic Adjustment in a Numerically Simulated Mesoscale Convective System: Impact of the Velocity Field

Ernanide Lima Nascimento School of Meteorology, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

Search for other papers by Ernanide Lima Nascimento in
Current site
Google Scholar
PubMed
Close
and
Kelvin K. Droegemeier School of Meteorology, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

Search for other papers by Kelvin K. Droegemeier in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

An identical twin methodology is applied to a three-dimensional cloud model to study the dynamics of adjustment in deep convective storms. The principal goal is to diagnose how mass and velocity fields mutually adjust in order to better understand the relative information content (value) of observations, the physical interdependency among variables, and to help in the design of dynamically consistent analyses to ensure smooth startup of numerical prediction models.

Using a control simulation (“truth” or “nature” run) of an idealized long-lived bow echo convective system, a series of adjustment experiments is created by resetting, in various combinations, the horizontal and vertical velocity components of the control run to their undisturbed base state values during the mature stage of storm system evolution. The integrations then are continued for comparison against the control. This strategy represents a methodology for studying transient response to an impulsive perturbation in a manner conceptually similar to that used in geostrophic and hydrostatic adjustment.

The results indicate that resetting both horizontal velocity components alters the character of the convection and slows considerably the overall storm system evolution. In sharp contrast, when only the vertical velocity component is reset, the model quickly restores both updrafts and downdrafts to nearly their correct (control run) values, producing subsequent storm evolution virtually identical to that of the control run. Other combinations yield results in between these two extremes, with the cross-line velocity component proving to be most important in restoration toward the control run. This behavior is explained by acoustic adjustment of the pressure and velocity fields in direct response to changes in velocity divergence forced by the withdrawal of wind information.

* Current affiliation: Laboratório de Estudos em Monitoramento e Modelagem Ambiental, Instituto Tecnológico SIMEPAR, Curitiba, Brazil

Corresponding author address: Dr. Ernani L. Nascimento, Instituto Tecnológico SIMEPAR, Centro Politécnico da UFPR, Caixa Postal 19100, Curitiba/PR, CEP. 81531-990, Brazil. Email: elnascimento@ufpr.br

Abstract

An identical twin methodology is applied to a three-dimensional cloud model to study the dynamics of adjustment in deep convective storms. The principal goal is to diagnose how mass and velocity fields mutually adjust in order to better understand the relative information content (value) of observations, the physical interdependency among variables, and to help in the design of dynamically consistent analyses to ensure smooth startup of numerical prediction models.

Using a control simulation (“truth” or “nature” run) of an idealized long-lived bow echo convective system, a series of adjustment experiments is created by resetting, in various combinations, the horizontal and vertical velocity components of the control run to their undisturbed base state values during the mature stage of storm system evolution. The integrations then are continued for comparison against the control. This strategy represents a methodology for studying transient response to an impulsive perturbation in a manner conceptually similar to that used in geostrophic and hydrostatic adjustment.

The results indicate that resetting both horizontal velocity components alters the character of the convection and slows considerably the overall storm system evolution. In sharp contrast, when only the vertical velocity component is reset, the model quickly restores both updrafts and downdrafts to nearly their correct (control run) values, producing subsequent storm evolution virtually identical to that of the control run. Other combinations yield results in between these two extremes, with the cross-line velocity component proving to be most important in restoration toward the control run. This behavior is explained by acoustic adjustment of the pressure and velocity fields in direct response to changes in velocity divergence forced by the withdrawal of wind information.

* Current affiliation: Laboratório de Estudos em Monitoramento e Modelagem Ambiental, Instituto Tecnológico SIMEPAR, Curitiba, Brazil

Corresponding author address: Dr. Ernani L. Nascimento, Instituto Tecnológico SIMEPAR, Centro Politécnico da UFPR, Caixa Postal 19100, Curitiba/PR, CEP. 81531-990, Brazil. Email: elnascimento@ufpr.br

Save
  • Bannon, P. R., 1995: Hydrostatic adjustment: Lamb’s problem. J. Atmos. Sci., 52 , 23022312.

  • Blumen, W., 1972: Geostrophic adjustment. Rev. Geophys. Space Phys., 10 , 485528.

  • Bretherton, C. S., and P. K. Smolarkiewicz, 1989: Gravity waves, compensating subsidence, and detrainment around cumulus clouds. J. Atmos. Sci., 46 , 740759.

    • Search Google Scholar
    • Export Citation
  • Brewster, K., 2003a: Phase-correcting data assimilation and application to storm-scale numerical weather prediction. Part I: Method description and simulation testing. Mon. Wea. Rev., 131 , 480492.

    • Search Google Scholar
    • Export Citation
  • Brewster, K., 2003b: Phase-correcting data assimilation and application to storm-scale numerical weather prediction. Part II: Application to a severe storm outbreak. Mon. Wea. Rev., 131 , 493507.

    • Search Google Scholar
    • Export Citation
  • Chagnon, J. M., and P. R. Bannon, 2001: Hydrostatic and geostrophic adjustment in a compressible atmosphere: Initial response and final equilibrium to an instantaneous localized heating. J. Atmos. Sci., 58 , 37763792.

    • Search Google Scholar
    • Export Citation
  • Charney, J., M. Halem, and R. Jastrow, 1969: Use of incomplete historical data to infer the present state of the atmosphere. J. Atmos. Sci., 26 , 11601163.

    • Search Google Scholar
    • Export Citation
  • Crook, N. A., 1996: Sensitivity of moist convection forced by boundary layer processes to low-level thermodynamic fields. Mon. Wea. Rev., 124 , 17671785.

    • Search Google Scholar
    • Export Citation
  • Crook, N. A., and J. D. Tuttle, 1994: Numerical simulations initialized with radar-derived winds. Part II: Forecasts of three gust-front cases. Mon. Wea. Rev., 122 , 12041217.

    • Search Google Scholar
    • Export Citation
  • Crook, N. A., and J. Sun, 2002: Assimilating radar, surface, and profiler data for the Sydney 2000 forecast demonstration project. J. Atmos. Oceanic Technol., 19 , 888898.

    • Search Google Scholar
    • Export Citation
  • Crook, N. A., and J. Sun, 2004: Analysis and forecasting of the low-level wind during the Sydney 2000 forecast demonstration project. Wea. Forecasting, 19 , 151167.

    • Search Google Scholar
    • Export Citation
  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.

  • Davies-Jones, R., R. J. Trapp, and H. B. Bluestein, 2001: Tornadoes and tornadic storms. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 167–221.

  • Davis, C. A., and M. L. Weisman, 1994: Balanced dynamics of mesoscale vortices produced in simulated convective systems. J. Atmos. Sci., 51 , 20052030.

    • Search Google Scholar
    • Export Citation
  • Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132 , 19822005.

    • Search Google Scholar
    • Export Citation
  • Droegemeier, K. K., and R. P. Davies-Jones, 1987: Simulation of thunderstorm microbursts with a supercompressible numerical model. Proc. Fifth Int. Conf. on Numerical Methods in Laminar and Turbulent Flow, Montreal, QC, Canada, Concordia University and Cosponsors, 1386–1397.

  • Droegemeier, K. K., and J. Levit, 1993: Sensitivity of storm-scale predictions to initialization with simulated Doppler radar data. Preprints, 17th Conf. on Severe Local Storms, St. Louis, MO, Amer. Meteor. Soc., 431–435.

  • Ducrocq, V., J-P. Lafore, J-L. Redelsperger, and F. Orain, 2000: Initialization of a fine-scale model for convective-system prediction: A case study. Quart. J. Roy. Meteor. Soc., 126 , 30413065.

    • Search Google Scholar
    • Export Citation
  • Duffy, D. G., 2003: Hydrostatic adjustment in nonisothermal atmospheres. J. Atmos. Sci., 60 , 339353.

  • Durran, D. R., 1999: Numerical Methods for Wave Equations in Geophysical Fluid Dynamics. Springer-Verlag, 465 pp.

  • Elmore, K. L., D. J. Stensrud, and K. C. Crawford, 2002: Ensemble cloud model applications to forecasting thunderstorms. J. Appl. Meteor., 41 , 363381.

    • Search Google Scholar
    • Export Citation
  • Fiedler, B. H., 2002: A wind transformation for acoustic adjustment in compressible models. Mon. Wea. Rev., 130 , 741746.

  • Fovell, R. G., 2002: Upstream influence of numerically simulated squall-line storms. Quart. J. Roy. Meteor. Soc., 128 , 893912.

  • Fritsch, J. M., and G. S. Forbes, 2001: Mesoscale convective systems. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 323–357.

  • Gao, J., M. Xue, A. Shapiro, Q. Xu, and K. K. Droegemeier, 2001: Three-dimensional simple adjoint velocity retrievals from single-Doppler radar. J. Atmos. Oceanic Technol., 18 , 2638.

    • Search Google Scholar
    • Export Citation
  • Gilmore, M. S., and L. J. Wicker, 1998: The influence of midtropospheric dryness on supercell morphology and evolution. Mon. Wea. Rev., 126 , 943958.

    • Search Google Scholar
    • Export Citation
  • Houze Jr, R. A., 1993: Cloud Dynamics. Academic Press, 573 pp.

  • Johns, R. H., 1993: Meteorological conditions associated with bow echo development in convective storms. Wea. Forecasting, 8 , 294299.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, 512 pp.

  • Klemp, J. B., and R. B. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35 , 10701096.

    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., and D. R. Durran, 1983: An upper boundary condition permitting internal gravity wave radiation in numerical mesoscale models. Mon. Wea. Rev., 111 , 430444.

    • Search Google Scholar
    • Export Citation
  • Lamb, H., 1908: On the theory of waves propagated vertically in the atmosphere. Proc. London Math. Soc., 7 , 122141.

  • Lamb, H., 1932: Hydrodynamics. Dover Publications, 738 pp.

  • Laroche, S., and I. Zawadzki, 1994: A variational analysis method for the retrieval of three-dimensional wind field from single-Doppler radar data. J. Atmos. Sci., 51 , 26642682.

    • Search Google Scholar
    • Export Citation
  • Lazarus, S., A. Shapiro, and K. K. Droegemeier, 2001: Application of the Zhang–Gal-Chen single-Doppler velocity retrieval to a deep convective storm. J. Atmos. Sci., 58 , 9981016.

    • Search Google Scholar
    • Export Citation
  • Li, Q., R. L. Bras, and S. Islam, 1995: Growth and decay of error in a numerical cloud model due to small initial perturbations and parameter changes. J. Appl. Meteor., 34 , 16221632.

    • Search Google Scholar
    • Export Citation
  • Liepman, H. W., and A. Roshko, 1957: Elements of Gasdynamics. John Wiley and Sons, 439 pp.

  • Lighthill, J., 1978: Waves in Fluids. Cambridge University Press, 504 pp.

  • Lin, Y. H., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22 , 10651092.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., 1993: Gregarious tropical convection. J. Atmos. Sci., 50 , 20262037.

  • McCaul, E. W., and C. Cohen, 2002: The impact on simulated storm structure and intensity of variations in the mixed layer and moist layer depths. Mon. Wea. Rev., 130 , 17221748.

    • Search Google Scholar
    • Export Citation
  • McPherson, R. A., and K. K. Droegemeier, 1991: Numerical predictability experiments of the 20 May 1977 Del City, OK supercell storm. Preprints, Ninth Conf. on Numerical Weather Prediction, Denver, CO, Amer. Meteor. Soc., 734–738.

  • Nicholls, M. E., and R. A. Pielke, 1994a: Thermal compression waves. I: Total-energy transfer. Quart. J. Roy. Meteor. Soc., 120 , 305332.

    • Search Google Scholar
    • Export Citation
  • Nicholls, M. E., and R. A. Pielke, 1994b: Thermal compression waves. II: Mass adjustment and vertical transfer of total energy. Quart. J. Roy. Meteor. Soc., 120 , 333359.

    • Search Google Scholar
    • Export Citation
  • Nicholls, M. E., and R. A. Pielke, 2000: Thermally induced compression waves and gravity waves generated by convective storms. J. Atmos. Sci., 57 , 32513271.

    • Search Google Scholar
    • Export Citation
  • Nicholls, M. E., R. A. Pielke, and W. R. Cotton, 1991: Thermally forced gravity waves in an atmosphere at rest. J. Atmos. Sci., 48 , 18691884.

    • Search Google Scholar
    • Export Citation
  • Pandya, R. E., and D. R. Durran, 1996: The influence of convectively generated thermal forcing on the mesoscale circulation around squall lines. J. Atmos. Sci., 53 , 29242951.

    • Search Google Scholar
    • Export Citation
  • Park, S. K., and K. K. Droegemeier, 2000: Sensitivity analysis of a 3D convective storm: Implications for variational data assimilation and forecast error. Mon. Wea. Rev., 128 , 184197.

    • Search Google Scholar
    • Export Citation
  • Przybylinski, R. W., 1995: The bow echo: Observations, numerical simulations, and severe weather detection methods. Wea. Forecasting, 10 , 203218.

    • Search Google Scholar
    • Export Citation
  • Qiu, C-J., and Q. Xu, 1996: Least squares retrieval of microburst winds from single-Doppler radar data. Mon. Wea. Rev., 124 , 11321144.

    • Search Google Scholar
    • Export Citation
  • Richardson, Y., 1999: The influence of horizontal variations in vertical shear and moisture on numerically-simulated convective storms. Ph.D. dissertation, University of Oklahoma, 236 pp.

  • Shapiro, A., S. Ellis, and J. Shaw, 1995: Single Doppler velocity retrievals with Phoenix II data: Clear air and microburst wind retrievals in the planetary boundary layer. J. Atmos. Sci., 52 , 12651285.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 1992: The stability of time-splitting numerical methods for hydrostatic and nonhydrostatic elastic equations. Mon. Wea. Rev., 120 , 21092127.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 1994: Efficiency and accuracy of the Klemp–Wilhelmson time splitting technique. Mon. Wea. Rev., 122 , 26232630.

    • Search Google Scholar
    • Export Citation
  • Smagorinsky, J., K. Miyakoda, and R. Strickler, 1970: The relative importance of variables in initial conditions for dynamical weather prediction. Tellus, 22 , 141154.

    • Search Google Scholar
    • Export Citation
  • Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131 , 16631677.

    • Search Google Scholar
    • Export Citation
  • Sotack, T., and P. R. Bannon, 1999: Lamb’s adjustment for heating of finite duration. J. Atmos. Sci., 56 , 7181.

  • Sun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54 , 16421661.

    • Search Google Scholar
    • Export Citation
  • Sun, J., and N. A. Crook, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55 , 835852.

    • Search Google Scholar
    • Export Citation
  • Sun, J., and N. A. Crook, 2001: Real-time low-level wind and temperature analysis using single WSR-88D data. Wea. Forecasting, 16 , 117132.

    • Search Google Scholar
    • Export Citation
  • Sun, J., D. W. Flicker, and D. K. Lilly, 1991: Recovery of three-dimensional wind and temperature fields from simulated single-Doppler radar data. J. Atmos. Sci., 48 , 876890.

    • Search Google Scholar
    • Export Citation
  • Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSSE experiments. Mon. Wea. Rev., 133 , 17891807.

    • Search Google Scholar
    • Export Citation
  • van Delden, A., 2000: Linear dynamics of hydrostatic adjustment to horizontally homogeneous heating. Tellus, 52A , 380390.

  • Wakimoto, R. M., 2001: Convectively driven high wind events. Severe Convective Storms, Meteor. Monogr., No. 50, Amer. Meteor. Soc., 255–298.

  • Weisman, M. L., 1993: The genesis of severe, long-lived bow echoes. J. Atmos. Sci., 50 , 645670.

  • Weisman, M. L., 2001: Bow echoes: A tribute to T. T. Fujita. Bull. Amer. Meteor. Soc., 82 , 97116.

  • Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110 , 504520.

    • Search Google Scholar
    • Export Citation
  • Weygandt, S. S., P. Nutter, E. Kalnay, S. K. Park, and K. K. Droegemeier, 1999: The relative importance of different data fields in a numerically-simulated convective storm. Preprints, Eighth Conf. on Mesoscale Processes, Boulder, CO, Amer. Meteor. Soc., 310–315.

  • Weygandt, S. S., A. Shapiro, and K. K. Droegemeier, 2002a: Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part I: Single-Doppler velocity retrieval. Mon. Wea. Rev., 130 , 433453.

    • Search Google Scholar
    • Export Citation
  • Weygandt, S. S., A. Shapiro, and K. K. Droegemeier, 2002b: Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm. Part II: Thermodynamic retrieval and numerical prediction. Mon. Wea. Rev., 130 , 454476.

    • Search Google Scholar
    • Export Citation
  • Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75 , 161193.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2001: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part II: Model physics and applications. Meteor. Atmos. Phys., 76 , 143165.

    • Search Google Scholar
    • Export Citation
  • Xue, M., D. Wang, J. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 78 , 139170.

    • Search Google Scholar
    • Export Citation
  • Xue, M., M. Tong, and K. K. Droegemeier, 2006: An OSSE framework based on the ensemble square-root Kalman filter for evaluating impact of data from radar networks on thunderstorm analysis and forecast. J. Atmos. Oceanic Technol., 23 , 4666.

    • Search Google Scholar
    • Export Citation
  • Yang, M-J., and R. A. Houze Jr., 1995a: Multicell squall-line structure as a manifestation of vertically trapped gravity waves. Mon. Wea. Rev., 123 , 641661.

    • Search Google Scholar
    • Export Citation
  • Yang, M-J., and R. A. Houze Jr., 1995b: Sensitivity of squall line rear inflow to ice microphysics and environmental humidity. Mon. Wea. Rev., 123 , 31753193.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132 , 12381253.

    • Search Google Scholar
    • Export Citation
  • Ziegler, C. L., 1985: Retrieval of thermal and microphysical variables in observed convective storms. Part I: Model development and preliminary testing. J. Atmos. Sci., 42 , 14871509.

    • Search Google Scholar
    • Export Citation
  • Ziegler, C. L., 1988: Retrieval of thermal and microphysical variables in observed convective storms. Part II: Sensitivity of cloud processes to variation of the microphysical parameterization. J. Atmos. Sci., 45 , 10721090.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 256 135 75
PDF Downloads 134 57 4