• Andren, A., A. R. Brown, J. Graf, P. J. Mason, C.-H. Moeng, F. T. M. Nieuwstadt, and U. Schumann, 1994: Large eddy simulation of a neutrally stratified boundary layer: A comparison of four codes. Quart. J. Roy. Meteor. Soc., 120, 14571484, doi:10.1002/qj.49712052003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bou-Zeid, E., C. Meneveau, and M. B. Parlange, 2005: A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows. Phys. Fluids, 17, 025105, doi:10.1063/1.1839152.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brasseur, J. G., and T. Wei, 2010: Designing large-eddy simulation of the turbulent boundary layer to capture law-of-the-wall scaling. Phys. Fluids, 22, 021303, doi:10.1063/1.3319073.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, A. R., J. M. Hobson, and N. Wood, 2001: Large-eddy simulation of neutral turbulent flow over rough sinusoidal ridges. Bound.-Layer Meteor., 98, 411441, doi:10.1023/A:1018703209408.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chow, F. K., R. L. Street, M. Xue, and J. H. Ferziger, 2005: Explicit filtering and reconstruction turbulence modeling for large-eddy simulation of neutral boundary layer flow. J. Atmos. Sci., 62, 20582077, doi:10.1175/JAS3456.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daniels, M. H., K. A. Lundquist, J. D. Mirocha, D. J. Wiersema, and F. K. Chow, 2016: A new vertical grid nesting capability in the Weather Research and Forecasting (WRF) Model. Mon. Wea. Rev., 144, 37253747, doi:10.1175/MWR-D-16-0049.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fang, J., and F. Porté-Agel, 2015: Large-eddy simulation of very-large-scale motions in the neutrally stratified atmospheric boundary layer. Bound.-Layer Meteor., 155, 397416, doi:10.1007/s10546-015-0006-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garratt, J. R., 1994: The Atmospheric Boundary Layer. 3rd ed. Cambridge University Press, 336 pp.

  • Hutchins, N., and I. Marusic, 2007: Evidence of very long meandering features in the logarithmic region of turbulent boundary layers. J. Fluid Mech., 579, 128, doi:10.1017/S0022112006003946.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirkil, G., J. D. Mirocha, F. K. Chow, and E. Bou-Zeid, 2012: Implementation and evaluation of dynamic subfilter-scale stress models for large-eddy simulation using WRF. Mon. Wea. Rev., 140, 266284, doi:10.1175/MWR-D-11-00037.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kosović, B., 1997: Subgrid-scale modeling for the large-eddy simulation of high-Reynolds-number boundary layers. J. Fluid Mech., 336, 151182, doi:10.1017/S0022112096004697.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kosović, B., and J. A. Curry, 2000: A large eddy simulation of a quasi-steady, stably stratified atmospheric boundary layer. J. Atmos. Sci., 57, 10521068, doi:10.1175/1520-0469(2000)057<1052:ALESSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lilly, D. K., 1967: The representation of small-scale turbulence in numerical experiment. Proc. IBM Scientific Computing Symp. on Environmental Sciences, White Plains, NY, IBM, 195210.

  • Lundquist, K., F. Chow, and J. Lundquist, 2008: An immersed boundary method for flow over complex terrain. 13th Conf. on Mountain Meteorology, Whistler, BC, Canada, Amer. Meteor. Soc., 9A.5. [Available online at https://ams.confex.com/ams/13MontMet17AP/webprogram/Paper141221.html.]

  • Lundquist, K., F. Chow, and J. Lundquist, 2010: Numerical errors in flow over steep topography: Analysis and alternatives. 14th Conf. on Mountain Meteorology, Lake Tahoe, CA, Amer. Meteor. Soc., 10.1. [Available online at https://ams.confex.com/ams/14MountMet/techprogram/paper_173801.htm.]

  • Mahrer, Y., 1984: An improved numerical approximation of the horizontal gradients in a terrain-following coordinate system. Mon. Wea. Rev., 112, 918922, doi:10.1175/1520-0493(1984)112<0918:AINAOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mason, P. J., and D. J. Thomson, 1992: Stochastic backscatter in large-eddy simulations of boundary layers. J. Fluid Mech., 242, 5178, doi:10.1017/S0022112092002271.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mirocha, J. D., J. K. Lundquist, and B. Kosović, 2010: Implementation of a nonlinear subfilter turbulence stress model for large-eddy simulation in the Advanced Research WRF Model. Mon. Wea. Rev., 138, 42124228, doi:10.1175/2010MWR3286.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mirocha, J. D., G. Kirkil, E. Bou-Zeid, F. K. Chow, and B. Kosović, 2013: Transition and equilibration of neutral atmospheric boundary layer flow in one-way nested large-eddy simulations using the Weather Research and Forecasting Model. Mon. Wea. Rev., 141, 918940, doi:10.1175/MWR-D-11-00263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moeng, C.-H., 1984: A large-eddy-simulation model for the study of planetary boundary-layer turbulence. J. Atmos. Sci., 41, 20522062, doi:10.1175/1520-0469(1984)041<2052:ALESMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monin, S. A., and A. M. Obukhov, 1954: Basic turbulence mixing laws in the atmospheric surface layer. Tr. Geofiz. Inst., Akad. Nauk SSSR, 24, 163187.

    • Search Google Scholar
    • Export Citation
  • Schar, C., D. Leuenberger, O. Fuhrer, D. Luthi, and C. Girard, 2002: A new terrain-following vertical coordinate formulation for atmospheric prediction models. Mon. Wea. Rev., 130, 24592480, doi:10.1175/1520-0493(2002)130<2459:ANTFVC>2.0.CO;2.

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

    • Crossref
    • Export Citation
  • Smagorinsky, J., 1963: General circulation experiments with the primitive equations. Mon. Wea. Rev., 91, 99152, doi:10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steffen, M., 1990: A simple method for monotonic interpolation in one dimension. Astron. Astrophys., 239, 443450.

  • Wong, V. C., and D. K. Lilly, 1994: A comparison of two dynamic subgrid closure methods for turbulent thermal-convection. Phys. Fluids, 6, 10161023, doi:10.1063/1.868335.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Assessment of Vertical Mesh Refinement in Concurrently Nested Large-Eddy Simulations Using the Weather Research and Forecasting Model

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  • 1 Lawrence Livermore National Laboratory, Livermore, California
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Abstract

To facilitate multiscale simulation using the Weather Research and Forecasting Model, vertical mesh refinement for one-way concurrent nested simulation was recently introduced. Grid refinement in the vertical dimension removes the requirement of different grid aspect ratios on the bounding versus the nested domain, such that results from refinement are in the horizontal directions only, and thereby can also reduce numerical errors on the bounding domain arising from large aspect ratios in the presence of complex terrain. Herein, the impacts of vertical grid refinement on the evolving downstream flow in nested large-eddy simulations are evaluated in relation to other model configuration choices, including turbulence subfilter-scale (SFS) stress models, mesh configuration, and alternative methods for calculating several near-surface flow parameters. Although vertical nesting requires coarsening of the vertical grid on the bounding domain, leading to a smaller range of resolved turbulence scales in the nest’s lateral boundary conditions, parameter values within the nested domains are generally only minimally impacted, relative to nesting using the same vertical grid on each domain. Two dynamic SFS models examined herein generally improved the simulated mean wind speed, turbulence kinetic energy, stresses and spectra, on both domains, and accelerated equilibration rates within nested domains, relative to two constant coefficient models. A new method of extrapolating horizontal velocity components to near-surface locations at nested domain lateral boundaries, and a correction to the calculation of deformation elements near the surface, are each shown to slightly alter the mean parameter values, yet only minimally impact equilibration rates within the nested domain.

Denotes content that is immediately available upon publication as open access.

© 2017 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: Jeffrey D. Mirocha, jmirocha@llnl.gov

Abstract

To facilitate multiscale simulation using the Weather Research and Forecasting Model, vertical mesh refinement for one-way concurrent nested simulation was recently introduced. Grid refinement in the vertical dimension removes the requirement of different grid aspect ratios on the bounding versus the nested domain, such that results from refinement are in the horizontal directions only, and thereby can also reduce numerical errors on the bounding domain arising from large aspect ratios in the presence of complex terrain. Herein, the impacts of vertical grid refinement on the evolving downstream flow in nested large-eddy simulations are evaluated in relation to other model configuration choices, including turbulence subfilter-scale (SFS) stress models, mesh configuration, and alternative methods for calculating several near-surface flow parameters. Although vertical nesting requires coarsening of the vertical grid on the bounding domain, leading to a smaller range of resolved turbulence scales in the nest’s lateral boundary conditions, parameter values within the nested domains are generally only minimally impacted, relative to nesting using the same vertical grid on each domain. Two dynamic SFS models examined herein generally improved the simulated mean wind speed, turbulence kinetic energy, stresses and spectra, on both domains, and accelerated equilibration rates within nested domains, relative to two constant coefficient models. A new method of extrapolating horizontal velocity components to near-surface locations at nested domain lateral boundaries, and a correction to the calculation of deformation elements near the surface, are each shown to slightly alter the mean parameter values, yet only minimally impact equilibration rates within the nested domain.

Denotes content that is immediately available upon publication as open access.

© 2017 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: Jeffrey D. Mirocha, jmirocha@llnl.gov
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