Editorial Type:
Article
Article Type:
Research Article

Evaluation of the Impact of Horizontal Grid Spacing in Terra Incognita on Coupled Mesoscale–Microscale Simulations Using the WRF Framework

Raj K. Rai Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by Raj K. Rai in
Current site
Google Scholar
PubMed Close
,
Larry K. Berg Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by Larry K. Berg in
Current site
Google Scholar
PubMed Close
,
Branko Kosović National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Branko Kosović in
Current site
Google Scholar
PubMed Close
,
Sue Ellen Haupt National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Sue Ellen Haupt in
Current site
Google Scholar
PubMed Close
,
Jeffrey D. Mirocha Lawrence Livermore National Laboratory, Livermore, California

Search for other papers by Jeffrey D. Mirocha in
Current site
Google Scholar
PubMed Close
,
Brandon L. Ennis Sandia National Laboratories, Albuquerque, New Mexico

Search for other papers by Brandon L. Ennis in
Current site
Google Scholar
PubMed Close
, and
Caroline Draxl National Renewable Energy Laboratory, Golden, Colorado

Search for other papers by Caroline Draxl in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Mar 2019
DOI:
https://doi.org/10.1175/MWR-D-18-0282.1
Page(s):
1007–1027
Restricted access

Abstract

Coupled mesoscale–microscale simulations are required to provide time-varying weather-dependent inflow and forcing for large-eddy simulations under general flow conditions. Such coupling necessarily spans a wide range of spatial scales (i.e., ~10 m to ~10 km). Herein, we use simulations that involve multiple nested domains with horizontal grid spacings in the terra incognita (i.e., km) that may affect simulated conditions in both the outer and inner domains. We examine the impact on simulated wind speed and turbulence associated with forcing provided by a terrain with grid spacing in the terra incognita. We perform a suite of simulations that use combinations of varying horizontal grid spacings and turbulence parameterization/modeling using the Weather Research and Forecasting (WRF) Model using a combination of planetary boundary layer (PBL) and large-eddy simulation subgrid-scale (LES-SGS) models. The results are analyzed in terms of spectral energy, turbulence kinetic energy, and proper orthogonal decomposition (POD) energy. The results show that the output from the microscale domain depends on the type of turbulence model (e.g., PBL or LES-SGS model) used for a given horizontal grid spacing but is independent of the horizontal grid spacing and turbulence modeling of the parent domain. Simulation using a single domain produced less POD energy in the first few modes compared to a coupled simulation (one-way nesting) for similar horizontal grid spacing, which highlights that coupled simulations are required to accurately pass the mesoscale features into the microscale domain.

© 2019 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: Raj K. Rai, raj.rai@pnnl.gov

Abstract

Coupled mesoscale–microscale simulations are required to provide time-varying weather-dependent inflow and forcing for large-eddy simulations under general flow conditions. Such coupling necessarily spans a wide range of spatial scales (i.e., ~10 m to ~10 km). Herein, we use simulations that involve multiple nested domains with horizontal grid spacings in the terra incognita (i.e., km) that may affect simulated conditions in both the outer and inner domains. We examine the impact on simulated wind speed and turbulence associated with forcing provided by a terrain with grid spacing in the terra incognita. We perform a suite of simulations that use combinations of varying horizontal grid spacings and turbulence parameterization/modeling using the Weather Research and Forecasting (WRF) Model using a combination of planetary boundary layer (PBL) and large-eddy simulation subgrid-scale (LES-SGS) models. The results are analyzed in terms of spectral energy, turbulence kinetic energy, and proper orthogonal decomposition (POD) energy. The results show that the output from the microscale domain depends on the type of turbulence model (e.g., PBL or LES-SGS model) used for a given horizontal grid spacing but is independent of the horizontal grid spacing and turbulence modeling of the parent domain. Simulation using a single domain produced less POD energy in the first few modes compared to a coupled simulation (one-way nesting) for similar horizontal grid spacing, which highlights that coupled simulations are required to accurately pass the mesoscale features into the microscale domain.

© 2019 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: Raj K. Rai, raj.rai@pnnl.gov
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Bendat, J. S., and A. G. Piersol, 2011: Random Data: Analysis and Measurement Procedures. Wiley Series in Probability and Statistics, Vol. 729, John Wiley & Sons, 640 pp.

    • Crossref
    • Export Citation

  • Berkooz, G., P. Holmes, and J. L. Lumley, 1993: The proper orthogonal decomposition in the analysis of turbulent flows. Annu. Rev. Fluid Mech., 25, 539–575, https://doi.org/10.1146/annurev.fl.25.010193.002543.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ching, J., R. Rotunno, M. Lemone, A. Martilli, B. Kosović, P. A. Jimenez, and J. Dudhia, 2014: Convectively induced secondary circulations in fine-grid mesoscale numerical weather prediction models. Mon. Wea. Rev., 142, 3284–3302, https://doi.org/10.1175/MWR-D-13-00318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Efstathiou, G., R. Beare, S. Osborne, and A. Lock, 2016: Grey zone simulations of the morning convective boundary layer development. J. Geophys. Res. Atmos., 121, 4769–4782, https://doi.org/10.1002/2016JD024860.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Fan, J., and Coauthors, 2017: Cloud-resolving model intercomparison of an MC3E squall line case: Part I—Convective updrafts. J. Geophys. Res. Atmos., 122, 9351–9378, https://doi.org/10.1002/2017JD026622.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Fierro, A. O., E. R. Mansell, C. L. Ziegler, and D. R. MacGorman, 2012: Application of a lightning data assimilation technique in the WRF-ARW model at cloud-resolving scales for the tornado outbreak of 24 May 2011. Mon. Wea. Rev., 140, 2609–2627, https://doi.org/10.1175/MWR-D-11-00299.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gibbs, J. A., and E. Fedorovich, 2014: Comparison of convective boundary layer velocity spectra retrieved from large-eddy-simulation and Weather Research and Forecasting Model data. J. Appl. Meteor. Climatol., 53, 377–394, https://doi.org/10.1175/JAMC-D-13-033.1.

    • 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, 2318–2341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Honnert, R., V. Masson, and F. Couvreux, 2011: A diagnostic for evaluating the representation of turbulence in atmospheric models at the kilometric scale. J. Atmos. Sci., 68, 3112–3131, https://doi.org/10.1175/JAS-D-11-061.1.

    • 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

  • Kelley, C. L., and B. L. Ennis, 2016: SWiFT Site Atmospheric Characterization. Tech. Rep. SAND2016-0216, Sandia National Laboratories, Alburquerque, NM, 82 pp., https://prod-ng.sandia.gov/techlib-noauth/access-control.cgi/2016/160216.pdf.

  • Kurowski, M. J., and J. Teixeira, 2018: A scale-adaptive turbulent kinetic energy closure for the dry convective boundary layer. J. Atmos. Sci., 75, 675–690, https://doi.org/10.1175/JAS-D-16-0296.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lilly, D. K., 1967: The representation of small-scale turbulence in numerical simulation experiments. Proc. IBM Scientific Computing Symp. on Environmental Sciences, Yorktown Heights, NY, IBM.

  • Lumley, J. L., 1967: The structure of inhomogeneous turbulent flows. Atmospheric Turbulence and Radio Wave Propagation, A. M. Yaglom and V. I. Tatarski, Eds., Nauka, 166–178.

  • Mazzaro, L. J., D. Muñoz-Esparza, J. K. Lundquist, and R. R. Linn, 2017: Nested mesoscale-to-LES modeling of the atmospheric boundary layer in the presence of under-resolved convective structures. J. Adv. Model. Earth Syst., 9, 1795–1810, https://doi.org/10.1002/2017MS000912.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, https://doi.org/10.1175/BAMS-87-3-343.

    • 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, 991–1007, https://doi.org/10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Muñoz-Esparza, D., B. Kosović, J. Mirocha, and J. van Beeck, 2014: Bridging the transition from mesoscale to microscale turbulence in numerical weather prediction models. Bound.-Layer Meteor., 153, 409–440, https://doi.org/10.1007/s10546-014-9956-9.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Muñoz-Esparza, D., J. K. Lundquist, J. A. Sauer, B. Kosović, and R. R. Linn, 2017: Coupled mesoscale-LES modeling of a diurnal cycle during the CWEX-13 field campaign: From weather to boundary-layer eddies. J. Adv. Model. Earth Syst., 9, 1572–1594, https://doi.org/10.1002/2017MS000960

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nakanishi, M., and H. Niino, 2006: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rai, R. K., L. K. Berg, B. Kosović, J. D. Mirocha, M. S. Pekour, and W. J. Shaw, 2017a: Comparison of measured and numerically simulated turbulence statistics in a convective boundary layer over complex terrain. Bound.-Layer Meteor., 163, 69–89, https://doi.org/10.1007/s10546-016-0217-y.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rai, R. K., L. K. Berg, M. Pekour, W. J. Shaw, B. Kosovic, J. D. Mirocha, and B. L. Ennis, 2017b: Spatiotemporal variability of turbulence kinetic energy budgets in the convective boundary layer over both simple and complex terrain. J. Appl. Meteor. Climatol., 56, 3285–3302, https://doi.org/10.1175/JAMC-D-17-0124.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rodrigo, J. S., M. Churchfield, and B. Kosović, 2016: A wind energy benchmark for ABL modelling of a diurnal cycle with a nocturnal low-level jet: GABLS3 revisited. J. Phys.: Conf. Ser., 753, https://doi.org/10.1088/1742-6596/753/3/032024.

    • Search Google Scholar
    • Export Citation

  • Salesky, S. T., M. Chamecki, and E. Bou-Zeid, 2017: On the nature of the transition between roll and cellular organization in the convective boundary layer. Bound.-Layer Meteor., 163, 41–68, https://doi.org/10.1007/s10546-016-0220-3.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shah, S., and E. Bou-Zeid, 2014: Very-large-scale motions in the atmospheric boundary layer educed by snapshot proper orthogonal decomposition. Bound.-Layer Meteor., 153, 355–387, https://doi.org/10.1007/s10546-014-9950-2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shin, H. H., and S.-Y. Hong, 2013: Analysis of resolved and parameterized vertical transports in convective boundary layers at gray-zone resolutions. J. Atmos. Sci., 70, 3248–3261, https://doi.org/10.1175/JAS-D-12-0290.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shin, H. H., and S.-Y. Hong, 2015: Representation of the subgrid-scale turbulent transport in convective boundary layers at gray-zone resolutions. Mon. Wea. Rev., 143, 250–271, https://doi.org/10.1175/MWR-D-14-00116.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shin, H. H., and J. Dudhia, 2016: Evaluation of PBL parameterizations in WRF at subkilometer grid spacings: Turbulence statistics in the dry convective boundary layer. Mon. Wea. Rev., 144, 1161–1177, https://doi.org/10.1175/MWR-D-15-0208.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sirovich, L., 1987: Turbulence and the dynamics of coherent structures. I. Coherent structures. Quart. Appl. Math., 45, 561–571, https://doi.org/10.1090/qam/910462.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 3019–3032, https://doi.org/10.1175/MWR2830.1.

    • 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., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation

  • Smagorinsky, J., 1963: General circulation experiments with the primitive equations: I. The basic experiment. Mon. Wea. Rev., 91, 99–164, https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Smith, T. R., J. Moehlis, and P. Holmes, 2005: Low-dimensional modelling of turbulence using the proper orthogonal decomposition: A tutorial. Nonlinear Dyn., 41, 275–307, https://doi.org/10.1007/s11071-005-2823-y.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Talbot, C., E. Bou-Zeid, and J. Smith, 2012: Nested mesoscale large-eddy simulations with WRF: Performance in real test cases. J. Hydrometeor., 13, 1421–1441, https://doi.org/10.1175/JHM-D-11-048.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tennekes, H., and J. L. Lumley, 1972: A First Course in Turbulence. The MIT Press, 300 pp.

    • Crossref
    • Export Citation

  • Weckwerth, T. M., T. W. Horst, and J. W. Wilson, 1999: An observational study of the evolution of horizontal convective rolls. Mon. Wea. Rev., 127, 2160–2179, https://doi.org/10.1175/1520-0493(1999)127<2160:AOSOTE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Welch, P., 1967: The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust., 15, 70–73, https://doi.org/10.1109/TAU.1967.1161901.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wyngaard, J. C., 2004: Toward numerical modeling in the “terra incognita.” J. Atmos. Sci., 61, 1816–1826, https://doi.org/10.1175/1520-0469(2004)061<1816:TNMITT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, X., J.-W. Bao, B. Chen, and E. D. Grell, 2018: A three-dimensional scale-adaptive turbulent kinetic energy scheme in the WRF-ARW Model. Mon. Wea. Rev., 146, 2023–2045, https://doi.org/10.1175/MWR-D-17-0356.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhou, B., J. S. Simon, and F. K. Chow, 2014: The convective boundary layer in the terra incognita. J. Atmos. Sci., 71, 2545–2563, https://doi.org/10.1175/JAS-D-13-0356.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1237 362 31
PDF Downloads 9353 281 15