The Impact of Grid and Spectral Nudging on the Variance of the Near-Surface Wind Speed

Claire Louise Vincent Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark

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Andrea N. Hahmann Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark

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Abstract

Grid and spectral nudging are effective ways of preventing drift from large-scale weather patterns in regional climate models. However, the effect of nudging on the wind speed variance is unclear. In this study, the impact of grid and spectral nudging on near-surface and upper boundary layer wind variance in the Weather Research and Forecasting Model is analyzed. Simulations are run on nested domains with horizontal grid spacing of 15 and 5 km over the Baltic Sea region. For the 15-km domain, 36-h simulations initialized each day are compared with 11-day simulations with either grid or spectral nudging at and above 1150 m above ground level (AGL). Nested 5-km simulations are not nudged directly but inherit boundary conditions from the 15-km experiments. Spatial and temporal spectra show that grid nudging causes smoothing of the wind in the 15-km domain at all wavenumbers, both at 1150 m AGL and near the surface where nudging is not applied directly, while spectral nudging mainly affects longer wavenumbers. Maps of mesoscale variance show spatial smoothing for both grid and spectral nudging, although the effect is less pronounced for spectral nudging. On the inner, 5-km domain, an indirect smoothing impact of nudging is seen up to 200 km inward from the dominant inflow boundary at 1150 m AGL, but there is minimal smoothing from the nudging near the surface, indicating that nudging an outer domain is an appropriate configuration for wind-resource modeling.

Current affiliation: School of Earth Sciences and ARC Centre of Excellence for Climate System Science, The University of Melbourne, Melbourne, Australia.

Corresponding author address: Claire Louise Vincent, School of Earth Sciences, The University of Melbourne, Melbourne VIC 3010, Australia. E-mail: claire.vincent@unimelb.edu.au

Abstract

Grid and spectral nudging are effective ways of preventing drift from large-scale weather patterns in regional climate models. However, the effect of nudging on the wind speed variance is unclear. In this study, the impact of grid and spectral nudging on near-surface and upper boundary layer wind variance in the Weather Research and Forecasting Model is analyzed. Simulations are run on nested domains with horizontal grid spacing of 15 and 5 km over the Baltic Sea region. For the 15-km domain, 36-h simulations initialized each day are compared with 11-day simulations with either grid or spectral nudging at and above 1150 m above ground level (AGL). Nested 5-km simulations are not nudged directly but inherit boundary conditions from the 15-km experiments. Spatial and temporal spectra show that grid nudging causes smoothing of the wind in the 15-km domain at all wavenumbers, both at 1150 m AGL and near the surface where nudging is not applied directly, while spectral nudging mainly affects longer wavenumbers. Maps of mesoscale variance show spatial smoothing for both grid and spectral nudging, although the effect is less pronounced for spectral nudging. On the inner, 5-km domain, an indirect smoothing impact of nudging is seen up to 200 km inward from the dominant inflow boundary at 1150 m AGL, but there is minimal smoothing from the nudging near the surface, indicating that nudging an outer domain is an appropriate configuration for wind-resource modeling.

Current affiliation: School of Earth Sciences and ARC Centre of Excellence for Climate System Science, The University of Melbourne, Melbourne, Australia.

Corresponding author address: Claire Louise Vincent, School of Earth Sciences, The University of Melbourne, Melbourne VIC 3010, Australia. E-mail: claire.vincent@unimelb.edu.au
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  • Bowden, J. H., T. L. Otte, C. G. Nolte, and M. J. Otte, 2012: Examining interior grid nudging techniques using two-way nesting in the WRF Model for regional climate modeling. J. Climate, 25, 28052823, doi:10.1175/JCLI-D-11-00167.1.

    • Search Google Scholar
    • Export Citation
  • Bowden, J. H., C. G. Nolte, and T. L. Otte, 2013: Simulating the impact of the large-scale circulation on the 2-m temperature and precipitation climatology. Climate Dyn., 40, 19031920, doi:10.1007/s00382-012-1440-y.

    • Search Google Scholar
    • Export Citation
  • Bullock, O. R., K. Alapaty, J. A. Herwehe, M. S. Mallard, T. L. Otte, R. C. Gilliam, and C. G. Nolte, 2014: An observation-based investigation of nudging in WRF for downscaling surface climate information to 12-km grid spacing. J. Appl. Meteor. Climatol., 53, 2033, doi:10.1175/JAMC-D-13-030.1.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Feser, F., 2006: Enhanced detectability of added value in limited-area model results separated into different spatial scales. Mon. Wea. Rev., 134, 21802190, doi:10.1175/MWR3183.1.

    • Search Google Scholar
    • Export Citation
  • Hahmann, A. N., C. L. Vincent, A. Peña, J. Lange, and C. B. Hasager, 2014: Wind climate estimation using WRF Model output: Method and model sensitivities over the sea. Int. J. Climatol., doi:10.1002/joc.4217, in press.

    • Search Google Scholar
    • Export Citation
  • Högström, U., and Coauthors, 2008: Momentum fluxes and wind gradients in the marine boundary layer—A multi-platform study. Boreal Environ. Res., 13, 475502. [Available online at http://www.borenv.net/BER/pdfs/ber13/ber13-475.pdf.]

    • Search Google Scholar
    • Export Citation
  • Larsén, X. G., C. Vincent, and S. Larsen, 2013: Spectral structure of mesoscale winds over the water. Quart. J. Roy. Meteor. Soc., 139, 685700, doi:10.1002/qj.2003.

    • Search Google Scholar
    • Export Citation
  • Liu, P., A. P. Tsimpidi, Y. Hu, B. Stone, A. G. Russell, and A. Nenes, 2012: Differences between downscaling with spectral and grid nudging using WRF. Atmos. Chem. Phys., 12, 36013610, doi:10.5194/acp-12-3601-2012.

    • Search Google Scholar
    • Export Citation
  • Lo, J. C.-F., Z.-L. Yang, and R. A. Pielke, 2008: Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) Model. J. Geophys. Res., 113, D09112, doi:10.1029/2007JD009216.

    • Search Google Scholar
    • Export Citation
  • Miguez-Macho, G., G. L. Stenchikov, and A. Robock, 2004: Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J. Geophys. Res., 109, D13104, doi:10.1029/2003JD004495.

    • Search Google Scholar
    • Export Citation
  • Nastrom, G. D., and K. S. Gage, 1985: A climatology of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft. J. Atmos. Sci., 42, 950960, doi:10.1175/1520-0469(1985)042<0950:ACOAWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Oppenheim, A. V., and R. W. Schafer, 2009: Discrete-Time Signal Processing. 3rd ed. Prentice Hall, 1120 pp.

  • Otte, T. L., C. G. Nolte, M. J. Otte, and J. H. Bowden, 2012: Does nudging squelch the extremes in regional climate modeling? J. Climate, 25, 70467066, doi:10.1175/JCLI-D-12-00048.1.

    • Search Google Scholar
    • Export Citation
  • Peña, A., S.-E. Gryning, and A. N. Hahmann, 2013: Observations of the atmospheric boundary layer height under marine upstream flow conditions at a coastal site. J. Geophys. Res., 118, 19241940, doi:10.1002/jgrd.50175.

    • Search Google Scholar
    • Export Citation
  • Rife, D., J. Pinto, A. Monaghan, C. Davis, and J. Hannan, 2010: Global distribution and characteristics of diurnally varying low-level jets. J. Climate, 23, 50415064, doi:10.1175/2010JCLI3514.1.

    • Search Google Scholar
    • Export Citation
  • Rutgersson, A., M. Norman, B. Schneider, H. Pettersson, and E. Sahlée, 2008: The annual cycle of carbon dioxide and parameters influencing the air–sea carbon exchange in the Baltic proper. J. Mar. Syst., 74, 381394, doi:10.1016/j.jmarsys.2008.02.005.

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

    • 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. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3_bw.pdf.]

  • Sørensen, P., N. A. Cutululis, A. Vigueras-Rodríguez, H. Madsen, P. Pinson, L. Jensen, J. Hjerrild, and M. Donovan, 2008: Modelling of power fluctuations from large offshore wind farms. Wind Energy, 11, 2943, doi:10.1002/we.246.

    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic, 666 pp.

  • Taylor, M., J. Freedman, K. Waight, and M. Brower, 2009: Using simulated wind data from a mesoscale model in MCP. AWS Truewind Tech. Rep., 10 pp. [Available online at https://www.awstruepower.com/assets/Using-Simulated-Wind-Data-From-a-Mesoscale-Model-Data-in-MCP1.pdf.]

  • Vigueras-Rodrìguez, A., P. Sørensen, N. A. Cutululis, A. Viedma, and M. H. Donovan, 2010: Wind model for low frequency power fluctuations in offshore wind farms. Wind Energy, 13, 471482, doi:10.1002/we.368.

    • Search Google Scholar
    • Export Citation
  • Vincent, C. L., P. Pinson, and G. Giebel, 2011: Wind fluctuations over the North Sea. Int. J. Climatol., 31, 15841595, doi:10.1002/joc.2175.

    • Search Google Scholar
    • Export Citation
  • Vincent, C. L., X. G. Larsén, S. E. Larsen, and P. Sørensen, 2013: Cross-spectra over the sea from observations and mesoscale modelling. Bound.-Layer Meteor., 146, 297318, doi:10.1007/s10546-012-9754-1.

    • Search Google Scholar
    • Export Citation
  • Welch, P. D., 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 (2), 7073, doi:10.1109/TAU.1967.1161901.

    • Search Google Scholar
    • Export Citation
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