A Combined Physical–Statistical Approach for the Downscaling of Model Wind Speed

Wim C. de Rooy Royal Netherlands Meteorological Institute, De Bilt, Netherlands

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Kees Kok Royal Netherlands Meteorological Institute, De Bilt, Netherlands

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Abstract

In this paper a combined physical–statistical approach for the downscaling of model wind speed is assessed. The key factor in this approach is the decomposition of the total error (model − observation) into a small-scale representation mismatch (RM) and a large-scale model error (ME). The RM is caused by the difference between the grid-box mean conditions of the model and the locally valid conditions. For wind speed, the RM is primarily determined by the difference in roughness between the model and the location. In the first step of the combined approach, the physical method (based on surface layer theory) adjusts the model output for the roughness characteristics at several observation sites. For these local wind estimates the RM is strongly reduced but the ME remains. To reduce this ME, the local wind estimates, together with the corresponding observations, are used in one pool to derive one linear regression equation. With local roughness length information derived from land-use maps, this regression equation can then be applied to model output to produce high-resolution wind speed fields. Using a 3-yr dataset, the combined approach is validated at six independent stations in the Netherlands (with different RMs). In this way, it is shown that for observation-free locations the combined approach results in a significant improvement in skill compared to the standard model output as well as the physical method only. The method can be optimized for special conditions, such as high wind speed cases.

Corresponding author address: Wim de Rooy, Royal Netherlands Meteorological Institute, P.O. Box 201, 3730 AE De Bilt, Netherlands. Email: rooyde@knmi.nl

Abstract

In this paper a combined physical–statistical approach for the downscaling of model wind speed is assessed. The key factor in this approach is the decomposition of the total error (model − observation) into a small-scale representation mismatch (RM) and a large-scale model error (ME). The RM is caused by the difference between the grid-box mean conditions of the model and the locally valid conditions. For wind speed, the RM is primarily determined by the difference in roughness between the model and the location. In the first step of the combined approach, the physical method (based on surface layer theory) adjusts the model output for the roughness characteristics at several observation sites. For these local wind estimates the RM is strongly reduced but the ME remains. To reduce this ME, the local wind estimates, together with the corresponding observations, are used in one pool to derive one linear regression equation. With local roughness length information derived from land-use maps, this regression equation can then be applied to model output to produce high-resolution wind speed fields. Using a 3-yr dataset, the combined approach is validated at six independent stations in the Netherlands (with different RMs). In this way, it is shown that for observation-free locations the combined approach results in a significant improvement in skill compared to the standard model output as well as the physical method only. The method can be optimized for special conditions, such as high wind speed cases.

Corresponding author address: Wim de Rooy, Royal Netherlands Meteorological Institute, P.O. Box 201, 3730 AE De Bilt, Netherlands. Email: rooyde@knmi.nl

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  • Achberger, C., Ekström M. , and Bärring L. , 2002: Estimation of local near-surface wind conditions—A comparison of WASP and regression based techniques. Meteor. Appl, 9 , 211221.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beljaars, A. C. M., 1987: The measurements of gustiness at routine wind stations—A review. WMO/IOM Rep. 31, Geneva, Switzerland, 50 pp.

    • Search Google Scholar
    • Export Citation
  • Best, M. J., Bornemann F. J. , Chalcraft B. V. , and Wilson C. A. , 2000: Mesoscale model upgrade—Introduction of the land surface tile scheme (MOSES 2). Forecasting Research Tech. Rep. 341, 30 pp. [Available from Met Office, London Road, Bracknell, Berkshire RG12 2SZ, United Kingdom.].

    • Search Google Scholar
    • Export Citation
  • Brandsma, T., Können G. P. , and Wessels H. R. A. , 2003: Empirical estimation of the effect of urban heat advection on the temperature series of De Bilt (The Netherlands). Int. J. Climatol, 23 , 829845.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Rooy, W., and Kok K. , 2002: On the use of physical and statistical downscaling techniques for NWP model output. KNMI Scientific Paper WR-2002-05, De Bilt, Netherlands, 82 pp.

    • Search Google Scholar
    • Export Citation
  • Holtslag, A. A. M., 1984: Estimates of diabatic wind speed profiles from near-surface weather observations. Bound.-Layer Meteor, 29 , 225250.

  • Källén, E., Ed.,. 1996: HIRLAM documentation manual—System 2.5. SMHI, 233 pp. [Available from SMHI, S-60176 Norrköping, Sweden.].

  • Koster, R. D., and Suarez M. J. , 1992: Modeling the land surface boundary in climate models as a composite of independent vegetation stands. J. Geophys. Res, 97D , 26972715.

    • Search Google Scholar
    • Export Citation
  • Kuo, H. L., 1974: Further studies of the influence of cumulus convection on large-scale flow. J. Atmos. Sci, 31 , 12321240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Landberg, L., and Watson S. J. , 1994: Short-term prediction of local wind conditions. Bound.-Layer Meteor, 70 , 171195.

  • Louis, J. F., 1979: A parametric model of vertical eddy fluxes in the atmosphere. Bound.-Layer Meteor, 17 , 187202.

  • McNaughton, K. G., and Jarvis P. G. , 1984: Using the Penman– Monteith equation predictively. Agric. Water Manage, 8 , 263246.

  • Monin, A. S., and Obukhov A. M. , 1954: Basic laws of turbulent mixing in the ground layer of the atmosphere. Tr. Geofiz. Inst., Akad. Nauk. SSSR, 24 , 163187.

    • Search Google Scholar
    • Export Citation
  • Obukhov, A. M., 1946: Turbulence in an atmosphere with a non-uniform temperature (in Russian). Tr. Akad. Nauk. SSSR Inst. Teorel. Geofiz, 1 , 95115. (English translation: Bound.-Layer Meteor.,2, 7–29.).

    • Search Google Scholar
    • Export Citation
  • Sundqvist, H., 1978: A parameterization scheme for non-convective condensation including prediction of cloud water content. Quart. J. Roy. Meteor. Soc, 104 , 677690.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tijm, S., 2001: Downscaling: Wind op zeer hoge resolutie. Meteorologica, 4 , 3033.

  • Verkaik, J. W., 2000: Evaluation of two gustiness models for exposure correction calculations. J. Appl. Meteor, 39 , 16131626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verkaik, J. W., and Smits A. , 2001: Interpretation and estimation of the local wind climate. Proc. Third European and African Conf. on Wind Engineering, Eindhoven, Netherlands, Eindhoven University of Technology, 43–56.

    • Search Google Scholar
    • Export Citation
  • Wieringa, J., 1976: An objective exposure correction method for average wind speeds measured at a sheltered location. Quart. J. Roy. Meteor. Soc, 102 , 241253.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wieringa, J., 1986: Roughness-dependent geographical interpolation of surface wind speed averages. Quart. J. Roy. Meteor. Soc, 112 , 867889.

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