Boundary-Layer Meteorology

, Volume 135, Issue 1, pp 161–175 | Cite as

Statistical Downscaling of Wind Variability from Meteorological Fields

  • Robert J. Davy
  • Milton J. Woods
  • Christopher J. Russell
  • Peter A. Coppin
Article

Abstract

Measurements show that on numerous occasions the low-level wind is highly variable across a large portion of south-eastern Australia. Under such conditions the risk of a large rapid change in total wind power is increased. While variability tends to increase with mean wind speed, a large component of wind variability is not explained by wind speed alone. In this work, reanalysis fields from the US National Centers for Environmental Prediction (NCEP) are statistically downscaled to model wind variability at a coastal location in Victoria, Australia. In order to reduce the dimensionality of the problem, the NCEP fields are each decomposed using empirical orthogonal function (EOF) techniques. The downscaling technique is applied to two periods in the seasonal cycle, namely (i) winter to early spring, and (ii) summer. In each case, data representing 2 years are used to form a model that is then validated using independent data from another year. The EOFs that best predict wind variability are examined. To allow for non-linearity and complex interaction between variables, all empirical models are built using random forests. Quantitatively, the model compares favourably with a simple regression of wind variability against wind speed, as well as multiple linear regression models.

Keywords

Downscaling Empirical orthogonal functions Random forests Wind forecasting Wind variability 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Breiman L (2001) Random forests. Mach Learn 45: 5–32CrossRefGoogle Scholar
  2. Brümmer B (1997) Boundary layer mass, water, and heat budgets in wintertime cold-air outbreaks from the arctic sea ice. Mon Weather Rev 125: 1824–1837CrossRefGoogle Scholar
  3. Coppin P, Davy R, Russell C, Noonan J (2006) Analysis of the impacts of extreme weather events on wind energy generation in South East Australia. Tech. Rep., CSIRO Marine and Atmospheric Research, prepared for the Australian Greenhouse Office, 104 ppGoogle Scholar
  4. Costa A, Crespo A, Navarro J, Lizcano G, Madsen H, Feitosa E (2008) A review on the young history of the wind power short-term prediction. Renew Sustain Energy Rev 12: 1725–1744CrossRefGoogle Scholar
  5. Eccel E, Ghielmi L, Granitto P, Barbiero R, Grazzini F, Cesari D (2007) Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models. Nonlinear Proc Geophys 14: 211–222CrossRefGoogle Scholar
  6. Hartmann J, Kottmeier C, Raasch S (1997) Roll vortices and boundary-layer development during a cold air outbreak. Boundary-Layer Meteorol 84: 45–65CrossRefGoogle Scholar
  7. Jimenez PA, Gonzalez-Rouco JF, Montavez JP, Navarro J, Garcia-Bustamante E, Valero F (2008) Surface wind regionalization in complex terrain. J Appl Meteorol Clim 47: 308–325CrossRefGoogle Scholar
  8. Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2: 18–22Google Scholar
  9. Pinson P, Kariniotakis G (2004) On-line assessment of prediction risk for wind power production forecasts. Wind Energy 7: 119–132CrossRefGoogle Scholar
  10. Pryor SC, Schoof JT, Barthelmie RJ (2005) Climate change impacts on wind speeds and wind energy density in northern Europe: empirical downscaling of multiple AOGCMs. Clim Res 29: 183–198CrossRefGoogle Scholar
  11. Schoof J, Pryor S (2001) Downscaling temperature and precipitation: a comparison of regression-based methods and artificial neural networks. Int J Climatol 21: 773–790CrossRefGoogle Scholar
  12. Simmonds I, Richter T (2000) Synoptic comparison of cold events in winter and summer in Melbourne and Perth. Theor Appl Climatol 67: 19–32CrossRefGoogle Scholar
  13. Sørensen P, Hansen AD, Rosas PAC (2002) Wind models for simulation of power fluctuations from wind farms. J Wind Eng Ind Aerodyn 90: 1381–1402CrossRefGoogle Scholar
  14. Tande JOG (2003) Grid integration of wind farms. Wind Energy 6: 281–295CrossRefGoogle Scholar
  15. Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York, p 495Google Scholar
  16. von Storch H, Zwiers F (2002) Statistical analysis in climate research. Cambridge University Press, New York, p 496Google Scholar
  17. Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14: 35–62CrossRefGoogle Scholar
  18. Zorita E, von Storch H (1998) The analog method as a simple statistical downscaling technique: comparison with more complicated methods. J Clim 12: 2474–2489CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Robert J. Davy
    • 1
  • Milton J. Woods
    • 1
  • Christopher J. Russell
    • 1
  • Peter A. Coppin
    • 1
  1. 1.Centre for Australian Weather and Climate Research, A Partnership between CSIRO and the Bureau of MeteorologyCanberraAustralia

Personalised recommendations