Climate Dynamics

, Volume 44, Issue 1–2, pp 529–542 | Cite as

Downscaling near-surface wind over complex terrain using a physically-based statistical modeling approach

  • Hsin-Yuan Huang
  • Scott B. Capps
  • Shao-Ching Huang
  • Alex Hall
Article

Abstract

A physically-based statistical modeling approach to downscale coarse resolution reanalysis near-surface winds over a region of complex terrain is developed and tested in this study. Our approach is guided by physical variables and meteorological relationships that are important for determining near-surface wind flow. Preliminary fine scale winds are estimated by correcting the course-to-fine grid resolution mismatch in roughness length. Guided by the physics shaping near-surface winds, we then formulate a multivariable linear regression model which uses near-surface micrometeorological variables and the preliminary estimates as predictors to calculate the final wind products. The coarse-to-fine grid resolution ratio is approximately 10–1 for our study region of southern California. A validated 3-km resolution dynamically-downscaled wind dataset is used to train and validate our method. Winds from our statistical modeling approach accurately reproduce the dynamically-downscaled near-surface wind field with wind speed magnitude and wind direction errors of <1.5 ms−1 and 30°, respectively. This approach can greatly accelerate the production of near-surface wind fields that are much more accurate than reanalysis data, while limiting the amount of computational and time intensive dynamical downscaling. Future studies will evaluate the ability of this approach to downscale other reanalysis data and climate model outputs with varying coarse-to-fine grid resolutions and domains of interest.

Keywords

Near-surface wind Dynamical downscaling Statistical downscaling Complex terrain 

References

  1. Capps SB, Hall A, Hughes M (2014) Sensitivity of Southern California wind energy to turbine characteristics. Wind Energy 17:141–159CrossRefGoogle Scholar
  2. Changnon SA, Fosse ER, Lecomte EL (1999) Interactions between the atmospheric sciences and insurers in the United States. Clim Change 42:51–67CrossRefGoogle Scholar
  3. Chen F, Kusaka H, Bornstein R, Ching J, Grimmond CSB, Grossman-Clarke S, Loridan T, Manning KW, Martilli A, Miao S, Sailor D, Salamanca FP, Taha H, Tewari M, Wang X, Wyszogrodzki AA, Zhang C (2011) The integrated WRF/urban modeling system: development, evaluation, and applications to urban environmental problems. Int J Climatol 31:273–288CrossRefGoogle Scholar
  4. Colette A, Vautard R, Vrac M (2012) Regional climate downscaling with prior statistical correction of the global climate forcing. Geophys Res Lett 39:L13707CrossRefGoogle Scholar
  5. Conil S, Hall A (2006) Local regimes of atmospheric variability: a case study of Southern California. J Clim 19:4308–4325CrossRefGoogle Scholar
  6. Curry CL, van der Kamp D, Monahan AH (2012) Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. I. Predicting wind speed. Clim Dyn 38:1281–1299CrossRefGoogle Scholar
  7. de Rooy WC, Kok K (2004) A combined physical-statistical approach for the downscaling of model wind speed. Weather Forecast 19:485–495CrossRefGoogle Scholar
  8. Dudhia J (1989) Numerical study of convection observed during the winter Monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107CrossRefGoogle Scholar
  9. Garratt JR (1994) The atmospheric boundary layer. Cambridge University Press, New York, pp 49–60Google Scholar
  10. Gustafson WI Jr, Leung LR (2007) Regional downscaling for air quality assessment. A reasonable proposition? Bull Am Meteorol Soc 88:1215–1227CrossRefGoogle Scholar
  11. Gutierrez JM, Cofino AS, Cano R, Rodrıguez MA (2004) Clustering methods for statistical downscaling in short-range weather forecasts. Mon Weather Rev 132:2169–2183CrossRefGoogle Scholar
  12. Haas R, Pinto JG (2012) A combined statistical and dynamical approach for downscaling large-scale footprints of European windstorms. Geophys Res Lett 39:L23804CrossRefGoogle Scholar
  13. Huang H-Y, Margulis SA (2009) On the impact of surface heterogeneity on a realistic convective boundary layer. Water Resour Res 45:W04425CrossRefGoogle Scholar
  14. Hughes M, Hall A (2010) Local and synoptic mechanisms causing Southern California’s Santa Ana winds. Clim Dyn 34:847–857CrossRefGoogle Scholar
  15. Jungo P, Goytette S, Beniston M (2002) Daily wind gust speed probabilities over Switzerland according to three types of synoptic circulation. Int J Climatol 22:485–499CrossRefGoogle Scholar
  16. Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181CrossRefGoogle Scholar
  17. Lebassi-Habtezion B, González J, Bornstein R (2011) Modeled large-scale warming impacts on summer California coastal-cooling trends. J Geophys Res 116:D20114CrossRefGoogle Scholar
  18. Lin Y-L, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Clim Appl Meteorol 22:1065–1092CrossRefGoogle Scholar
  19. Lo JC-F, Yang Z-L, Pielke RA Sr (2008) Assessment of three dynamical climate downscaling methods using the weather research and forecasting (WRF) model. J Geophys Res 113:D09112Google Scholar
  20. McNaughton KG, Jarvis PG (1984) Using the Penman–Monteith equation predictively. Agric Water Manag 8:263–278CrossRefGoogle Scholar
  21. Mesinger F, Treadon RE (1995) “Horizontal” reduction of pressure to sea level: comparison against the NMC’s Shuell method. Mon Weather Rev 123:59–68CrossRefGoogle Scholar
  22. Mesinger F, DiMego G, Kalnay E, Mitchell K, Shafran PC, Ebisuzaki W, Jović D, Woollen J, Rogers E, Berbery EH, Ek MB, Fan Y, Grumbine R, Higgins W, Li H, Lin Y, Manikin G, Parrish D, Shi W (2006) North American regional reanalysis. Bull Am Meteorol Soc 87:343–360CrossRefGoogle Scholar
  23. Michelangeli P-A, Vrac M, Loukos H (2009) Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophys Res Lett 36:L11708CrossRefGoogle Scholar
  24. Mlawer EJ, Taubman PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(16):663–682Google Scholar
  25. Monin AS, Obukhov AM (1954) Basic laws of turbulent mixing in the ground layer of the atmosphere. Tr Geofiz Inst Akad Nauk SSSR 24:163–187Google Scholar
  26. Nakanishi M, Niino H (2004) An improved Mellor–Yamada level-3 model with condensation physics: its design and verification. Bound Layer Meteorol 112:1–31CrossRefGoogle Scholar
  27. Pryor SC, Schoof JT, Barthelmie RJ (2005) Empirical downscaling of wind speed probability distributions. J Geophys Res 110:D19109CrossRefGoogle Scholar
  28. Sailor DJ, Hu T, Li X, Rosen JN (2000) A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change. Renew Energy 19:359–378CrossRefGoogle Scholar
  29. Salameh T, Drobinski P, Vrac M, Naveau P (2009) Statistical downscaling of near-surface wind over complex terrain in southern France. Meteorol Atmos Phys 103:253–265CrossRefGoogle Scholar
  30. Skamarock WC (2004) Evaluating mesoscale NWP models using kinetic energy spectra. Mon Weather Rev 132:3019–3032CrossRefGoogle Scholar
  31. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda M, Huang X-Y, Wang W, Power JG (2008) A description of the advanced research WRF version 3. NCAR technical note, NCAR/tech notes-475 + STR, 125 ppGoogle Scholar
  32. Strassberg D, LeMone MA, Warner TT, Alfieri JG (2008) Comparison of observed 10-m wind speeds to those based on Monin–Obukhov similarity theory using IHOP_2002 aircraft and surface data. Mon Weather Rev 136:964–972CrossRefGoogle Scholar
  33. Vrac M, Stein ML, Hayhoe K, Liang X-Z (2007) A general method for validating statistical downscaling methods under future climate change. Geophys Res Lett 34:L18701CrossRefGoogle Scholar
  34. Wieringa J (1986) Roughness-dependent geographical interpolation of surface wind speed averages. Q J R Meteorol Soc 112:867–889CrossRefGoogle Scholar
  35. Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21:530–548CrossRefGoogle Scholar
  36. Yoon J-H, Ruby Leung L, Correia J Jr (2012) Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States. J Geophys Res 117:D21109Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Hsin-Yuan Huang
    • 1
  • Scott B. Capps
    • 2
  • Shao-Ching Huang
    • 3
  • Alex Hall
    • 2
  1. 1.Joint Institute for Regional Earth System Science and EngineeringUniversity of California, Los AngelesLos AngelesUSA
  2. 2.Department of Atmospheric and Oceanic SciencesUniversity of California, Los AngelesLos AngelesUSA
  3. 3.Institute for Digital Research and EducationUniversity of California, Los AngelesLos AngelesUSA

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