Editorial Type:
Article
Article Type:
Research Article

Statistical Downscaling of Daily Wind Speed Variations

Megan C. Kirchmeier Department of Atmospheric and Oceanic Sciences and Center for Climatic Research, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by Megan C. Kirchmeier in
Current site
Google Scholar
PubMed Close
,
David J. Lorenz Center for Climatic Research, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by David J. Lorenz in
Current site
Google Scholar
PubMed Close
, and
Daniel J. Vimont Department of Atmospheric and Oceanic Sciences and Center for Climatic Research, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by Daniel J. Vimont in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Mar 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-0230.1
Page(s):
660–675
Restricted access

Abstract

This study presents the development of a method to statistically downscale daily wind speed variations in an extended Great Lakes region. A probabilistic approach is used, predicting a daily-varying probability density function (PDF) of local-scale daily wind speed conditioned on large-scale daily wind speed predictors. Advantages of a probabilistic method are that it provides realistic information on the variance and extremes in addition to information on the mean, it allows the autocorrelation of downscaled realizations to be tuned to match the autocorrelation of local-scale observations, and it allows flexibility in the use of the final downscaled product. Much attention is given to fitting the proper functional form of the PDF by investigating the observed local-scale wind speed distribution (predictand) as a function of the decile of the large-scale wind (predictor). It is found that the local-scale standard deviation and the local-scale shape parameter (from a gamma distribution) are nonconstant functions of the large-scale predictor. As such, a vector generalized linear model is developed to relate the large-scale and local-scale wind speeds. Maximum likelihood and cross validation are used to fit local-scale gamma distribution shape and scale parameters to the large-scale wind speed. The result is a daily-varying probability distribution of local-scale wind speed, conditioned on the large-scale wind speed.

Corresponding author address: Megan Kirchmeier, 1225 W. Dayton St., Madison, WI 53706. E-mail: kirchmeier@wisc.edu

Abstract

This study presents the development of a method to statistically downscale daily wind speed variations in an extended Great Lakes region. A probabilistic approach is used, predicting a daily-varying probability density function (PDF) of local-scale daily wind speed conditioned on large-scale daily wind speed predictors. Advantages of a probabilistic method are that it provides realistic information on the variance and extremes in addition to information on the mean, it allows the autocorrelation of downscaled realizations to be tuned to match the autocorrelation of local-scale observations, and it allows flexibility in the use of the final downscaled product. Much attention is given to fitting the proper functional form of the PDF by investigating the observed local-scale wind speed distribution (predictand) as a function of the decile of the large-scale wind (predictor). It is found that the local-scale standard deviation and the local-scale shape parameter (from a gamma distribution) are nonconstant functions of the large-scale predictor. As such, a vector generalized linear model is developed to relate the large-scale and local-scale wind speeds. Maximum likelihood and cross validation are used to fit local-scale gamma distribution shape and scale parameters to the large-scale wind speed. The result is a daily-varying probability distribution of local-scale wind speed, conditioned on the large-scale wind speed.

Corresponding author address: Megan Kirchmeier, 1225 W. Dayton St., Madison, WI 53706. E-mail: kirchmeier@wisc.edu
Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Beckmann, B.-R., and T. Adri Buishand, 2002: Statistical downscaling relationships for precipitation in the Netherlands and north Germany. Int. J. Climatol., 22, 15–32.

    • Search Google Scholar
    • Export Citation

  • Brodtkorb, P., P. Johannesson, G. Lindgren, I. Rychlik, J. Rydén, and E. Sjö, 2000: WAFO—A Matlab toolbox for the analysis of random waves and loads. Proc. 10th Int. Offshore and Polar Engineering Conf., Vol. 3, Seattle, WA, International Society of Offshore and Polar Engineers, 343–350.

  • Bürger, G., T. Q. Murdock, T. Werner, S. R. Sobie, and J. Cannon, 2012: Downscaling extremes—An intercomparison of multiple statistical methods for present climate. J. Climate, 25, 4366–4388.

    • Search Google Scholar
    • Export Citation

  • Cheng, C. S., G. Li, Q. Li, and H. Auld, 2008: Statistical downscaling of hourly and daily climate scenarios for various meteorological variables in south-central Canada. Theor. Appl. Climatol., 91, 129–147.

    • Search Google Scholar
    • Export Citation

  • Cheng, C. S., G. Li, Q. Li, H. Auld, and C. Fu, 2012: Possible impacts of climate change on wind gusts under downscaled future climate conditions over Ontario, Canada. J. Climate, 25, 3390–3408.

    • Search Google Scholar
    • Export Citation

  • Coleman, T. F., and Y. Li, 1996: An interior trust region approach for nonlinear minimization subject to bounds. SIAM J. Optim., 6, 418–445.

    • Search Google Scholar
    • Export Citation

  • de Rooy, W. C., and K. Kok, 2004: A combined physical–statistical approach for the downscaling of model wind speed. Wea. Forecasting, 19, 485–495.

    • Search Google Scholar
    • Export Citation

  • Engen-Skaugen, T., 2007: Refinement of dynamically downscaled precipitation and temperature scenarios. Climatic Change, 84, 365–382.

    • Search Google Scholar
    • Export Citation

  • Fealy, R., and J. Sweeney, 2007: Statistical downscaling of precipitation for a selection of sites in Ireland employing a generalised linear modelling approach. Int. J. Climatol., 27, 2083–2094.

    • Search Google Scholar
    • Export Citation

  • Frías, M. D., E. Zorita, J. Fernández, and C. Rodríguez-Puebla, 2006: Testing statistical downscaling methods in simulated climates. Geophys. Res. Lett., 33, L19807, doi:10.1029/2006GL027453.

    • Search Google Scholar
    • Export Citation

  • Fuentes, U., and D. Heimann, 2000: An improved statistical–dynamical downscaling scheme and its application to the Alpine precipitation climatology. Theor. Appl. Climatol., 135, 119–135.

    • Search Google Scholar
    • Export Citation

  • Giorgi, F., 2006: Regional climate modeling: Status and perspectives. J. Phys. IV, 139, 101–118.

  • Goubanova, K., V. Echevin, B. Dewitte, F. Codron, K. Takahashi, P. Terray, and M. Vrac, 2010: Statistical downscaling of sea-surface wind over the Peru–Chile upwelling region: Diagnosing the impact of climate change from the IPSL-CM4 model. Climate Dyn., 36, 1365–1378.

    • Search Google Scholar
    • Export Citation

  • Hastie, T., and R. Tibshirani, 1986: Generalized additive models. Stat. Sci., 1, 297–318.

  • Horvath, K., D. Koracin, R. Vellore, J. Jiang, and R. Belu, 2012: Sub-kilometer dynamical downscaling of near-surface winds in complex terrain using WRF and MM5 mesoscale models. J. Geophys. Res., 117, D11111, doi:10.1029/2012JD017432.

    • Search Google Scholar
    • Export Citation

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.

  • Lagarias, J. C., J. a. Reeds, M. H. Wright, and P. E. Wright, 1998: Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM J. Optim., 9, 112–147.

    • Search Google Scholar
    • Export Citation

  • Leander, R., and T. A. Buishand, 2007: Resampling of regional climate model output for the simulation of extreme river flows. J. Hydrol., 332, 487–496.

    • Search Google Scholar
    • Export Citation

  • Maraun, D., H. W. Rust, and T. J. Osborn, 2010a: Synoptic airflow and UK daily precipitation extremes. Extremes, 13, 133–153.

  • Maraun, D., and Coauthors, 2010b: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev. Geophys., 48, RG3003, doi: 10.1029/2009RG000314.

    • Search Google Scholar
    • Export Citation

  • Martinez, Y., W. Yu, and H. Lin, 2013: A new statistical–dynamical downscaling procedure based on EOF analysis for regional time series generation. J. Appl. Meteor. Climatol., 52, 935–952.

    • Search Google Scholar
    • Export Citation

  • McCullagh, P., and J. Nelder, 1989: Generalized Linear Models. 2d ed. CRC Press, 511 pp.

  • Mengelkamp, H., H. Kapitza, and U. Pflueger, 1997: Statistical-dynamical downscaling of wind climatologies. J. Wind Eng. Ind. Aerodyn., 67–68, 449–457.

    • Search Google Scholar
    • Export Citation

  • Michelangeli, P.-A., M. Vrac, and H. Loukos, 2009: Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophys. Res. Lett., 36, L11708, doi:10.1029/2009GL038401.

    • Search Google Scholar
    • Export Citation

  • Monahan, A. H., 2012: Can we see the wind? Statistical downscaling of historical sea surface winds in the subarctic northeast Pacific. J. Climate, 25, 1511–1528.

    • Search Google Scholar
    • Export Citation

  • Najac, J., C. Lac, and L. Terray, 2011: Impact of climate change on surface winds in France using a statistical-dynamical downscaling method with mesoscale modelling. Int. J. Climatol., 31, 415–430.

    • Search Google Scholar
    • Export Citation

  • Oh, J.-H., T. Kim, M.-K. Kim, S.-H. Lee, S.-K. Min, and W.-T. Kwon, 2004: Regional climate simulation for Korea using dynamic downscaling and statistical adjustment. J. Meteor. Soc. Japan, 82, 1629–1643.

    • Search Google Scholar
    • Export Citation

  • Panofsky, H. A., and G. W. Brier, 1958: Some Applications of Statistics to Meteorology. Mineral Industries Continuing Education, College of Mineral Industries, The Pennsylvania State University, 224 pp.

    • Search Google Scholar
    • Export Citation

  • Pryor, S. C., J. Schoof, and R. Barthelmie, 2005: Empirical downscaling of wind speed probability distributions. J. Geophys. Res., 110, doi:10.1029/2005JD005899.

    • Search Google Scholar
    • Export Citation

  • Salameh, T., P. Drobinski, M. Vrac, and P. Naveau, 2009: Statistical downscaling of near-surface wind over complex terrain in southern France. Meteor. Atmos. Phys., 103, 253–265.

    • Search Google Scholar
    • Export Citation

  • Schmidli, J., C. M. Goodess, C. Frei, M. R. Haylock, Y. Hundecha, J. Ribalaygua, and T. Schmith, 2007: Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps. J. Geophys. Res., 112, D04105, doi:10.1029/2005JD007026.

    • Search Google Scholar
    • Export Citation

  • Schmith, T., 2008: Stationarity of regression relationships: Application to empirical downscaling. J. Climate, 21, 4529–4537.

  • van der Kamp, D., C. L. Curry, and A. H. Monahan, 2012: Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. II. Predicting wind components. Climate Dyn., 38, 1301–1311.

    • Search Google Scholar
    • Export Citation

  • Vrac, M., M. Stein, and K. Hayhoe, 2007: Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing. Climate Res., 34, 169–184.

    • Search Google Scholar
    • Export Citation

  • Wood, A. W., 2002: Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res., 107, 4429, doi:10.1029/2001JD000659.

    • Search Google Scholar
    • Export Citation

  • Yan, Z., S. Bate, R. Chandler, V. Isham, and H. Wheater, 2002: An analysis of daily maximum wind speed in northwestern Europe using generalized linear models. J. Climate, 15, 2073–2088.

    • Search Google Scholar
    • Export Citation

  • Yang, C., R. E. Chandler, V. S. Isham, and H. S. Wheater, 2005: Spatial–temporal rainfall simulation using generalized linear models. Water Resour. Res., 41, W11415, doi:10.1029/2004WR003739.

    • Search Google Scholar
    • Export Citation

  • Yee, T. W., and A. G. Stephenson, 2007: Vector generalized linear and additive extreme value models. Extremes, 10, 1–19.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 3480 1875 45
PDF Downloads 961 230 10