Editorial Type:
Article
Article Type:
Research Article

Probabilistic Wind Gust Forecasting Using Nonhomogeneous Gaussian Regression

Thordis L. Thorarinsdottir Institute of Applied Mathematics, Heidelberg University, Heidelberg, Germany

Search for other papers by Thordis L. Thorarinsdottir in
Current site
Google Scholar
PubMed Close
and
Matthew S. Johnson Department of Statistics, Oregon State University, Corvallis, Oregon

Search for other papers by Matthew S. Johnson in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Feb 2012
DOI:
https://doi.org/10.1175/MWR-D-11-00075.1
Page(s):
889–897
Restricted access

Abstract

A joint probabilistic forecasting framework is proposed for maximum wind speed, the probability of gust, and, conditional on gust being observed, the maximum gust speed in a setting where only the maximum wind speed forecast is available. The framework employs the nonhomogeneous Gaussian regression (NGR) statistical postprocessing method with appropriately truncated Gaussian predictive distributions. For wind speed, the distribution is truncated at zero, the location parameter is a linear function of the wind speed ensemble forecast, and the scale parameter is a linear function of the ensemble variance. The gust forecasts are derived from the wind speed forecast using a gust factor, and the predictive distribution for gust speed is truncated according to its definition. The framework is applied to 48-h-ahead forecasts of wind speed over the North American Pacific Northwest obtained from the University of Washington mesoscale ensemble. The resulting density forecasts for wind speed and gust speed are calibrated and sharp, and offer substantial improvement in predictive performance over the raw ensemble or climatological reference forecasts.

Corresponding author address: Thordis L. Thorarinsdottir, Institute of Applied Mathematics, Heidelberg University, Im Neuenheimer Feld 294, D-69120 Heidelberg, Germany. E-mail: thordis@uni-heidelberg.de

Abstract

A joint probabilistic forecasting framework is proposed for maximum wind speed, the probability of gust, and, conditional on gust being observed, the maximum gust speed in a setting where only the maximum wind speed forecast is available. The framework employs the nonhomogeneous Gaussian regression (NGR) statistical postprocessing method with appropriately truncated Gaussian predictive distributions. For wind speed, the distribution is truncated at zero, the location parameter is a linear function of the wind speed ensemble forecast, and the scale parameter is a linear function of the ensemble variance. The gust forecasts are derived from the wind speed forecast using a gust factor, and the predictive distribution for gust speed is truncated according to its definition. The framework is applied to 48-h-ahead forecasts of wind speed over the North American Pacific Northwest obtained from the University of Washington mesoscale ensemble. The resulting density forecasts for wind speed and gust speed are calibrated and sharp, and offer substantial improvement in predictive performance over the raw ensemble or climatological reference forecasts.

Corresponding author address: Thordis L. Thorarinsdottir, Institute of Applied Mathematics, Heidelberg University, Im Neuenheimer Feld 294, D-69120 Heidelberg, Germany. E-mail: thordis@uni-heidelberg.de
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Ágústsson, H., and H. Ólafsson, 2004: Mean gust factor in complex terrain. Meteor. Z., 13, 149–155.

  • Ágústsson, H., and H. Ólafsson, 2009: Forecasting wind gusts in complex terrain. Meteor. Atmos. Phys., 103, 173–185.

  • Anderson, J. L., 1996: A method for producing and evaluating probabilistic forecasts from ensemble model integrations. J. Climate, 9, 1518–1530.

    • Search Google Scholar
    • Export Citation

  • Baars, J., cited 2005: Observations QC summary page. [Available online at http://www.atmos.washington.edu/mm5rt/qc_obs/qc_obs_stats.html.]

    • Search Google Scholar
    • Export Citation

  • Bertsekas, D. P., 1999: Nonlinear Programming. 2nd ed. Athena Scientific, 802 pp.

  • Brasseur, O., 2001: Development and application of a physical approach to estimating wind gusts. Mon. Wea. Rev., 129, 5–25.

  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 1–3.

  • Dawid, A. P., 1984: Statistical theory: The prequential approach (with discussion and rejoinder). J. Roy. Stat. Soc., 147A, 278–292.

  • Durst, C. S., 1960: Wind speeds over short period of time. Meteor. Mag., 89, 181–187.

  • Eckel, A. F., and C. F. Mass, 2005: Aspects of effective mesoscale, short-range ensemble forecasting. Wea. Forecasting, 20, 328–350.

  • Etienne, C., A. Lehmann, S. Goyette, J. Lopez-Moreno, and M. Beniston, 2010: Spatial predictions of extreme wind speeds over Switzerland using generalized additive models. J. Appl. Meteor. Climatol., 49, 1956–1970.

    • Search Google Scholar
    • Export Citation

  • Friederichs, P., M. Göber, S. Bentzien, A. Lenz, and R. Krampitz, 2009: A probabilistic analysis of wind gusts using extreme value statistics. Meteor. Z., 18, 615–629.

    • Search Google Scholar
    • Export Citation

  • Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 1098–1118.

    • Search Google Scholar
    • Export Citation

  • Gneiting, T., F. Balabdaoui, and A. E. Raftery, 2007: Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc., 69B, 243–268.

    • Search Google Scholar
    • Export Citation

  • Goyette, S., O. Brasseur, and M. Beniston, 2003: Application of a new wind gust parameterization: Multiscale case studies performed with the Canadian regional climate model. J. Geophys. Res., 108, 4371–4389.

    • Search Google Scholar
    • Export Citation

  • Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta-RSM short-range ensemble forecasts. Mon. Wea. Rev., 125, 1312–1327.

  • He, Y., A. H. Monahan, C. G. Jones, A. Dai, S. Biner, D. Caya, and K. Winger, 2010: Probability distributions of land surface wind speeds over North America. J. Geophys. Res., 115, D04103, doi:10.1029/2008JD010708.

    • Search Google Scholar
    • Export Citation

  • Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559–570.

    • Search Google Scholar
    • Export Citation

  • Jungo, P., S. Goyette, and M. Beniston, 2002: Daily wind gust speed probabilities over Switzerland and according to three types of synoptic circulation. Int. J. Climatol., 22, 485–499.

    • Search Google Scholar
    • Export Citation

  • Kleiber, W., A. E. Raftery, J. Baars, T. Gneiting, C. Mass, and E. P. Grimit, 2011a: Locally calibrated probabilistic temperature forecasting using geostatistical model averaging and local Bayesian model averaging. Mon. Wea. Rev., 139, 2630–2649.

    • Search Google Scholar
    • Export Citation

  • Kleiber, W., A. E. Raftery, and T. Gneiting, 2011b: Geostatistical model averaging for locally calibrated probabilistic quantitative precipitation forecasting. J. Amer. Stat. Assoc., in press.

    • Search Google Scholar
    • Export Citation

  • Li, H., F. Liu, J. R. Hosking, and Y. Amemiya, 2010: Gust speed forecasting using weather model outputs and meteorological observations. IBM Research Rep. RC25087 (W1012-060), 18 pp.

    • Search Google Scholar
    • Export Citation

  • Matheson, J. E., and R. L. Winkler, 1976: Scoring rules for continuous probability distributions. Manage. Sci., 22, 1087–1096.

  • National Weather Service, cited 1998: Automated Surface Observing System (ASOS) user’s guide. [Available online at http://www.weather.gov/asos/aum-toc.pdf.]

    • Search Google Scholar
    • Export Citation

  • R Development Core Team, cited 2010: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. [Available online at http://www.R-project.org.]

    • Search Google Scholar
    • Export Citation

  • Sloughter, J. M., T. Gneiting, and A. E. Raftery, 2010: Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. J. Amer. Stat. Assoc., 105, 25–35.

    • Search Google Scholar
    • Export Citation

  • Thorarinsdottir, T. L., and T. Gneiting, 2010: Probabilistic forecasts of wind speed: Ensemble model output statistics using heteroskedastic censored regression. J. Roy. Stat. Soc., 173A, 371–388.

    • Search Google Scholar
    • Export Citation

  • Walshaw, D., and C. W. Anderson, 2000: A model for extreme wind gusts. J. Roy. Stat. Soc., 49C, 499–508.

  • Weggel, J. R., 1999: Maximum daily wind gusts related to mean daily wind speed. J. Struct. Eng., 125, 465–468.

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 648 pp.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 3001 1775 115
PDF Downloads 763 147 17