Editorial Type:
Article
Article Type:
Research Article

Modeling the Variability of Sydney Harbor Wind Measurements

Edward Cripps School of Mathematics, University of New South Wales, Sydney, New South Wales, Australia

Search for other papers by Edward Cripps in
Current site
Google Scholar
PubMed Close
and
William T. M. Dunsmuir School of Mathematics, University of New South Wales, Sydney, New South Wales, Australia

Search for other papers by William T. M. Dunsmuir in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Aug 2003
DOI:
https://doi.org/10.1175/1520-0450(2003)042<1131:MTVOSH>2.0.CO;2
Page(s):
1131–1138
Restricted access

Abstract

The time-dependent behavior in the variability of wind measurements is investigated using bivariate generalized autoregressive conditional heteroscedastic models. These models express the current level of short-timescale wind variability in terms of previous observed values of the fluctuations from mean wind fields. As such, these models provide a useful descriptive model that can be applied to short-term forecasting of variability of wind fluctuations around local mean levels.

Corresponding author address: William T. M. Dunsmuir, Department of Statistics, School of Mathematics, Red Centre Rm. 2057, University of New South Wales, Sydney, NSW 2052, Australia. w.dunsmuir@unsw.edu.au

Abstract

The time-dependent behavior in the variability of wind measurements is investigated using bivariate generalized autoregressive conditional heteroscedastic models. These models express the current level of short-timescale wind variability in terms of previous observed values of the fluctuations from mean wind fields. As such, these models provide a useful descriptive model that can be applied to short-term forecasting of variability of wind fluctuations around local mean levels.

Corresponding author address: William T. M. Dunsmuir, Department of Statistics, School of Mathematics, Red Centre Rm. 2057, University of New South Wales, Sydney, NSW 2052, Australia. w.dunsmuir@unsw.edu.au

Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Bollerslev, T. 1986. Generalized autoregressive conditional heteroscedasticity. J. Econ. 31:307–327.

  • Breckling, J. 1989. The Analysis of Directional Time Series. Springer-Verlag, 238 pp.

  • Brockwell, P. J. and R. A. Davis. 1991. Time Series: Theory and Methods. 2d ed. Spinger-Verlag, 577 pp.

  • Brockwell, P. J. and R. A. Davis. 2002. Introduction to Time Series and Forecasting. 2d ed. Springer-Verlag, 434 pp.

  • Engle, R. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1008.

    • Search Google Scholar
    • Export Citation

  • Gouriéroux, G. 1997. ARCH Models and Financial Applications. Springer-Verlag, 228 pp.

  • Hafner, C. 1998. Nonlinear Time Series with Applications to Foreign Exchange Rate Volatility. Physica-Verlag, 222 pp.

  • Rice, J. A. 1995. Mathematical Statistics and Data Analysis. 2d ed. Duxbury Press, 602 pp.

  • S+GARCH, 2000. S+GARCH Users Manual. [Available from 1700 Westlake Avenue, North Suite 500, Seattle, WA 98109-3044, or online at http://www.insightful.com.].

    • Search Google Scholar
    • Export Citation

  • Spark, E., W. T. M. Dunsmuir, and S. K. Kim. 2000. Wind forecasts for the 2000 Olympic Games in Sydney with a focus on Sydney Harbour. Preprints, 15th Conf. on Probability and Statistics in the Atmospheric Sciences, Asheville, NC, Amer. Meteor. Soc., 36–39.

    • Search Google Scholar
    • Export Citation

  • Tol, R. S. 1996. Autoregressive conditional heteroscedasticity in daily temperature measurements. Envirometrics 7:67–75.

  • Tol, R. S. 1997. Autoregressive conditional heteroscedasticity in daily wind speed measurements. Theor. Appl. Climatol. 56:113–122.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 514 226 98
PDF Downloads 118 24 0