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Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression

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  • Thordis L. Thorarinsdottir
  • Tilmann Gneiting
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    Abstract

    As wind energy penetration continues to grow, there is a critical need for probabilistic forecasts of wind resources. In addition, there are many other societally relevant uses for forecasts of wind speed, ranging from aviation to ship routing and recreational boating. Over the past two decades, ensembles of dynamical weather prediction models have been developed, in which multiple estimates of the current state of the atmosphere are used to generate a collection of deterministic forecasts. However, even state of the art ensemble systems are uncalibrated and biased. Here we propose a novel way of statistically post-processing dynamical ensembles for wind speed by using heteroscedastic censored (tobit) regression, where location and spread derive from the ensemble. The resulting ensemble model output statistics method is applied to 48-h-ahead forecasts of maximum wind speed over the North American Pacific Northwest by using the University of Washington mesoscale ensemble. The statistically post-processed density forecasts turn out to be calibrated and sharp, and result in a substantial improvement over the unprocessed ensemble or climatological reference forecasts. Copyright (c) 2009 Royal Statistical Society.

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    Bibliographic Info

    Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).

    Volume (Year): 173 (2010)
    Issue (Month): 2 ()
    Pages: 371-388

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    Handle: RePEc:bla:jorssa:v:173:y:2010:i:2:p:371-388

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    Cited by:
    1. Jakob W. Messner & Achim Zeileis & Jochen Broecker & Georg J. Mayr, 2013. "Improved Probabilistic Wind Power Forecasts with an Inverse Power Curve Transformation and Censored Regression," Working Papers, Faculty of Economics and Statistics, University of Innsbruck 2013-01, Faculty of Economics and Statistics, University of Innsbruck.
    2. Jakob W. Messner & Georg J. Mayr & Daniel S. Wilks & Achim Zeileis, 2013. "Extending Extended Logistic Regression for Ensemble Post-Processing: Extended vs. Separate vs. Ordered vs. Censored," Working Papers, Faculty of Economics and Statistics, University of Innsbruck 2013-32, Faculty of Economics and Statistics, University of Innsbruck.
    3. Baran, Sándor, 2014. "Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 75(C), pages 227-238.
    4. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, Open Access Journal, vol. 5(3), pages 621-657, March.

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