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September 2015 Probabilistic wind speed forecasting on a grid based on ensemble model output statistics
Michael Scheuerer, David Möller
Ann. Appl. Stat. 9(3): 1328-1349 (September 2015). DOI: 10.1214/15-AOAS843

Abstract

Probabilistic forecasts of wind speed are important for a wide range of applications, ranging from operational decision making in connection with wind power generation to storm warnings, ship routing and aviation. We present a statistical method that provides locally calibrated, probabilistic wind speed forecasts at any desired place within the forecast domain based on the output of a numerical weather prediction (NWP) model. Three approaches for wind speed post-processing are proposed, which use either truncated normal, gamma or truncated logistic distributions to make probabilistic predictions about future observations conditional on the forecasts of an ensemble prediction system (EPS). In order to provide probabilistic forecasts on a grid, predictive distributions that were calibrated with local wind speed observations need to be interpolated. We study several interpolation schemes that combine geostatistical methods with local information on annual mean wind speeds, and evaluate the proposed methodology with surface wind speed forecasts over Germany from the COSMO-DE (Consortium for Small-scale Modelling) ensemble prediction system.

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Michael Scheuerer. David Möller. "Probabilistic wind speed forecasting on a grid based on ensemble model output statistics." Ann. Appl. Stat. 9 (3) 1328 - 1349, September 2015. https://doi.org/10.1214/15-AOAS843

Information

Received: 1 February 2014; Revised: 1 May 2015; Published: September 2015
First available in Project Euclid: 2 November 2015

zbMATH: 06525988
MathSciNet: MR3418725
Digital Object Identifier: 10.1214/15-AOAS843

Keywords: continuous ranked probability score , density forecast , ensemble prediction system , Gaussian process , numerical weather prediction

Rights: Copyright © 2015 Institute of Mathematical Statistics

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Vol.9 • No. 3 • September 2015
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