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March 2018 Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs
Julie Bessac, Emil Constantinescu, Mihai Anitescu
Ann. Appl. Stat. 12(1): 432-458 (March 2018). DOI: 10.1214/17-AOAS1099

Abstract

We propose a statistical space–time model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate space–time framework that combines multiple sources of past physical model outputs and measurements in order to produce a probabilistic wind speed forecast within the prediction window. We illustrate this strategy on wind speed forecasts during several months in 2012 for a region near the Great Lakes in the United States. The results show that the prediction is improved in the mean-squared sense relative to the numerical forecasts as well as in probabilistic scores. Moreover, the samples are shown to produce realistic wind scenarios based on sample spectra and space–time correlation structure.

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Julie Bessac. Emil Constantinescu. Mihai Anitescu. "Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs." Ann. Appl. Stat. 12 (1) 432 - 458, March 2018. https://doi.org/10.1214/17-AOAS1099

Information

Received: 1 October 2016; Revised: 1 September 2017; Published: March 2018
First available in Project Euclid: 9 March 2018

zbMATH: 06894713
MathSciNet: MR3773400
Digital Object Identifier: 10.1214/17-AOAS1099

Keywords: Hierarchical Gaussian model , multiple data sources , predictive scenarios , spatio-temporal wind speed

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 1 • March 2018
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