The Annals of Applied Statistics

Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs

Julie Bessac, Emil Constantinescu, and Mihai Anitescu

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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.

Article information

Source
Ann. Appl. Stat. Volume 12, Number 1 (2018), 432-458.

Dates
Received: October 2016
Revised: September 2017
First available in Project Euclid: 9 March 2018

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1520564479

Digital Object Identifier
doi:10.1214/17-AOAS1099

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

Citation

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai. Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs. Ann. Appl. Stat. 12 (2018), no. 1, 432--458. doi:10.1214/17-AOAS1099. https://projecteuclid.org/euclid.aoas/1520564479


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