Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs
Abstract
We propose a statistical spacetime model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate spacetime 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 meansquared 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 spacetime correlation structure.
 Authors:

 Argonne National Lab. (ANL), Argonne, IL (United States)
 Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
 Publication Date:
 Research Org.:
 Argonne National Lab. (ANL), Argonne, IL (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC)
 OSTI Identifier:
 1439836
 Grant/Contract Number:
 AC0206CH11357
 Resource Type:
 Accepted Manuscript
 Journal Name:
 The Annals of Applied Statistics
 Additional Journal Information:
 Journal Volume: 12; Journal Issue: 1; Journal ID: ISSN 19326157
 Publisher:
 Institute of Mathematical Statistics
 Country of Publication:
 United States
 Language:
 English
 Subject:
 54 ENVIRONMENTAL SCIENCES
Citation Formats
Bessac, Julie, Constantinescu, Emil, and Anitescu, Mihai. Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs. United States: N. p., 2018.
Web. doi:10.1214/17AOAS1099.
Bessac, Julie, Constantinescu, Emil, & Anitescu, Mihai. Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs. United States. doi:10.1214/17AOAS1099.
Bessac, Julie, Constantinescu, Emil, and Anitescu, Mihai. Thu .
"Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs". United States. doi:10.1214/17AOAS1099. https://www.osti.gov/servlets/purl/1439836.
@article{osti_1439836,
title = {Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs},
author = {Bessac, Julie and Constantinescu, Emil and Anitescu, Mihai},
abstractNote = {We propose a statistical spacetime model for predicting atmospheric wind speed based on deterministic numerical weather predictions and historical measurements. We consider a Gaussian multivariate spacetime 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 meansquared 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 spacetime correlation structure.},
doi = {10.1214/17AOAS1099},
journal = {The Annals of Applied Statistics},
number = 1,
volume = 12,
place = {United States},
year = {2018},
month = {3}
}
Web of Science