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

Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai

doi:10.1214/17-AOAS1099

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

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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.

Authors:

Bessac, Julie ^[1]; Constantinescu, Emil ^[2]; Anitescu, Mihai ^[2]

Argonne National Lab. (ANL), Argonne, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)

Publication Date:: Thu Mar 01 00:00:00 EST 2018

Research Org.:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1439836

Grant/Contract Number:: AC02-06CH11357

Resource Type:: Accepted Manuscript

Journal Name:: The Annals of Applied Statistics

Additional Journal Information:: Journal Volume: 12; Journal Issue: 1; Journal ID: ISSN 1932-6157

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:10.1214/17-AOAS1099.

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                    Bessac, Julie, Constantinescu, Emil, & Anitescu, Mihai. Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs.  United States.  https://doi.org/10.1214/17-AOAS1099

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                    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.  https://doi.org/10.1214/17-AOAS1099.  https://www.osti.gov/servlets/purl/1439836.

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@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 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.},

  doi          = {10.1214/17-AOAS1099},

  journal      = {The Annals of Applied Statistics},

  number       = 1,

  volume       = 12,

  place        = {United States},

  year         = {Thu Mar 01 00:00:00 EST 2018},

  month        = {Thu Mar 01 00:00:00 EST 2018}

}

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