Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs
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:
-
- 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:
- 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.
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
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.
@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 = {2018},
month = {3}
}
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