Stochastic simulation of predictive space–time scenarios of wind speed using observations and physical model outputs
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
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.
- Research Organization:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1439836
- Journal Information:
- The Annals of Applied Statistics, Vol. 12, Issue 1; ISSN 1932-6157
- Publisher:
- Institute of Mathematical StatisticsCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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