STOCHASTIC SIMULATION OF PREDICTIVE SPACE-TIME SCENARIOS OF WIND SPEED USING OBSERVATIONS AND PHYSICAL MODEL OUTPUTS

• 成果类型: Article
• 署名作者: Bessac, Julie; Constantinescu, Emil; Anitescu, Mihai
• 署名单位: United States Department of Energy (DOE); Argonne National Laboratory; University of Chicago; University of Chicago
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/17-AOAS1099
• 发表日期: 2018
• 页码: 432-458
• 关键词: cross-covariance functions; probabilistic forecasts; ensemble forecasts; Uncertainty Quantification; statistics

摘要

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.