Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method

Gneiting, Tilmann; Larson, Kristin; Westrick, Kenneth J.; Genton, Marc G.; Aldrich, Eric M.

doi:10.21236/ada459831

Cited by 68 publications

(153 citation statements)

References 33 publications

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“…Gneiting et al (2004) proposed a novel spatiotemporal approach, the regime-switching space-time (RST) method, that merges meteorological and statistical expertise to obtain fully probabilistic forecasts of wind resources. Henceforth, we illustrate our diagnostic approach to the evaluation of predictive performance by a comparison and ranking of three competing methodologies for 2-hour-ahead forecasts of hourly average wind speed at the Stateline wind energy centre.…”

Section: Case-study: Probabilistic Forecasts At the Stateline Wind Enmentioning

confidence: 99%

“…The predictive distribution assigns a typically small positive mass to the negative half-axis, and, in view of the non-negativity of the predictand, we redistribute this mass to wind speed 0. The details are described in Gneiting et al (2004), where the method is referred to as the AR-D technique. The third method is the RST approach of Gneiting et al (2004).…”

Section: Predictive Distributions For Hourly Average Wind Speedmentioning

confidence: 99%

“…The details are described in Gneiting et al (2004), where the method is referred to as the AR-D technique. The third method is the RST approach of Gneiting et al (2004). The RST model is parsimonious yet takes account of all the salient features of wind speed: alternating atmospheric regimes, temporal and spatial autocorrelation, diurnal and seasonal non-stationarity, conditional heteroscedasticity and non-Gaussianity.…”

Section: Predictive Distributions For Hourly Average Wind Speedmentioning

confidence: 99%

“…The method uses off-site information from the nearby meteorological towers at Goodnoe Hills and Kennewick, identifies atmospheric regimes and fits conditional predictive models for each regime, based on a sliding 45-day training period. Details can be found in Gneiting et al (2004), where the method is referred to as the RST-D-CH technique. Any minor discrepancies in the results that are reported below and in Gneiting et al (2004) stem from the use of R rather than S-PLUS and differences in optimization routines.…”

Section: Predictive Distributions For Hourly Average Wind Speedmentioning

confidence: 99%

“…Details can be found in Gneiting et al (2004), where the method is referred to as the RST-D-CH technique. Any minor discrepancies in the results that are reported below and in Gneiting et al (2004) stem from the use of R rather than S-PLUS and differences in optimization routines.…”

Section: Predictive Distributions For Hourly Average Wind Speedmentioning

confidence: 99%

See 4 more Smart Citations

Probabilistic Forecasts, Calibration and Sharpness

Gneiting¹,

Balabdaoui²,

Raftery³

2005

Self Cite

129

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Summary. Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the predictions and the events that materialize. Sharpness refers to the concentration of the predictive distributions and is a property of the forecasts only. A simple theoretical framework allows us to distinguish between probabilistic calibration, exceedance calibration and marginal calibration. We propose and study tools for checking calibration and sharpness, among them the probability integral transform histogram, marginal calibration plots, the sharpness diagram and proper scoring rules. The diagnostic approach is illustrated by an assessment and ranking of probabilistic forecasts of wind speed at the Stateline wind energy centre in the US Pacific Northwest. In combination with cross-validation or in the time series context, our proposal provides very general, nonparametric alternatives to the use of information criteria for model diagnostics and model selection.

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Section: Case-study: Probabilistic Forecasts At the Stateline Wind Enmentioning

confidence: 99%

Section: Predictive Distributions For Hourly Average Wind Speedmentioning

confidence: 99%

Section: Predictive Distributions For Hourly Average Wind Speedmentioning

confidence: 99%

Section: Predictive Distributions For Hourly Average Wind Speedmentioning

confidence: 99%

Section: Predictive Distributions For Hourly Average Wind Speedmentioning

confidence: 99%

See 3 more Smart Citations

Probabilistic Forecasts, Calibration and Sharpness

Gneiting¹,

Balabdaoui²,

Raftery³

2005

Self Cite

129

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Multivariate postprocessing techniques for probabilistic hydrological forecasting

2015

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Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both location and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield wellcalibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. The domain of this study covers three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. In this study, the two approaches to model the temporal dependence structure are ensemble copula coupling (ECC), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA), which estimates the temporal correlations from training observations. The results indicate that both methods are suitable for modeling the temporal dependencies of probabilistic hydrologic forecasts.

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Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction

Friederichs

Thorarinsdottir

2012

Environmetrics

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Predictions of the uncertainty associated with extreme events are a vital component of any prediction system for such events. Consequently, the prediction system ought to be probabilistic in nature, with the predictions taking the form of probability distributions. This paper concerns probabilistic prediction systems where the data are assumed to follow either a generalized extreme value (GEV) distribution or a generalized Pareto distribution. In this setting, the properties of proper scoring rules that facilitate the assessment of the prediction uncertainty are investigated, and closed form expressions for the continuous ranked probability score (CRPS) are provided. In an application to peak wind prediction, the predictive performance of a GEV model under maximum likelihood estimation, optimum score estimation with the CRPS, and a Bayesian framework are compared. The Bayesian inference yields the highest overall prediction skill and is shown to be a valuable tool for covariate selection, while the predictions obtained under optimum CRPS estimation are the sharpest and give the best performance for high thresholds and quantiles. Copyright © 2012 John Wiley & Sons, Ltd.

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Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching Space-Time (RST) Method

Cited by 68 publications

References 33 publications

Probabilistic Forecasts, Calibration and Sharpness

Probabilistic Forecasts, Calibration and Sharpness

Multivariate postprocessing techniques for probabilistic hydrological forecasting

Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction

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