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Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression
A novel way of statistically post-processing dynamical ensembles for wind speed by using heteroscedastic censored (tobit) regression, where location and spread derive from the ensemble, which results in a substantial improvement over the unprocessed ensemble or climatological reference forecasts.
Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling
- Roman Schefzik, T. Thorarinsdottir, T. Gneiting
- Environmental Science, Computer Science
- 28 February 2013
It is shown that seemingly unrelated, recent advances can be interpreted, fused and consolidated within the framework of ECC, the common thread being the adoption of the empirical copula of the raw ensemble.
Bayesian hierarchical modeling of extreme hourly precipitation in Norway
Spatial maps of extreme precipitation are a critical component of flood estimation in hydrological modeling, as well as in the planning and design of important infrastructure. This is particularly…
Ensemble Model Output Statistics for Wind Vectors
AbstractA bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic…
Forecaster's Dilemma: Extreme Events and Forecast Evaluation
Using theoretical arguments, simulation experiments, and a real data study on probabilistic forecasts of U.S. inflation and gross domestic product growth, the forecaster's dilemma is illustrated and the potential remedies discussed.
Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction
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,…
Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms
New methods for empirically assessing forecast calibration in a multivariate setting where the probabilistic forecast is given by an ensemble of equally probable forecast scenarios are proposed.
Multivariate probabilistic forecasting using ensemble Bayesian model averaging and copulas
This work proposes the use of a Gaussian copula, which offers a simple procedure for recovering the dependence that is lost in the estimation of the ensemble BMA marginals, and shows that it recovers many well‐understood dependencies between weather quantities and subsequently improves calibration and sharpness over both the raw ensemble and a method which does not incorporate joint distributional information.
Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression
AbstractStatistical postprocessing techniques are commonly used to improve the skill of ensembles from numerical weather forecasts. This paper considers spatial extensions of the well-established…
Using Proper Divergence Functions to Evaluate Climate Models
- T. Thorarinsdottir, T. Gneiting, Nadine Gissibl
- Computer ScienceSIAM/ASA J. Uncertain. Quantification
- 24 January 2013
The score divergences introduced in this paper derive from proper scoring rules and, thus, they are proper with the integrated quadratic distance and the Kullback--Leibler divergence being particularly attractive choices.