Ensemble Model Output Statistics for Wind Vectors

By Nina Schuhen, Thordis Linda Thorarinsdottir, Tilmann Gneiting. 2012

85 citations recorded by the source corpus.

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 forecast takes the form of a bivariate normal probability density function. The postprocessed means and variances of the wind vector components are linearly bias-corrected versions of the ensemble means and ensemble variances, respectively, and the conditional correlation between the wind components is represented by a trigonometric function of the ensemble mean wind direction. In a case study on 48-h forecasts of wind vectors over the North American Pacific Northwest with the University of Washington Mesoscale Ensemble, the bivariate EMOS density forecasts were calibrated and sharp, and showed considerable improvement over the raw ensemble and reference forecasts, including ensemble copula coupling.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/165f05497d2f2f4acd1744051577f6a2511525f0

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Citations in the corpus

Multivariate—Intervariable, Spatial, and Temporal—Bias Correction* (2015, methodology): “The ensemble copula coupling (ECC) is an adaptation thereof to multivariate ensemble post-processing (Schefzik et al. 2013; M öller et al. 2012 ; Schuhen et al. 2012; Thorarinsdottir et al. 2015).”
Comparison of non-homogeneous regression models for probabilistic wind speed forecasting (2013, methodology): “ while Thorarinsdottir and Johnson (2012) apply the same set-up to predict gust speeds based on NWP forecasts of wind speed and gust factors. A bivariate normal model for wind vectors is discussed in Schuhen et al. (2012). An NR framework for quantitative precipitation has recently been proposed by ”
Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics (2013, methodology): “, 2005), wind speed (Thorarinsdottir and Gneiting, 2010), wind vectors (Schuhen et al., 2012) and wind gusts (Thorarinsdottir and Johnson, 2012).”
Assessing the Calibration of High-Dimensional Ensemble Forecasts Using Rank Histograms (2013, background): “While the multivariate rank histogram has been shown to work well for low-dimensional forecasts, see e.g. Schuhen et al. (2012) and M ¨ oller et al. (2013), the multivariate ordering in the ﬁrst step seems to lack power in higher dimensions (Pinson and Girard, 2012).”
Discrimination ability of the Energy score (2013, background): “, [16], appear to report results that go significantly beyond this theoretical upper bound, for reasons we cannot explain.”