Articles | Volume 5, issue 2
18 Jul 2019
 | 18 Jul 2019

Bivariate Gaussian models for wind vectors in a distributional regression framework

Moritz N. Lang, Georg J. Mayr, Reto Stauffer, and Achim Zeileis

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Cited articles

Baran, S.: Probabilistic Wind Speed Forecasting Using Bayesian Model Averaging with Truncated Normal Components, Comput. Stat. Data An., 75, 227–238,, 2014. a
Baran, S. and Lerch, S.: Log-Normal Distribution Based Ensemble Model Output Statistics Models for Probabilistic Wind-Speed Forecasting, Q. J. Roy. Meteor. Soc., 141, 2289–2299,, 2015. a
Baran, S. and Lerch, S.: Mixture EMOS Model for Calibrating Ensemble Forecasts of Wind Speed, Environmetrics, 27, 116–130,, 2016. a
Buizza, R., Houtekamer, P. L., Pellerin, G., Toth, Z., Zhu, Y., and Wei, M.: A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems, Mon. Weather Rev., 133, 1076–1097,, 2005. a
Courtney, J. F., Lynch, P., and Sweeney, C.: High Resolution Forecasting for Wind Energy Applications Using Bayesian Model Averaging, Tellus A, 65, 19669,, 2013. a
Short summary
Accurate wind forecasts are of great importance for decision-making processes in today's society. This work presents a novel probabilistic post-processing method for wind vector forecasts employing a bivariate Gaussian response distribution. To capture a possible mismatch between the predicted and observed wind direction caused by location-specific properties, the approach incorporates a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.