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Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
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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.
ASCMO | Articles | Volume 5, issue 2
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, 2019
https://doi.org/10.5194/ascmo-5-115-2019
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, 2019
https://doi.org/10.5194/ascmo-5-115-2019

  18 Jul 2019

18 Jul 2019

Bivariate Gaussian models for wind vectors in a distributional regression framework

Moritz N. Lang et al.

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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, https://doi.org/10.1016/j.csda.2014.02.013, 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, https://doi.org/10.1002/qj.2521, 2015. a
Baran, S. and Lerch, S.: Mixture EMOS Model for Calibrating Ensemble Forecasts of Wind Speed, Environmetrics, 27, 116–130, https://doi.org/10.1002/env.2380, 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, https://doi.org/10.1175/MWR2905.1, 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, https://doi.org/10.3402/tellusa.v65i0.19669, 2013. a
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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.
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