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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.
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ASCMO | Articles | Volume 5, issue 2
ASCMO | Articles | Volume 5, issue 2
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, 2019
https://doi.org/10.5194/ascmo-5-115-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, 2019
https://doi.org/10.5194/ascmo-5-115-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
ASCMO | 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 et al.

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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.
Read more
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Advances in Statistical Climatology, Meteorology and Oceanography

An international open-access journal on applied statistics

All site content, except where otherwise noted, is licensed under the Creative Commons Attribution 4.0 License.
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