Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 5, issue 2
Articles | Volume 5, issue 2
https://doi.org/10.5194/ascmo-5-115-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-5-115-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 4,325 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,605 612 108 4,325 134 115
  • HTML: 3,605
  • PDF: 612
  • XML: 108
  • Total: 4,325
  • BibTeX: 134
  • EndNote: 115
Views and downloads (calculated since 18 Jul 2019)
Cumulative views and downloads (calculated since 18 Jul 2019)

Viewed (geographical distribution)

Total article views: 3,580 (including HTML, PDF, and XML) Thereof 3,526 with geography defined and 54 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 03 Jul 2025
Download
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
Share