Advances in Statistical Climatology, Meteorology and Oceanography
Advances in Statistical Climatology, Meteorology and Oceanography
ASCMO
Articles | Volume 5, issue 1
Articles | Volume 5, issue 1
https://doi.org/10.5194/ascmo-5-1-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-5-1-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 1
04 Feb 2019
 | 04 Feb 2019

NWP-based lightning prediction using flexible count data regression

Thorsten Simon, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis

Viewed

Total article views: 7,416 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
6,640 672 104 7,416 96 91
  • HTML: 6,640
  • PDF: 672
  • XML: 104
  • Total: 7,416
  • BibTeX: 96
  • EndNote: 91
Views and downloads (calculated since 04 Feb 2019)
Cumulative views and downloads (calculated since 04 Feb 2019)

Viewed (geographical distribution)

Total article views: 5,949 (including HTML, PDF, and XML) Thereof 5,770 with geography defined and 179 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Dec 2024
Download
Short summary
Lightning in Alpine regions is associated with events such as thunderstorms, extreme precipitation, high wind gusts, flash floods, and debris flows. We present a statistical approach to predict lightning counts based on numerical weather predictions. Lightning counts are considered on a grid with 18 km mesh size. Skilful prediction is obtained for a forecast horizon of 5 days over complex terrain.
Read more