Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 24, issue 8
Articles | Volume 24, issue 8
https://doi.org/10.5194/nhess-24-2793-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-2793-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 8
Research article
 | 
22 Aug 2024
Research article |  | 22 Aug 2024

Probabilistic short-range forecasts of high-precipitation events: optimal decision thresholds and predictability limits

François Bouttier and Hugo Marchal

Viewed

Total article views: 655 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
444 174 37 655 23 19
  • HTML: 444
  • PDF: 174
  • XML: 37
  • Total: 655
  • BibTeX: 23
  • EndNote: 19
Views and downloads (calculated since 16 Jan 2024)
Cumulative views and downloads (calculated since 16 Jan 2024)

Viewed (geographical distribution)

Total article views: 655 (including HTML, PDF, and XML) Thereof 646 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 31 Aug 2024
Download
Short summary
Weather prediction uncertainties can be described as sets of possible scenarios – a technique called ensemble prediction. Our machine learning technique translates them into more easily interpretable scenarios for various users, balancing the detection of high precipitation with false alarms. Key parameters are precipitation intensity and space and time scales of interest. We show that the approach can be used to facilitate warnings of extreme precipitation.
Read more
Altmetrics
Final-revised paper
Preprint