Articles | Volume 29, issue 4
https://doi.org/10.5194/hess-29-1135-2025
https://doi.org/10.5194/hess-29-1135-2025
Research article
 | 
28 Feb 2025
Research article |  | 28 Feb 2025

Leveraging a radar-based disdrometer network to develop a probabilistic precipitation phase model in eastern Canada

Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau

Viewed

Total article views: 791 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
460 195 136 791 23 30
  • HTML: 460
  • PDF: 195
  • XML: 136
  • Total: 791
  • BibTeX: 23
  • EndNote: 30
Views and downloads (calculated since 29 Apr 2024)
Cumulative views and downloads (calculated since 29 Apr 2024)

Viewed (geographical distribution)

Total article views: 791 (including HTML, PDF, and XML) Thereof 786 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 03 Mar 2025
Download
Short summary
Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.
Share