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

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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.
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