Articles | Volume 22, issue 3
https://doi.org/10.5194/hess-22-1957-2018
https://doi.org/10.5194/hess-22-1957-2018
Research article
 | 
23 Mar 2018
Research article |  | 23 Mar 2018

Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment

Sanjeev K. Jha, Durga L. Shrestha, Tricia A. Stadnyk, and Paulin Coulibaly

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Short summary
The output from numerical weather prediction (NWP) models is known to have errors. River forecast centers in Canada mostly use precipitation forecasts directly obtained from American and Canadian NWP models. In this study, we evaluate the forecast performance of ensembles generated by a Bayesian post-processing approach in cold climates. We demonstrate that the post-processing approach generates bias-free forecasts and provides a better picture of uncertainty in the case of an extreme event.
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