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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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HESS | Articles | Volume 22, issue 3
Hydrol. Earth Syst. Sci., 22, 1957–1969, 2018
https://doi.org/10.5194/hess-22-1957-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 22, 1957–1969, 2018
https://doi.org/10.5194/hess-22-1957-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 et al.

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Cited articles

Abaza, M., Anctil, F., Fortin, V., and Turcotte, R.: A comparison of the Canadian global and regional meteorological ensemble prediction systems for short-term hydrological forecasting, Mon. Weather Rev., 141, 3462–3476, 2013. 
AEP (Alberta Environment and Parks): https://rivers.alberta.ca/, last access: February 2017. 
Ahmed, S., Coulibaly, P., and Tsanis, I.: Improved Spring Peak-Flow Forecasting Using Ensemble Meteorological Predictions, J. Hydrol. Eng., 20, 04014044, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001014, 2014. 
AMEC: Weather Forecast Review Project for Operational Open-water River Forecasting, available at: http://aep.alberta.ca/water/programs-and-services/flood-mitigation/flood-mitigation-studies.aspx (last access: February 2017), 2014. 
Atger, F.: Estimation of the reliability of ensemble-based probabilistic forecasts, Q. J. Roy. Meteor. Soc., 130, 627–646, 2004. 
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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.
The output from numerical weather prediction (NWP) models is known to have errors. River...
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