Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-331
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
07 Aug 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
Evaluation of ensemble precipitation forecasts generated through postprocessing in a Canadian catchment
Sanjeev K. Jha1, Durga Lal Shrestha2, Tricia Stadnyk1, and Paulin Coulibaly3 1Department of Civil Engineering, University of Manitoba, Winnipeg, R3T 5V6, Canada
2Commonwealth Science and Industrial Research Organization, Clayton South Victoria, 3169, Australia
3Department of Civil Engineering, McMaster University, Hamilton, L8S 4L7, Canada
Abstract. Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effect over diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall-post processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global ensemble forecasting system (GEFS) Reforecast 2 project from National Centers for Environmental Protection (NCEP), and Global deterministic forecast system (GDPS) from Environment and Climate Change Canada (ECCC) are used in this study. The study period from Jan 2013 to Dec 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias, and reduce the continuous ranked probability score of both GEFS and GDPS forecasts. Ensembles generated from the RPP better depict the forecast uncertainty.

Citation: Jha, S. K., Shrestha, D. L., Stadnyk, T., and Coulibaly, P.: Evaluation of ensemble precipitation forecasts generated through postprocessing in a Canadian catchment, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-331, in review, 2017.
Sanjeev K. Jha et al.
Sanjeev K. Jha et al.
Sanjeev K. Jha et al.

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Short summary
The output from a Numerical Weather Prediction model (NWP) is known to have errors. The River Forecast Centres 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 also provides better picture of uncertainty in case of an extreme event.
The output from a Numerical Weather Prediction model (NWP) is known to have errors. The River...
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