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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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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. Jha1,2, Durga L. Shrestha3, Tricia A. Stadnyk1, and Paulin Coulibaly4 Sanjeev K. Jha et al.
  • 1Department of Civil Engineering, University of Manitoba, Winnipeg, R3T 5V6, Canada
  • 2Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Bhopal, Madhya Pradesh, 462066, India
  • 3Commonwealth Science and Industrial Research Organization, Clayton South Victoria, 3169, Australia
  • 4Department 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 effects over a 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 the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 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 errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.

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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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