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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. [ABSTRACT FROM AUTHOR]

This manuscript presents a simple yet effective method to account for uncertainty in hydrologic ensemble forecasting applications. Most operational hydrological ensemble forecasting systems only account for uncertainty in future climate (e.g. precipitation) forcings, ignoring other sources of uncertainty (e.g. model error). The result can be under-dispersive and overconfident forecasts. Ensemble dressing is a form of statistical post-processing to include information about the uncertainty of individual ensemble members. First, the historical simulations can be corrected to remove systematic biases. Next, the simulated and observed flow data are transformed to ensure that model residuals can be fitted to a distribution (e.g. normal); this study uses a 2-parameter log-sinh transformation. Model residuals in transformed space determine the width of the error distribution. Ensemble forecasts are then generated and transformed. Each ensemble is dressed with the error distribution, the results are untransformed, and finally, a probabilistic forecast is derived from the collective distribution of the dressed ensembles. The method is applied to 128 catchments in southeast Australia, demonstrating that the raw ensemble can be made reliable through the use of this dressing method. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]