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Consistent and continuous fields provided by precipitation analyses are valuable for hydrometeorological applications and land data assimilation modeling, among others. Providing uncertainty estimates is a logical step in the analysis development, and a consistent approach to reach this objective is the production of an ensemble analysis. In the present study, a 6-h High-Resolution Ensemble Precipitation Analysis (HREPA) was developed for the domain covering Canada and the northern part of the contiguous United States. The data assimilation system is the same as the Canadian Precipitation Analysis (CaPA) and is based on optimal interpolation (OI). Precipitation from the Canadian national 2.5-km atmospheric prediction system constitutes the background field of the analysis, while at-site records and radar quantitative precipitation estimates (QPE) compose the observation datasets. By using stochastic perturbations, multiple observations and background field random realizations were generated to subsequently feed the data assimilation system and provide 24 HREPA members plus one control run. Based on one summer and one winter experiment, HREPA capabilities in terms of bias and skill were verified against at-site observations for different climatic regions. The results indicated HREPA's reliability and skill for almost all types of precipitation events in winter, and for precipitation of medium intensity in summer. For both seasons, HREPA displayed resolution and sharpness. The overall good performance of HREPA and the lack of ensemble precipitation analysis (PA) at such spatiotemporal resolution in the literature motivate further investigations on transitional seasons and more advanced perturbation approaches. [ABSTRACT FROM AUTHOR]

From June 19 to June 22, 2013, intense rainfall and concurrent snowmelt led to devastating floods in the Canadian Rockies, foothills and downstream areas of southern Alberta and southeastern British Columbia. The complexity of the topography in the mountain headwaters, presence of snow at high elevations and other factors challenged hydrological forecasting of this extreme event. In this study, the ability of the Global Environmental Multi-scale hydrological modelling platform (GEM-Hydro), running at a 1-km grid spacing, to simulate hydrometeorological conditions in several Alberta rivers during this event is assessed. Four quantitative precipitation estimation (QPE) products were generated using the Canadian Precipitation Analysis (CaPA) system by varying (i) station density and (ii) horizontal resolutions (10, 2.5 and 1 km) of the GEM precipitation background. CaPA at 2.5 and 1 km including all available stations in the headwaters provided the most accurate estimation of intensity and total amount of precipitation during the flooding event. Using these products to drive GEM-Hydro, it is shown that QPE accuracy dominates the ability to predict flood volumes. Initial snow conditions also represent a large additional source of uncertainty. Default GEM-Hydro simulations starting with almost no snowpack at high-elevations led to a systematic underestimation of flood volume and peak flow. Gridded estimates of snow water equivalent from the Snow Data Assimilation System (SNODAS) were also considered. They led to contrasting abilities to simulate flood discharge volumes and a consistent overestimation in the headwater catchments, illustrating the strong need for a reference snow product in the mountains of Western Canada. Finally, GEM-Hydro did not predict peak flow timing and hydrograph shape well. Model sensitivity tests show that it could be improved by adjusting the Manning coefficients, suggesting the need to revisit the routing parameters. There may be a need to include water management effects on flood hydrographs as well. These results will guide the development of GEM-Hydro as a hydrological forecasting system in Western Canada. [ABSTRACT FROM AUTHOR]