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Several data assimilation (DA) approaches exist to generate consistent and continuous precipitation fields valuable for hydrometeorological applications and land data assimilation. Usually, DA is based on either static or dynamic approaches. Static methods rely on deterministic forecasts to estimate background error covariance matrices, whereas dynamic approaches use ensemble forecasts. Associating the two methods is known as hybrid DA, and it has proven beneficial for different applications as it combines the advantages of both approaches. The present study intends to explore hybrid DA for the 6 h Canadian Precipitation Analysis (CaPA). Based on optimal interpolation (OI), CaPA blends forecasts and observations from surface stations and ground-based radar datasets to provide precipitation fields over the North American domain. The application of hybrid DA to CaPA consisted of finding the optimal linear combination between (i) an OI based on the Regional Deterministic Prediction System (RDPS) and (ii) an ensemble Kalman filter (EnKF) based on the 20-member Regional Ensemble Prediction System (REPS). The results confirmed the known effectiveness of the hybrid approach when low-density observation networks are assimilated. Indeed, the experiments conducted for the summer without radar datasets and for the winter (characterized by very few observations in CaPA) showed that attributing a relatively high weight to the EnKF (50 % and 70 % for summer and winter, respectively) resulted in better analysis skill and a reduction in false alarms compared with the OI method. A deterioration in the moderate- to high-intensity precipitation bias was, however, observed during summer. Reducing the weight attributed to the EnKF to 30 % alleviated the bias deterioration while improving skill compared with the OI-based CaPA. [ABSTRACT FROM AUTHOR]

Environment and Climate Change Canada has initiated the production of a 1980–2018, 10 km, North American precipitation and surface reanalysis. ERA-Interim is used to initialize the Global Deterministic Reforecast System (GDRS) at a 39 km resolution. Its output is then dynamically downscaled to 10 km by the Regional Deterministic Reforecast System (RDRS). Coupled with the RDRS, the Canadian Land Data Assimilation System (CaLDAS) and Precipitation Analysis (CaPA) are used to produce surface and precipitation analyses. All systems used are close to operational model versions and configurations. In this study, a 7-year sample of the reanalysis (2011–2017) is evaluated. Verification results show that the skill of the RDRS is stable over time and equivalent to that of the current operational system. The impact of the coupling between RDRS and CaLDAS is explored using an early version of the reanalysis system which was run at 15 km resolution for the period 2010–2014, with and without the use of CaLDAS. Significant improvements are observed with CaLDAS in the lower troposphere and surface layer, especially for the 850 hPa dew point and absolute temperatures in summer. Precipitation is further improved through an offline precipitation analysis which allows the assimilation of additional observations of 24 h precipitation totals. The final dataset should be of particular interest for hydrological applications focusing on transboundary and northern watersheds, where existing products often show discontinuities at the border and assimilate very few – if any – precipitation observations. [ABSTRACT FROM AUTHOR]

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 19 to 22 June 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, Canada. Such an event is typical of late-spring floods in cold-region mountain headwater, combining intense precipitation with rapid melting of late-lying snowpack, and represents a challenge for hydrological forecasting systems. This study investigated the factors governing the ability to predict such an event. Three sources of uncertainty, other than the hydrological model processes and parameters, were considered: (i) the resolution of the atmospheric forcings, (ii) the snow and soil moisture initial conditions (ICs) and (iii) the representation of the soil texture. The Global Environmental Multiscale hydrological modeling platform (GEM-Hydro), running at a 1 km grid spacing, was used to simulate hydrometeorological conditions in the main headwater basins of southern Alberta during this event. The GEM atmospheric model and the Canadian Precipitation Analysis (CaPA) system were combined to generate atmospheric forcing at 10, 2.5 and 1 km over southern Alberta. Gridded estimates of snow water equivalent (SWE) from the Snow Data Assimilation System (SNODAS) were used to replace the model SWE at peak snow accumulation and generate alternative snow and soil moisture ICs before the event. Two global soil texture datasets were also used. Overall 12 simulations of the flooding event were carried out. Results show that the resolution of the atmospheric forcing affected primarily the flood volume and peak flow in all river basins due to a more accurate estimation of intensity and total amount of precipitation during the flooding event provided by CaPA analysis at convection-permitting scales (2.5 and 1 km). Basin-averaged snowmelt also changed with the resolution due to changes in near-surface wind and resulting turbulent fluxes contributing to snowmelt. Snow ICs were the main sources of uncertainty for half of the headwater basins. Finally, the soil texture had less impact and only affected peak flow magnitude and timing for some stations. These results highlight the need to combine atmospheric forcing at convection-permitting scales with high-quality snow ICs to provide accurate streamflow predictions during late-spring floods in cold-region mountain river basins. The predictive improvement by inclusion of high-elevation weather stations in the precipitation analysis and the need for accurate mountain snow information suggest the necessity of integrated observation and prediction systems for forecasting extreme events in mountain river basins. [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]

The Fire Weather Index (FWI) System is the basic method of fire danger rating for fire management agencies in Canada. The spatial interpolation of this weather-based system can be difficult in areas where weather stations are sparse. Previous studies have shown that interpolation of precipitation is especially problematic owing to the isolated nature of convective summer storms. Environment and Climate Change Canada (ECCC) has recently developed the Canadian Precipitation Analysis (CaPA), a gridded precipitation product that integrates forecast data, station observations and Doppler radar, to improve precipitation estimates. The objective of the present study was to evaluate whether CaPA is indeed superior to the currently used interpolation method (thin-plate spline), and to assess the impact of the two precipitation estimates on the FWI System for the province of Ontario. Overall, the results showed the CaPA had increased skill in areas of radar coverage. In non-radar areas, where the ECCC station network was much sparser, there were less significant differences between the methods. Precipitation estimates from the CaPA also significantly improved the estimation of the Fine Fuel Moisture Code, Initial Spread Index and FWI in areas of radar coverage, improving estimates of potential fire danger. [ABSTRACT FROM AUTHOR]