Canadian Precipitation Analysis Research Articles

Assimilation of Satellite-Based Rainfall Estimations in the Canadian Precipitation Analysis

AbstractThe Canadian Precipitation Analysis (CaPA) produces a gridded product by assimilating data from surface stations and radar, using a background field provided by the Global Environmental Mul...

Towards validation of the Canadian precipitation analysis (CaPA) for hydrologic modeling applications in the Canadian Prairies

This study presents a three–step validation technique to compare the performance of the Canadian Precipitation Analysis (CaPA) product relative to actual observation as a hydrologic forcing in regional watershed simulation. CaPA is an interpolated (6h or 24h accumulation) reanalysis precipitation product in near real time covering all of North America. The analysis procedure involves point-to-point (P2P) and map-to-map (M2M) comparisons, followed by proxy validation using an operational version of the WATFLOOD™ hydrologic model from 2002 to 2005 in the Lake Winnipeg Basin (LWB), Canada. The P2P technique using a Bayesian change point analysis shows that CaPA corresponds with actual observations (Canadian daily climate data, CDCD), on both an annual and seasonal basis. CaPA has the same spatial pattern, dependency and autocorrelation properties as CDCD pixel by pixel (M2M). When used as hydrologic forcing in WATFLOOD™, results indicate that CaPA is a reliable product for water resource modeling and predictions, but that the quality of CaPA data varies annually and seasonally, as does the quality of observations. CaPA proved most beneficial as a hydrologic forcing during winter seasons where observation quality is the lowest. Reanalysis products, such as CaPA, can be a reliable option in sparse network areas, and is beneficial for regional governments when the cost of new weather stations is prohibitive.

Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada

Abstract. A number of global and regional gridded climate products based on multiple data sources are available that can potentially provide reliable estimates of precipitation for climate and hydrological studies. However, research into the consistency of these products for various regions has been limited and in many cases non-existent. This study inter-compares several gridded precipitation products over 15 terrestrial ecozones in Canada for different seasons. The spatial and temporal variability of the errors (relative to station observations) was quantified over the period of 1979 to 2012 at a 0.5° and daily spatio-temporal resolution. These datasets were assessed in their ability to represent the daily variability of precipitation amounts by four performance measures: percentage of bias, root mean square error, correlation coefficient, and standard deviation ratio. Results showed that most of the datasets were relatively skilful in central Canada. However, they tended to overestimate precipitation amounts in the west and underestimate in the north and east, with the underestimation being particularly dominant in northern Canada (above 60° N). The global product by WATCH Forcing Data ERA-Interim (WFDEI) augmented by Global Precipitation Climatology Centre (GPCC) data (WFDEI [GPCC]) performed best with respect to different metrics. The Canadian Precipitation Analysis (CaPA) product performed comparably with WFDEI [GPCC]; however, it only provides data starting in 2002. All the datasets performed best in summer, followed by autumn, spring, and winter in order of decreasing quality. Findings from this study can provide guidance to potential users regarding the performance of different precipitation products for a range of geographical regions and time periods.

Evaluating uncertainties in modelling the snow hydrology of the Fraser River Basin, British Columbia, Canada

Abstract. This study evaluates predictive uncertainties in the snow hydrology of the Fraser River Basin (FRB) of British Columbia (BC), Canada, using the Variable Infiltration Capacity (VIC) model forced with several high-resolution gridded climate datasets. These datasets include the Canadian Precipitation Analysis and the thin-plate smoothing splines (ANUSPLIN), North American Regional Reanalysis (NARR), University of Washington (UW) and Pacific Climate Impacts Consortium (PCIC) gridded products. Uncertainties are evaluated at different stages of the VIC implementation, starting with the driving datasets, optimization of model parameters, and model calibration during cool and warm phases of the Pacific Decadal Oscillation (PDO). The inter-comparison of the forcing datasets (precipitation and air temperature) and their VIC simulations (snow water equivalent – SWE – and runoff) reveals widespread differences over the FRB, especially in mountainous regions. The ANUSPLIN precipitation shows a considerable dry bias in the Rocky Mountains, whereas the NARR winter air temperature is 2 °C warmer than the other datasets over most of the FRB. In the VIC simulations, the elevation-dependent changes in the maximum SWE (maxSWE) are more prominent at higher elevations of the Rocky Mountains, where the PCIC-VIC simulation accumulates too much SWE and ANUSPLIN-VIC yields an underestimation. Additionally, at each elevation range, the day of maxSWE varies from 10 to 20 days between the VIC simulations. The snow melting season begins early in the NARR-VIC simulation, whereas the PCIC-VIC simulation delays the melting, indicating seasonal uncertainty in SWE simulations. When compared with the observed runoff for the Fraser River main stem at Hope, BC, the ANUSPLIN-VIC simulation shows considerable underestimation of runoff throughout the water year owing to reduced precipitation in the ANUSPLIN forcing dataset. The NARR-VIC simulation yields more winter and spring runoff and earlier decline of flows in summer due to a nearly 15-day earlier onset of the FRB springtime snowmelt. Analysis of the parametric uncertainty in the VIC calibration process shows that the choice of the initial parameter range plays a crucial role in defining the model hydrological response for the FRB. Furthermore, the VIC calibration process is biased toward cool and warm phases of the PDO and the choice of proper calibration and validation time periods is important for the experimental setup. Overall the VIC hydrological response is prominently influenced by the uncertainties involved in the forcing datasets rather than those in its parameter optimization and experimental setups.

Evaluation of the Canadian Precipitation Analysis (CaPA) to improve forest fire danger rating

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.

Added Value of Alternative Information in Interpolated Precipitation Datasets for Hydrology

Abstract Gridded climate datasets are produced in many parts of the world by applying various interpolation methods to weather observations, to which are sometimes added secondary information (in addition to geographic location) such as topography and radar or atmospheric model outputs. For a region of interest, the choice of a dataset for a given study can be a significant challenge given the lack of information on the similarities and differences that exist between datasets, or about the benefits that one dataset may present relative to another. This study aims to provide information on the spatial and temporal differences between gridded precipitation datasets and their implication for hydrological modeling. Three gridded datasets for the province of Quebec are considered: the Natural Resources Canada (NRCan) dataset, the Canadian Precipitation Analysis (CaPA) dataset, and the dataset from the Ministère du Développement Durable, de l’Environnement et de la Lutte contre les Changements Climatiques du Québec (MDDELCC). Using statistical metrics and diagrams, these precipitation datasets are compared with each other. Hydrological responses of 181 Quebec watersheds with respect to each gridded precipitation dataset are also analyzed using the hydrological model HSAMI. The results indicate strong similarities in the southern parts and disparities in the central and northern parts of the province of Quebec. Analysis of hydrological simulations indicates that the CaPA dataset offers the best results, particularly for watersheds located in the central and northern parts of the province. MDDELCC shows the best performance in watersheds located on the south shore of the St. Lawrence River and comes out as the overall second-best option.

Great Lakes Runoff Inter-comparison Project, phase 2: Lake Ontario (GRIP-O)

The Great Lakes Runoff Inter-comparison Project for Lake Ontario (GRIP-O) aims to compare different hydrologic models, using the same settings, in their ability to estimate runoff for the Lake Ontario watershed. The watershed is challenging because many of its tributaries have a regulated flow regime and a significant part remains ungauged. GRIP-O follows the GRIP-M project which focused on Lake Michigan. It involves a comparison between two different sources of precipitation data (CaPA - Canadian Precipitation Analysis and the GHCND - Global Historical Climatology Network - Daily), and focuses here on two lumped models, GR4J (modele du Genie Rural a 4 parametres Journalier) and LBRM (Large Basin Runoff Model). Results indicate that both models perform very well, with GR4J performing slightly better than LBRM and the GHCND precipitation dataset resulting in better simulations than CaPA, for this area. Performances are, however, always very satisfactory whatever the combination of model/precipitation data used, even for regulated catchments, and do not show any clear correlation to any of the catchments' properties studied here. Results also tend to confirm that the Area-Ratio Method is appropriate for extrapolating flows from the gauged part of a catchment to the whole catchment including its ungauged parts, as demonstrated in GRIP-M.

Comparison of land surface scheme simulations with field observations versus atmospheric model output as forcing

ABSTRACTThe low density of meteorological stations in parts of Canada necessitates using numerical weather prediction (NWP)/assimilation output for hydrological modelling. In this study, comparisons are made of simulated land surface variables when using field observations versus NWP output as forcing for two well-instrumented sites: the mountainous and forested Marmot Creek Basin (MCRB) in the Canadian Rocky Mountains, and a prairie cropland/grassland site (Kenaston). The Canadian Land Surface Scheme 3.6 (CLASS) was used for modelling. The Global Environmental Multiscale (GEM) model with Canadian Precipitation Analysis (CaPA) was also used as forcing. There was good agreement between observed meteorology and GEM/CaPA, though some deficiencies in GEM/CaPA were identified: the effects of sub-grid topography on incoming radiation and wind speed were not accounted for at MCRB, and CaPA did not capture some convective rainfall events at Kenaston. CLASS simulations using both sets of forcing showed difficulties in simulating snow depth, soil moisture and evapotranspiration; certain difficulties were linked to GEM/CaPA deficiencies and/or CLASS. Both sets of forcing tended to overestimate the duration of snow cover at MCRB, but during different years. With GEM/CaPA as forcing, CLASS overestimated the duration of frozen soils. The GEM/CaPA precipitation difficulties at Kenaston degraded soil moisture simulations.EDITOR A. Castellarin; ASSOCIATE EDITOR E. Volpi

Performance Evaluation of the Canadian Precipitation Analysis (CaPA)

Abstract This paper presents an assessment of the operational system used by the Meteorological Service of Canada for producing near-real-time precipitation analyses over North America. The Canadian Precipitation Analysis (CaPA) system optimally combines available surface observations with numerical weather prediction (NWP) output in order to produce estimates of precipitation on a 15-km grid at each synoptic hour (0000, 0600, 1200, and 1800 UTC). The validation protocol used to assess the quality of the CaPA has demonstrated the usefulness of the system for producing reliable estimates of precipitation over Canada, even in areas with few or no weather stations. The CaPA is found to be better in autumn, spring, and winter than in summer. This is because of the difficulty in correctly producing convective precipitation in the NWP because of the low spatial resolution of the meteorological model. An investigation of the quality of the precipitation analyses in the 15 terrestrial ecozones of Canada indicates the need to have a sufficient number of observations (at least ~1.17 stations per 10 000 km2) in order to produce a precipitation analysis that is significantly better than the raw NWP product. Improvements of the CaPA system by including provincial networks as well as radar and satellite information are expected in the future.

Assimilation of radar quantitative precipitation estimations in the Canadian Precipitation Analysis (CaPA)

Summary The Canadian Precipitation Analysis (CaPA) is a data analysis system used operationally at the Canadian Meteorological Center (CMC) since April 2011 to produce gridded 6-h and 24-h precipitation accumulations in near real-time on a regular grid covering all of North America. The current resolution of the product is 10-km. Due to the low density of the observational network in most of Canada, the system relies on a background field provided by the Regional Deterministic Prediction System (RDPS) of Environment Canada, which is a short-term weather forecasting system for North America. For this reason, the North American configuration of CaPA is known as the Regional Deterministic Precipitation Analysis (RDPA). Early in the development of the CaPA system, weather radar reflectivity was identified as a very promising additional data source for the precipitation analysis, but necessary quality control procedures and bias-correction algorithms were lacking for the radar data. After three years of development and testing, a new version of CaPA–RDPA system was implemented in November 2014 at CMC. This version is able to assimilate radar quantitative precipitation estimates (QPEs) from all 31 operational Canadian weather radars. The radar QPE is used as an observation source and not as a background field, and is subject to a strict quality control procedure, like any other observation source. The November 2014 upgrade to CaPA–RDPA was implemented at the same time as an upgrade to the RDPS system, which brought minor changes to the skill and bias of CaPA–RDPA. This paper uses the frequency bias indicator ( FBI ), the equitable threat score ( ETS ) and the departure from the partial mean ( DPM ) in order to assess the improvements to CaPA–RDPA brought by the assimilation of radar QPE. Verification focuses on the 6-h accumulations, and is done against a network of 65 synoptic stations (approximately two stations per radar) that were withheld from the station data assimilated by CaPA–RDPA. It is shown that the ETS and the DPM scores are both improved for precipitation events between 0.2 mm and 25 mm per 6-h, and that the FBI is unchanged.

Predicting Snow Depth in a Forest–Tundra Landscape using a Conceptual Model Allowing for Snow Redistribution and Constrained by Observations from a Digital Camera

abstractEstimation of snow depth in the forest–tundra landscape remains a challenge because of a lack of reliable and frequent observations on precipitation and snow depth. Snow models forced by gridded meteorological datasets are often the only option available for assessing snow depth at the local scale. Unfortunately, these models generally do not take into account the snow redistribution process between open and forested areas which frequently occurs in the forest–tundra landscape. A simple modification to an existing snow accumulation and melt model is proposed in order to allow for snow redistribution. Along with a technique for taking advantage of snow depth observations obtained from a digital camera, the model is shown to provide accurate predictions of snow depth at the local scale when forced with precipitation data from Environment Canada's Canadian Precipitation Analysis. Results from this study suggest that instrumenting automated weather stations with a digital camera, together with small modifications to an existing model used operationally for snow depth prediction, could result in significant improvements to snow depth prediction and analysis in this environment. Further testing at sites where snow water equivalent of the snowpack is available should, however, be performed to fully validate the method.

Inter‐comparison of high‐resolution gridded climate data sets and their implication on hydrological model simulation over the Athabasca Watershed, Canada

AbstractSeveral different gridded climate data sets have recently been made available with the purpose of providing a consistent set of climatic data for many hydro‐climatic studies. Recent advances in land‐surface schemes and their implementation in fully distributed processes‐based hydrologic models have demanded even higher‐resolution gridded data. It remains, however, a challenge to identify the most reliable gridded climate data for hydrologic modelling, especially in mountainous headwater regions where there is significant spatial variability but few observing stations. Moreover, the accuracy of such climate forcing data applied to alpine headwaters directly affects the modelled hydrologic responses of the lower, downstream portions of river basins. This study evaluates the spatial and temporal differences in precipitation and temperature fields among three high‐resolution climate data sets available in Canada, namely, the North American Regional Reanalysis, the Canadian Precipitation Analysis and the thin‐plate smoothing splines (ANUSPLIN). Inter‐comparison of the quality of these data sets was undertaken for the Athabasca River basin in western Canada. The hydrologic responses of this watershed with respect to each of the three gridded climate data sets were also evaluated using the Variable Infiltration Capacity model. Results indicate that the data sets have systematic differences, which vary with regional characteristics – the largest differences being for mountainous regions. The hydrologic model simulations corresponding to those three forcing data sets also show significant differences and more with North American Regional Reanalysis than those between Canadian Precipitation Analysis and ANUSPLIN. © 2014 Her Majesty the Queen in Right of Canada. Hydrological Processes © 2014 John Wiley & Sons Ltd.

Predicting the Net Basin Supply to the Great Lakes with a Hydrometeorological Model

Abstract The paper presents the incremental improvement of the prediction of the Great Lakes net basin supply (NBS) with the hydrometeorological model Modélisation Environmentale–Surface et Hydrologie (MESH) by increasing the accuracy of the simulated NBS components (overlake precipitation, lake evaporation, and runoff into the lake). This was achieved through a series of experiments with MESH and its parent numerical weather prediction model [the Canadian Global Environmental Multiscale model in its regional configuration (GEM Regional)]. With forcing extracted from operational GEM Regional forecasts, MESH underestimated the NBS in fall and winter. The underestimation increased when the GEM precipitation was replaced with its corrected version provided by the Canadian Precipitation Analysis. This pointed to overestimated lake evaporation and prompted the revision of the parameterization of the surface turbulent fluxes over water used both in MESH and GEM. The revised parameterization was validated against turbulent fluxes measured at a point on Lake Superior. Its use in MESH reduced the lake evaporation and largely corrected the NBS underestimation. However, the Lake Superior NBS became overestimated, signaling an inconsistency between the reduced lake evaporation and the prescribed precipitation. To remove the inconsistency, a new forcing dataset (including precipitation) was generated with the GEM model using the revised flux parameterization. A major NBS simulation improvement was obtained with the new atmospheric forcing reflecting the atmospheric response to the modified surface fluxes over the lakes. Additional improvements resulted by correcting the runoff with a modified snowmelt rate and by insertion of observed streamflows. The study shows that accurate lake evaporation simulation is crucial for accurate NBS prediction.

A Canadian Precipitation Analysis (CaPA) Project: Description and Preliminary Results

Abstract A Canadian Precipitation Analysis (CaPA) project for producing 6 h rainfall accumulations at a resolution of 15 km over North America in real‐time is described. The spatial interpolation technique is based on statistical interpolation using short‐range precipitation forecasts from the Canadian Meteorological Centre's (CMC) regional model as the background field with rain‐gauge measurements from the surface network and radar derived rain rates as observations. A pilot study was undertaken over the province of Québec at a resolution of 10 km using additional rain‐gauge observations from a cooperative network for August 2003. This test period allowed the development of methodologies for an objective estimation of background and observation error statistics and for improving the overall quality of rain‐gauge and radar data. The improved skill of the analysis with respect to the model short‐range forecasts is assessed against radar precipitation. The use of additional rain gauges from the surface cooperative network in Québec significantly increased the quality of the resulting analysis.

A Canadian precipitation analysis (CaPA) project: Description and preliminary results

A Canadian Precipitation Analysis (CaPA) project for producing 6 h rainfall accumulations at a resolution of 15 km over North America in real‐time is described. The spatial interpolation technique is based on statistical interpolation using short‐range precipitation forecasts from the Canadian Meteorological Centre's (CMC) regional model as the background field with rain‐gauge measurements from the surface network and radar derived rain rates as observations. A pilot study was undertaken over the province of Québec at a resolution of 10 km using additional rain‐gauge observations from a cooperative network for August 2003. This test period allowed the development of methodologies for an objective estimation of background and observation error statistics and for improving the overall quality of rain‐gauge and radar data. The improved skill of the analysis with respect to the model short‐range forecasts is assessed against radar precipitation. The use of additional rain gauges from the surface cooperative network in Québec significantly increased the quality of the resulting analysis.