The added value of stochastic spatial disaggregation for short-term rainfall forecasts currently available in Canada - Gagnon, Patrick; Rousseau, Alain N.; Charron, Dominique; Fortin, Vincent; Audet, René (Journal of Hydrology, 2017)
Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada’s (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100 km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Quebec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.
MAG ID: 2747439784
Keywords: Rousseau_Alain

Using a Particle Filter to Estimate the Spatial Distribution of the Snowpack Water Equivalent - Cantet, Philippe; Boucher, Marie-Amélie; Lachance-Coutier, S.; Turcotte, Richard; Fortin, Vincent (Journal of Hydrometeorology, 2019)
AbstractA snow model forced by temperature and precipitation is used to simulate the spatial distribution of snow water equivalent (SWE) over a 600,000 km2 portion of the province of Quebec, Canada. We propose to improve model simulations by assimilating SWE data from sporadic manual snow surveys with a particle filter. A temporally and spatially correlated perturbation of the meteorological forcing is used to generate the set of particles. The magnitude of the perturbations is fixed objectively. First, the particle filter and direct insertion were both applied on 88 sites for which measured SWE consist of more or less five values per year over a period of 17 years. The temporal correlation of perturbations enables to improve the accuracy and the ensemble dispersion of the particle filter, while the spatial correlation lead to a spatial coherence in the particle weights. The spatial estimates of SWE obtained with the particle filter are compared with those obtained through optimal interpolation of the sno...
MAG ID: 2917589794

Warm Season Evaluation of Soil Moisture Prediction in the Soil, Vegetation, and Snow (SVS) Scheme - Alavi, Nasim; Bélair, Stéphane; Fortin, Vincent; Zhang, Shunli; Husain, Syed Zahid; Carrera, Marco L.; Abrahamowicz, Maria (Journal of Hydrometeorology, 2016)
AbstractA new land surface scheme has been developed at Environment and Climate Change Canada (ECCC) to provide surface fluxes of momentum, heat, and moisture for the Global Environmental Multiscale (GEM) atmospheric model. In this study, the performance of the Soil, Vegetation, and Snow (SVS) scheme in estimating the surface and root-zone soil moisture is evaluated against the Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme currently used operationally at ECCC within GEM for numerical weather prediction. In addition, the sensitivity of SVS soil moisture results to soil texture and vegetation data sources (type and fractional coverage) has been explored. The performance of SVS and ISBA was assessed against a large set of in situ observations as well as the brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) satellite over North America. The results indicate that SVS estimates the time evolution of soil moisture more accurately, and compared to ISBA, results in highe...
MAG ID: 2464829665

The Multibudget Soil, Vegetation, and Snow (SVS) Scheme for Land Surface Parameterization: Offline Warm Season Evaluation - Husain, Syed Zahid; Alavi, Nasim; Bélair, Stéphane; Carrera, Marco L.; Zhang, Shunli; Fortin, Vincent; Abrahamowicz, Maria; Gauthier, Nathalie (Journal of Hydrometeorology, 2016)
AbstractA new land surface parameterization scheme, named the Soil, Vegetation, and Snow (SVS) scheme, was recently developed at Environment and Climate Change Canada to replace the operationally used Interactions between Soil, Biosphere, and Atmosphere (ISBA) scheme. The new scheme is designed to address a number of weaknesses and limitations of ISBA that have been identified over the last decade. Unlike ISBA, which calculates a single energy budget for the different land surface components, SVS introduces a new tiling approach that includes separate energy budgets for bare ground, vegetation, and two different snowpacks (over bare ground and low vegetation and under high vegetation). The inclusion of a photosynthesis module as an option to determine the surface stomatal resistance is another significant addition in SVS. The representation of vertical water transport through soil has also been substantially improved in SVS with the introduction of multiple soil layers. Overall, offline simulations conduc...
MAG ID: 2427751365

Toward an Operational Water Cycle Prediction System for the Great Lakes and St. Lawrence River - Durnford, Dorothy; Fortin, Vincent; Smith, Gregory C.; Archambault, B.; Deacu, Daniel; Dupont, Frédéric; Dyck, Sarah; Martinez, Y.; Klyszejko, Erika; MacKay, M.; Liu, L.; Pellerin, Pierre; et al. (Bulletin of the American Meteorological Society, 2017)
AbstractIn this time of a changing climate, it is important to know whether lake levels will rise, potentially causing flooding, or river flows will dry up during abnormally dry weather. The Great Lakes region is the largest freshwater lake system in the world. Moreover, agriculture, industry, commerce, and shipping are active in this densely populated region. Environment and Climate Change Canada (ECCC) recently implemented the Water Cycle Prediction System (WCPS) over the Great Lakes and St. Lawrence River watershed (WCPS-GLS version 1.0) following a decade of research and development. WCPS, a network of linked models, simulates the complete water cycle, following water as it moves from the atmosphere to the surface, through the river network and into lakes, and back to the atmosphere. Information concerning the water cycle is passed between the models. WCPS is the first short-to-medium-range prediction system of the complete water cycle to be run on an operational basis anywhere. It currently produces ...
MAG ID: 2766397048