Accurate and reliable spatial and temporal datasets of precipitation and surface variables are essential not only for meteorological and hydrological forecasting, but also for water resources management and climate change studies. The need for higher spatial and temporal resolution of precipitation and surface variables is a demand that leads regional reanalysis and various downscaling techniques. Over the last decade, precipitation and surface reanalysis datasets have been significantly improved by several centres around the world. Further, there is a growing number of applications in various sectors of activity, such as research, services, and policy-making, and therefore the demand for these datasets is increasing.
This presentation describes the deterministic reforecast and dynamical downscaling approach carried out by ECCC, based on its major operational meteorological systems, and used in the generation of reforecast/reanalysis. A global reanalysis (ERA-Interim) is used to initialize the Global Deterministic Reforecast System (GDRS) at 39-km resolution based on the Global Environmental Multi-Scale (GEM) model. Subsequently, the Regional Deterministic Reforecast System (RDRS)at 10 km resolution is coupled with the Canadian Surface Assimilation System (CaLDAS) and the Canadian Precipitation Analysis System (CaPA) to produce surface and precipitation analyses. Dedicated surface reanalysis products, such as precipitation, snow depth, surface ground temperature and moisture have been validated and will also be presented.