Since November 2014, the Meteorological Services of Canada (MSC) has been running a real-time numerical weather prediction system that provides deterministic forecasts on a regional domain with a 2.5-km horizontal grid spacing covering a large portion of Canada using the Global Environmental Multiscale (GEM) forecast model. This system, referred to as the High Resolution Deterministic Prediction System (HRDPS), is currently downscaled from MSC’s operational 10-km GEM-based regional system but uses initial surface fields from a high-resolution (2.5 km) land data assimilation system coupled to the HRDPS and initial hydrometeor fields from the forecast of a 2.5-km cycle, which reduces the spinup time for clouds and precipitation. Forecast runs of 48 h are provided four times daily. The HRDPS was tested and compared to the operational 10-km system. Model runs from the two systems were evaluated against surface observations for common weather elements (temperature, humidity, winds, and precipitation), fractional cloud cover, and also against upper-air soundings, all using standard metrics. Although the predictions of some fields were degraded in some specific regions, the HRDPS generally outperformed the operational system for a majority of the scores. The evaluation illustrates the added value of the 2.5-km model and the potential for improved numerical guidance for the prediction of high-impact weather.
CANFIS, an empirical-statistical technique, is used to reconstruct continuous daily surface marine winds at 6-hourly intervals at 13 Canadian buoy sites along the western coast of Canada for the 40 yr period 1958-1997. CANFIS combines Classification and Regression Trees (CART) and the NeuroFuzzy Inference System (NFIS) in a 2-step procedure. CART is a tree-based algorithm used to optimize the process of selecting relevant predictors from a large pool of potential predictors. Using the selected predictors, NFIS builds a model for continuous output of the predictand. In this project we used CAN-FIS to link large-scale atmospheric predictors with regional wind observations during a learning phase from 1990 to 1995 in order to generate empirical-statistical relationships between the predictors and buoy winds. The large-scale predictors are derived from the NCAR/NCEP 40 yr reanalysis project while the buoy winds come from the Canadian Atmospheric Environment Service buoy network. Validation results with independent buoy wind data show a good performance of CANFIS. The CANFIS winds reproduce the independent buoy winds with greater accuracy than winds reconstructed with a stepwise multivariate linear regression technique. In addition, they are better than the NCEP reanalyzed winds interpolated to the buoy locations. The reconstructed statistical winds recover more than 60% of the observed wind variance during an independent verification period. In particular, correlation coefficients between independent buoy wind time series and CANFIS wind time series vary between 0.61 and 0.98. Our results suggest that CANFIS is a successful downscaling method. It is able to recover a substantial fraction of the variation of surface marine winds, especially along coastal regions where ageostrophic effects are relatively important.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.