Subventions et des contributions :

Titre :
Numerical Weather Prediction Advances for a Growing Canadian Economy
Numéro de l’entente :
RGPIN
Valeur d'entente :
160 000,00 $
Date d'entente :
10 mai 2017 -
Organisation :
Conseil de recherches en sciences naturelles et en génie du Canada
Location :
Colombie-Britannique, Autre, CA
Numéro de référence :
GC-2017-Q1-01548
Type d'entente :
subvention
Type de rapport :
Subventions et des contributions
Informations supplémentaires :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Nom légal du bénéficiaire :
Stull, Roland (The University of British Columbia)
Programme :
Programme de subventions à la découverte - individuelles
But du programme :

Earth’s climate system is changing. For Canadians, a changing climate provides an opportunity for economic growth to serve the needs of our growing population. Our research focuses on enhancing the economy through the use of computerized weather prediction.
Computerized forecasts from Numerical Weather Prediction (NWP) models are replacing human forecasters in more and more forecast applications. At UBC we have the tools needed for NWP research: high­-performance-computing clusters, NWP programs, and associated expertise. Every day we run an ensemble of 62 NWP model members in real time that we combine into several short­-range (1 to 5 day) and medium-range (6 to 30 day) forecasts. These forecasts allow us to robustly test our research against natural daily weather variability, rather than focusing on unrepresentative ideal case studies. We also use these advanced tools to educate the next generation of scientists.
Our broad research program focuses on advancing short- and medium-­range NWP for a growing Canadian economy. This includes improving weather-forecast methods for transportation (highway, rail, shipping), weather­-related disasters (floods, debris flows, avalanches, windstorms, forest-fire smoke), and clean energy (hydro, wind, solar, biomass) over complex terrain such as in Western Canada.
We propose to: 1) devise new dynamical cores for NWP models; 2) design new NWP planetary-boundary-layer parameterizations for mountainous terrain; 3) reduce forecast errors at key locations in the mountains by creating new post­-processing methods to correct errors associated with NWP spatial resolution; 4) optimize NWP ensemble size using cloud-computing platforms; 5) improve ensemble forecasts of forest-­fire smoke as affects air quality; 6) analyze recent weather-related disasters to improve understanding and prediction; and 7) create a water model for resource management and flood forecasting.
Significance: As population increases, more Canadians will be:
1) competing for energy and water resources. Our NWP research will enable utility companies to better integrate variable clean-energy sources into the power grid, and to provide clean water.
2) living in marginal geographic regions such as floodplains, coastal areas, and steep mountain slopes. Our work to improve prediction of weather­-related disasters in the face of climate change will empower Canadians to be survivors, and will allow local governments to provide better zoning and emergency­-preparedness plans.
3) producing air pollution as a byproduct of life and commerce. Our work on improving air-­quality forecasting will support the health of Canadians while allowing industries to thrive.