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| Global Environmental Multiscale Model | |
|---|---|
| Name | Global Environmental Multiscale Model |
| Acronym | GEM |
| Developer | Environment and Climate Change Canada |
| Initial release | 1990s |
| Programming language | Fortran |
| Latest release | ongoing |
| License | proprietary (operational) |
| Website | Environment and Climate Change Canada |
Global Environmental Multiscale Model is a numerical weather prediction and atmospheric modeling system developed and maintained by Environment and Climate Change Canada. The model serves as a unified framework for short-term forecasting, seasonal prediction, and research in mesoscale and global dynamics, interfacing with observational programs such as World Meteorological Organization initiatives and numerical centers like Met Office and National Oceanic and Atmospheric Administration. GEM has been employed in operational services, research collaborations with institutions such as Canadian Space Agency and University of Toronto, and international intercomparisons including the WMO Unified Model exchanges and World Weather Research Programme projects.
GEM is structured as a nonhydrostatic compressible model with options for global and regional grids and coupling to ocean and land components, used by Environment and Climate Change Canada alongside collaborations with Canadian Centre for Meteorological and Environmental Prediction partners. It is applied in contexts ranging from synoptic forecasting for Toronto and Vancouver to climate-scale experiments connecting to programs led by Intergovernmental Panel on Climate Change and the Coupled Model Intercomparison Project. Operational centers such as National Aeronautics and Space Administration research divisions and university groups including McGill University have contrasted GEM output with models like the European Centre for Medium-Range Weather Forecasts system and the Global Forecast System.
Development began in the late 20th century within Environment Canada, influenced by frameworks from institutions like Canadian Meteorological Centre predecessors and collaborations with academic groups at University of British Columbia and Université de Montréal. Major milestones include transitions to nonhydrostatic dynamics, coupling to land-surface schemes tested in projects with Natural Resources Canada and Fisheries and Oceans Canada, and operational upgrades coincident with initiatives from Canadian Space Agency missions. GEM evolution has been documented in comparisons at meetings hosted by American Meteorological Society and exchanges at European Geosciences Union assemblies.
GEM offers multiple dynamical cores and physical parameterizations, comparable in scope to schemes used at ECMWF and the Met Office. Physical packages include radiation schemes validated against datasets from Atmospheric Radiation Measurement sites, microphysics similar to those in studies at National Center for Atmospheric Research, and planetary boundary layer treatments compared with work at Scripps Institution of Oceanography. Land-surface coupling has been developed with reference to observations from Canadian Prairies and tundra studies involving Nunavut research programs. Ocean coupling experiments have been undertaken with partners at Fisheries and Oceans Canada and examined in intercomparison projects involving Jet Propulsion Laboratory and NOAA.
GEM uses variational and ensemble-based assimilation methods that align with practices at ECMWF and Met Office; implementations have been compared with 4D-Var efforts and Ensemble Kalman Filter studies in collaborations with University of Oxford and Princeton University. Observational inputs include satellite radiances from missions by European Space Agency, microwave sounders from NOAA satellites, radiosonde networks coordinated by World Meteorological Organization, and surface networks in partnership with Environment and Climate Change Canada operations. Reanalysis and initialization efforts draw on archives linked to Global Climate Observing System standards and have been evaluated against products from NCEP and ECMWF reanalyses.
Operationally, GEM drives national forecasts supporting services in provinces such as Ontario and British Columbia and integrates into aviation forecasting used at hubs like Toronto Pearson International Airport and Vancouver International Airport. Research applications include high-resolution studies of severe convection comparable to case studies from Storm Prediction Center investigations, coupled atmosphere–ocean forecasts for Atlantic Canada fisheries, and air quality modeling aligned with work by Health Canada and provincial agencies. Emergency management collaborations have linked GEM outputs to planning at Public Safety Canada and international disaster-relief exercises run under United Nations frameworks.
Verification of GEM has been conducted using classical metrics and case-study intercomparisons at venues such as American Meteorological Society conferences and in journals affiliated with Royal Meteorological Society. Skill assessments compare GEM to models from ECMWF, NOAA, and the Met Office using datasets from projects coordinated by WMO and verification centers at University of Alberta and McMaster University. Specialized verification for precipitation, wind, and temperature leverages observations from networks operated by Environment and Climate Change Canada and regional observatories including Pacific Climate Impacts Consortium.
Known limitations include challenges in convection-permitting scales and computational demands relative to ensembles used by ECMWF and NOAA, prompting research collaborations with high-performance computing centers such as Compute Canada and Canadian Foundation for Innovation. Future directions emphasize coupling with biogeochemical components in partnership with Fisheries and Oceans Canada and Natural Resources Canada, improving assimilation with inputs from Copernicus Programme satellites and enhancing predictability studies in coordination with World Climate Research Programme and academic partners at University of Toronto and McGill University.
Category:Numerical weather prediction models