Global Environmental eMuLator
The Global Environmental eMuLator (GEML) is a component of the experimental Global Deterministic Prediction System (GDPS) and is an artificial intelligence (AI)-based weather emulator trained on past atmospheric states. More specifically, this GEML model is based on data compatible with the ¼°, 13-level version of the GraphCast model (Lam et al. 2023) from DeepMind. It was trained and refined by ECCC, using ECMWF's ERA5 data (1979-2016) and operational analyses (2016-2021). The weights have been recalculated and are also available to the public. Forecasts are carried out twice daily, each with a 10-day lead time. It generates the reference large-scale temperature and horizontal wind fields, toward which GDPS's GEM forecasts are spectrally nudged. The geographical coverage is global with a horizontal resolution of 28 km. Data is available on 13 pressure levels, and employs a uniform latitude-longitude grid with 0.25-degree grid resolution. Six atmospheric variables defined on the 13 pressure levels, along with 4 surface variables are available every 6 hours.
Metadata
Date Created
2025-06-12
Date Published
2025-06-12
Temporal Coverage
2025-06-12 - Present
Access in last 30 days
3
All time access
28
Source(s) and Citation
Government of Canada; Environment and Climate Change Canada; Meteorological Service of Canada. (2025-06-12). Global Environmental eMuLator.
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Related keywords
prediction, global, deterministic, emulator, artificial intelligence, weather forecasts, precipitation, humidity, air temperature, wind, meteorological data
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