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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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