Description

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.

Resources

Name	Format	Description	Link
Datamart du SMC AMQP	GRIB2		amqps://dd.meteo.gc.ca/*.WXO-DD.model_gdps-geml_25km.#
MSC Datamart AMQP	GRIB2		amqps://dd.weather.gc.ca/*.WXO-DD.model_gdps-geml_25km.#
Datamart du SMC	GRIB2		https://dd.meteo.gc.ca/today/model_gdps-geml/25km/
MSC Datamart	GRIB2		https://dd.weather.gc.ca/today/model_gdps-geml/25km/
MSC Open Data documentation	HTML		https://eccc-msc.github.io/open-data/msc-data/nwp_gdps/readme_gdps-geml-datamart_en/
Documentation des données ouvertes du SMC	HTML		https://eccc-msc.github.io/open-data/msc-data/nwp_gdps/readme_gdps-geml-datamart_fr/
SGPD-GEML - Température de l'air à 2m au dessus de la surface [°C] [expérimental]	WMS		https://geo.meteo.gc.ca/geomet?lang=fr&service=WMS&request=GetCapabilities&layers=GDPS-GEML_25km_AirTemp-Contour_2m
GDPS-GEML - Air temperature at 2m above ground [°C] [experimental]	WMS		https://geo.weather.gc.ca/geomet?lang=en&service=WMS&request=GetCapabilities

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