ECMWF AIFS ENS forecast

Spatial domain	Global
Spatial resolution	0.25 degrees (~20km)
Time domain	Forecasts initialized 2025-07-02 00:00:00 UTC to Present
Time resolution	Forecasts initialized every 6 hours
Forecast domain	Forecast lead time 0-360 hours (0-15 days) ahead
Forecast resolution	6 hourly

STAC (browse) · validation report

The Artificial Intelligence Forecasting System (AIFS) is a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS ENS is the ensemble configuration of AIFS, containing 51 ensemble members. AIFS is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses.

This dataset is an archive of past and present ECMWF AIFS ENS forecasts. Forecasts are identified by an initialization time (init_time) denoting the start time of the model run, as well as by the ensemble_member. Each forecast steps forward in time along the lead_time dimension.

Examples

	Quickstart (Github)
	Quickstart (Colab)

dynamical.org - ECMWF AIFS ENS forecast

Maximum temperature in ensemble

import dynamical_catalog  # dynamical-catalog>=0.5.0

ds = dynamical_catalog.open("ecmwf-aifs-ens-forecast")
ds["temperature_2m"].sel(init_time="2025-08-01T00", latitude=0, longitude=0).max().compute()

Dimensions

	min	max	units
ensemble_member		Present	1

init_time	2025-07-02T00:00:00Z	Present	seconds since 1970-01-01

latitude	-90	90	degree_north

lead_time	0	1296000	seconds

longitude	-180	179.75	degree_east

Variables

	units	dimensions
dew_point_temperature_2m (`2d`) 2 metre dewpoint temperature	degree_Celsius	init_time × lead_time × ensemble_member × latitude × longitude

downward_long_wave_radiation_flux_surface (`sdlwrf`) Surface downward long-wave radiation flux	W m-2	init_time × lead_time × ensemble_member × latitude × longitude

downward_short_wave_radiation_flux_surface (`sdswrf`) Surface downward short-wave radiation flux	W m-2	init_time × lead_time × ensemble_member × latitude × longitude

geopotential_height_500hpa (`gh`) Geopotential height	m	init_time × lead_time × ensemble_member × latitude × longitude

geopotential_height_850hpa (`gh`) Geopotential height	m	init_time × lead_time × ensemble_member × latitude × longitude

geopotential_height_925hpa (`gh`) Geopotential height	m	init_time × lead_time × ensemble_member × latitude × longitude

precipitation_surface (`prate`) Precipitation rate Average precipitation rate since the previous forecast step. Units equivalent to mm/s.	kg m-2 s-1	init_time × lead_time × ensemble_member × latitude × longitude

pressure_reduced_to_mean_sea_level (`prmsl`) Pressure reduced to MSL	Pa	init_time × lead_time × ensemble_member × latitude × longitude

pressure_surface (`sp`) Surface pressure	Pa	init_time × lead_time × ensemble_member × latitude × longitude

temperature_2m (`2t`) 2 metre temperature	degree_Celsius	init_time × lead_time × ensemble_member × latitude × longitude

temperature_850hpa (`t`) Temperature	degree_Celsius	init_time × lead_time × ensemble_member × latitude × longitude

temperature_925hpa (`t`) Temperature	degree_Celsius	init_time × lead_time × ensemble_member × latitude × longitude

total_cloud_cover_atmosphere (`tcc`) Total cloud cover	percent	init_time × lead_time × ensemble_member × latitude × longitude

wind_u_100m (`100u`) 100 metre U wind component	m s-1	init_time × lead_time × ensemble_member × latitude × longitude

wind_u_10m (`10u`) 10 metre U wind component	m s-1	init_time × lead_time × ensemble_member × latitude × longitude

wind_v_100m (`100v`) 100 metre V wind component	m s-1	init_time × lead_time × ensemble_member × latitude × longitude

wind_v_10m (`10v`) 10 metre V wind component	m s-1	init_time × lead_time × ensemble_member × latitude × longitude

Don't see what you're looking for? Let us know at feedback@dynamical.org.

Details

License

Dataset licensed under CC BY 4.0 and ECMWF Terms of Use.

Attribution and citation

ECMWF AIFS ENS forecast data processed by dynamical.org from ECMWF Open Data.

Or ECMWF AIFS ENS from dynamical.org.

Source

The source grib files this archive is constructed from are provided by ECMWF Open Data and accessed from the AWS Open Data Registry.

ECMWF does not provide user support for the free & open datasets. Users should refer to the public User Forum for any questions related to the source material.

Model updates

AIFS ENS is updated annually. Find details of recent and upcoming changes to the forecasting system on the ECMWF website.

Storage

Storage for this dataset is generously provided by AWS Open Data.

Chunks & shards

This dataset is stored in Zarr format, which splits each variable into a grid of chunks — the smallest unit read from storage. Chunks are grouped into larger shards (the objects actually written to storage), which keeps the object count manageable for long-archive datasets. When possible, aligning your reads with this dataset's chunk grid can significantly improve data access speed.

The element count and coordinate span of this dataset:

dimension	chunk	shard
init_time	1 (6 hours)	1 (6 hours)
lead_time	61 (366 hours)	61 (366 hours)
ensemble_member	51	51
latitude	32 (8°)	384 (96°)
longitude	32 (8°)	288 (72°)
uncompressed	12.2 MiB	1.3 GiB

The same values are published in the dynamical-org:chunking field of this dataset's STAC collection metadata.

Compression

The data values in this dataset have been rounded in their binary floating point representation to improve compression. See Klöwer et al. 2021 for more information on this approach. The exact number of rounded bits can be found in our reformatting code.