ECMWF IFS ENS forecast, 46 day, 6 hourly, 1.5 degree

Name: ECMWF IFS ENS forecast, 46 day, 6 hourly, 1.5 degree
Creator: dynamical.org
License: https://creativecommons.org/licenses/by/4.0/

time-optimized

Spatial domain	Global
Spatial resolution	1.5 degrees (~165km)
Time domain	Forecasts initialized 2026-01-01 00:00:00 UTC to Present
Time resolution	Forecasts initialized every 24 hours
Forecast domain	Forecast lead time 0-1104 hours (0-46 days) ahead
Forecast resolution	6 hourly

STAC (browse) · validation report

The Integrated Forecasting System (IFS) is a global forecast model developed by ECMWF. ENS is an ensemble configuration of IFS, containing 51 ensemble members. IFS consists of a numerical model of the Earth system, which includes an atmospheric model at its heart, coupled with models of other Earth system components such as the ocean. The data assimilation system combines the latest weather observations with a recent forecast to obtain the best possible estimate of the current state of the Earth system.

This dataset is an archive of ECMWF IFS ENS sub-seasonal-range 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 along the lead_time dimension from 0 to 1104 hours (0 to 46 days) at a 6 hourly step, and carries 101 ensemble members on a global 1.5 degree grid. This dataset contains the 00 UTC initialization times only.

This is the 6 hourly companion to the daily 46 day dataset. It carries five surface variables: the 10 metre wind components, and the precipitation rate and maximum and minimum 2 metre temperature over the previous 6 hours.

Note: ECMWF's licence holds sub-seasonal-range forecasts back for 48 hours, so this is not a real-time dataset — each initialization becomes available about two days after its init_time.

Related Datasets

Examples

	ECMWF IFS ENS 46 day, daily and 6 hourly (Github)
	ECMWF IFS ENS 46 day, daily and 6 hourly (Colab)

import dynamical_catalog  # dynamical-catalog>=0.8.0

ds = dynamical_catalog.open("ecmwf-ifs-ens-forecast-46-day-6-hourly-1-5-degree", chunks=None)
ds["maximum_temperature_2m"].sel(init_time="2026-08-01T00", latitude=0, longitude=0).max()

import icechunk
import pystac
import xarray as xr

catalog = pystac.Catalog.from_file("https://stac.dynamical.org/catalog.json")
collection = catalog.get_child("ecmwf-ifs-ens-forecast-46-day-6-hourly-1-5-degree")
asset = collection.assets["icechunk-https"]

repo = icechunk.Repository.open(icechunk.http_storage(asset.href))
session = repo.readonly_session("main")

ds = xr.open_zarr(session.store, chunks=None)
ds["maximum_temperature_2m"].sel(init_time="2026-08-01T00", latitude=0, longitude=0).max()

ECMWF IFS ENS forecast, 46 day, 6 hourly, 1.5 degree · Maximum ensemble temperature

Fetch and follow the setup instructions at https://dynamical.org/prompt.md

Dimensions

	min	max	units
ensemble_member	0	100	1

init_time	2026-01-01T00:00:00Z	Present	seconds since 1970-01-01

latitude	-90	90	degree_north

lead_time	0	3974400	seconds

longitude	-180	178.5	degree_east

Variables

Access a variable by its name (the bold identifier, e.g. ds["maximum_temperature_2m"]).

Dimensions: init_time × lead_time × ensemble_member × latitude × longitude

variable	units
maximum_temperature_2m Maximum temperature (`tmax`)Maximum temperature at 2 metres over the previous 6 hours.	degree_Celsius
minimum_temperature_2m Minimum temperature (`tmin`)Minimum temperature at 2 metres over the previous 6 hours.	degree_Celsius
precipitation_surface Precipitation rate (`prate`)Average precipitation rate over the previous 6 hours. Units equivalent to mm/s.	kg m-2 s-1
wind_u_10m 10 metre U wind component (`10u`)	m s-1
wind_v_10m 10 metre V wind component (`10v`)	m s-1

Don't see what you're looking for? Let us know at [email protected].

Details

License

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

Attribution and citation

ECMWF IFS ENS sub-seasonal-range forecast data processed by dynamical.org from the ECMWF Data Store.

Or ECMWF IFS ENS from dynamical.org.

Source

This archive is built from the ECMWF sub-seasonal-range (S2S) forecast, retrieved from the ECMWF Data Store (ECDS) into the dynamical.org ECMWF IFS grib archive on Source Cooperative.

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

IFS is updated regularly. Find details of recent and upcoming changes to the forecasting system on the ECMWF website.

Storage

Storage for this dataset is generously provided by Source Cooperative, a Radiant Earth initiative. Icechunk storage 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 (1 day)	1 (1 day)
lead_time	93 (558 hours)	186 (1110 hours)
ensemble_member	51	102
latitude	32 (48°)	128 (181.5°)
longitude	30 (45°)	120 (180°)
uncompressed	17.4 MiB	1.1 GiB

Validation report

Review the validation report for variable availability, missing data, known quirks, fill values, value distributions, and sample plots.

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.