ECMWF IFS ENS forecast, 6 day, 0.1 degree, virtual

Name: ECMWF IFS ENS forecast, 6 day, 0.1 degree, virtual
Creator: dynamical.org

map-optimized · low-latency

ID	`ecmwf-ifs-ens-forecast-6-day-0-1-degree-virtual`
Spatial domain	Global
Spatial resolution	0.1 degrees (~10km)
Time domain	Forecasts initialized 2026-10-21 00:00:00 UTC to Present
Time resolution	Forecasts initialized every 6 hours
Forecast domain	Forecast lead time 0-144 hours (0-6 days) ahead
Forecast resolution	3 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: 51 ensemble members in the medium range and 101 in the sub-seasonal (extended) range. 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 past and present ECMWF IFS ENS forecasts at 0.1 degree resolution, optimized for spatial (map) access patterns. Forecasts are identified by an initialization time (init_time) denoting the start time of the model run as well as by the ensemble_member, and step forward along the lead_time dimension out to 6 days. A new forecast is initialized every 6 hours.

Variables on pressure levels live in the pressure_level group.

Note: dynamical-catalog>=1.0.0 (or zarr>=3.2 icechunk>=2.0 gribberish>=1.7) is required.

Related Datasets

Examples

	ECMWF IFS ENS forecasts, virtual (Github)
	ECMWF IFS ENS forecasts, virtual (Colab)

import dynamical_catalog  # dynamical-catalog>=1.0.0

ds = dynamical_catalog.open("ecmwf-ifs-ens-forecast-6-day-0-1-degree-virtual", chunks=None)
ds["temperature_2m"].sel(init_time="2026-11-01T00", lead_time="24h").mean("ensemble_member")

# Variables with a vertical dimension live in the pressure_level group
ds_pressure = dynamical_catalog.open("ecmwf-ifs-ens-forecast-6-day-0-1-degree-virtual", group="pressure_level", chunks=None)
ds_pressure["geopotential_height"].sel(init_time="2026-11-01T00", lead_time="120h", pressure_level=500).std("ensemble_member")

ECMWF IFS ENS forecast, 6 day, 0.1 degree, virtual · Ensemble mean temperature map

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-6-day-0-1-degree-virtual")
asset = collection.assets["icechunk-https"]

authorize = icechunk.containers_credentials({"gs://ecmwf-open-data/": icechunk.gcs_credentials(anonymous=True), "s3://ecmwf-forecasts/": icechunk.s3_anonymous_credentials()})
repo = icechunk.Repository.open(
    icechunk.http_storage(asset.href),
    authorize_virtual_chunk_access=authorize,
)
session = repo.readonly_session("main")

ds = xr.open_zarr(session.store, chunks=None)
ds["temperature_2m"].sel(init_time="2026-11-01T00", lead_time="24h").mean("ensemble_member")

# Variables with a vertical dimension live in the pressure_level group
ds_pressure = xr.open_zarr(session.store, group="pressure_level", chunks=None)
ds_pressure["geopotential_height"].sel(init_time="2026-11-01T00", lead_time="120h", pressure_level=500).std("ensemble_member")

ECMWF IFS ENS forecast, 6 day, 0.1 degree, virtual · Ensemble mean temperature map

Explore

Dimensions

	min	max	units
ensemble_member	0	50	1

init_time	2026-10-21T00:00:00Z	Present	seconds since 1970-01-01

latitude	-90	90	degree_north

lead_time	0	518400	seconds

longitude	-180	179.9	degree_east

pressure_level	10	1000	hPa

Variables

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

Dimensions: init_time × lead_time × ensemble_member × latitude × longitude

variable	units
categorical_precipitation_type_surface Precipitation type (`ptype`)	1
convective_precipitation_run_total_surface Convective precipitation (`cp`)Accumulated since forecast initialization.	kg m-2
dew_point_temperature_2m 2 metre dewpoint temperature (`2d`)	degree_Celsius
downward_long_wave_radiation_run_total_surface Surface long-wave (thermal) radiation downwards (`strd`)Accumulated since forecast initialization.	W s m-2
downward_short_wave_radiation_run_total_surface Surface short-wave (solar) radiation downwards (`ssrd`)Accumulated since forecast initialization.	W s m-2
eastward_turbulent_surface_stress_run_total_surface Time-integrated eastward turbulent surface stress (`ewss`)Accumulated since forecast initialization.	Pa s
geopotential_height_surface Geopotential height (`gh`)Surface (orography) geopotential height. Available for control member 0 only; members 1-50 are NaN. Time-invariant field published at lead time 0 only.	m
high_cloud_cover High cloud cover (`hcc`)	percent
land_sea_mask_surface Land-sea mask (`lsm`)Fraction (0-1) of the grid box that is land.	1
low_cloud_cover Low cloud cover (`lcc`)	percent
maximum_temperature_2m Maximum temperature (`tmax`)Maximum since the preceding forecast step. Not available at lead time 0.	degree_Celsius
medium_cloud_cover Medium cloud cover (`mcc`)	percent
minimum_temperature_2m Minimum temperature (`tmin`)Minimum since the preceding forecast step. Not available at lead time 0.	degree_Celsius
most_unstable_convective_available_potential_energy_surface Most-unstable CAPE (`mucape`)	J kg-1
net_long_wave_radiation_run_total_atmosphere_top Top net long-wave (thermal) radiation (`ttr`)Positive downwards, the negative of outgoing long-wave radiation. Accumulated since forecast initialization.	W s m-2
net_long_wave_radiation_run_total_surface Surface net long-wave (thermal) radiation (`str`)Accumulated since forecast initialization.	W s m-2
net_short_wave_radiation_run_total_surface Surface net short-wave (solar) radiation (`ssr`)Accumulated since forecast initialization.	W s m-2
northward_turbulent_surface_stress_run_total_surface Time-integrated northward turbulent surface stress (`nsss`)Accumulated since forecast initialization.	Pa s
precipitable_water_atmosphere Precipitable water (`pwat`)	kg m-2
precipitation_rate_surface Precipitation rate (`prate`)	kg m-2 s-1
pressure_reduced_to_mean_sea_level Pressure reduced to MSL (`prmsl`)	Pa
pressure_surface Surface pressure (`sp`)	Pa
runoff_water_equivalent_run_total_surface Runoff water equivalent (surface plus subsurface) (`rowe`)Accumulated since forecast initialization.	kg m-2
sea_ice_thickness_surface Sea ice thickness (`sithick`)NaN where there is no ocean model value, including over land.	m
sea_surface_height Sea surface height (`zos`)NaN where there is no ocean model value, including over land.	m
sea_water_velocity_u_surface Eastward surface sea water velocity (`sve`)Applies over the ocean only; over land the values are NaN or near zero, so mask with land_sea_mask_surface.	m s-1
sea_water_velocity_v_surface Northward surface sea water velocity (`svn`)Applies over the ocean only; over land the values are NaN or near zero, so mask with land_sea_mask_surface.	m s-1
skin_temperature_surface Skin temperature (`skt`)	degree_Celsius
slope_of_sub_gridscale_orography_surface Slope of sub-gridscale orography (`slor`)Available for control member 0 only; members 1-50 are NaN. Time-invariant field published at lead time 0 only.	1
snow_albedo_surface Snow albedo (`asn`)Applies only where snow is present (snow_water_equivalent_surface > 0); snow-free cells hold placeholder values that also occur over snow.	percent
snow_density_surface Snow density (`rsn`)Applies only where snow is present (snow_water_equivalent_surface > 0); snow-free cells hold placeholder values that also occur over snow.	kg m-3
snow_water_equivalent_surface Snow depth water equivalent (`sd`)Capped at 10 m; most of the Antarctic and Greenland ice sheets sit at this cap.	m
snowfall_water_equivalent_run_total_surface Snowfall water equivalent (`sf`)Accumulated since forecast initialization.	kg m-2
soil_temperature_layer_1 Soil temperature (`sot`)ECMWF soil level 1, the uppermost soil layer. Over water this is the sea surface temperature, not a soil temperature.	degree_Celsius
soil_temperature_layer_2 Soil temperature (`sot`)ECMWF soil level 2, the second soil layer from the surface. Over water this is the sea surface temperature, not a soil temperature.	degree_Celsius
soil_temperature_layer_3 Soil temperature (`sot`)ECMWF soil level 3. Over water this is the sea surface temperature, not a soil temperature.	degree_Celsius
soil_temperature_layer_4 Soil temperature (`sot`)ECMWF soil level 4. Over water this is the sea surface temperature, not a soil temperature.	degree_Celsius
standard_deviation_of_sub_gridscale_orography_surface Standard deviation of sub-gridscale orography (`sdor`)Available for control member 0 only; members 1-50 are NaN. Time-invariant field published at lead time 0 only.	m
temperature_2m 2 metre temperature (`2t`)	degree_Celsius
total_cloud_cover_atmosphere Total cloud cover (`tcc`)	percent
total_column_cloud_ice_water_atmosphere Total column cloud ice water (`tciw`)	kg m-2
total_column_cloud_liquid_water_atmosphere Total column cloud liquid water (`tclw`)	kg m-2
total_column_rain_water_atmosphere Total column rain water (`tcrw`)	kg m-2
total_column_water_atmosphere Total column water (`tcw`)	kg m-2
total_precipitation_run_total_surface Total precipitation (`tp`)Accumulated since forecast initialization.	kg m-2
volumetric_soil_moisture_layer_1 Volumetric soil moisture (`vsw`)ECMWF soil level 1, the uppermost soil layer. Applies over land only; over water the values are near zero, so mask with land_sea_mask_surface.	1
volumetric_soil_moisture_layer_2 Volumetric soil moisture (`vsw`)ECMWF soil level 2, the second soil layer from the surface. Applies over land only; over water the values are near zero, so mask with land_sea_mask_surface.	1
volumetric_soil_moisture_layer_3 Volumetric soil moisture (`vsw`)ECMWF soil level 3. Applies over land only; over water the values are near zero, so mask with land_sea_mask_surface.	1
volumetric_soil_moisture_layer_4 Volumetric soil moisture (`vsw`)ECMWF soil level 4. Applies over land only; over water the values are near zero, so mask with land_sea_mask_surface.	1
wind_gust_10m Maximum 10 metre wind gust since previous post-processing (`10fg`)Maximum over the preceding hour through lead 90 hours, then since the preceding forecast step. Not available at lead time 0.	m s-1
wind_u_100m 100 metre U wind component (`100u`)	m s-1
wind_u_10m 10 metre U wind component (`10u`)	m s-1
wind_v_100m 100 metre V wind component (`100v`)	m s-1
wind_v_10m 10 metre V wind component (`10v`)	m s-1

Pressure Level (10 variables)

These variables live in the pressure_level Zarr group (e.g. pass group="pressure_level" to dynamical_catalog.open() or xr.open_zarr()).

Dimensions: init_time × lead_time × ensemble_member × latitude × longitude × pressure_level

variable	units
divergence Divergence (`d`)	s-1
geopotential Geopotential (`z`)Available for control member 0 only; members 1-50 are NaN.	m2 s-2
geopotential_height Geopotential height (`gh`)	m
relative_humidity Relative humidity (`r`)	percent
relative_vorticity Vorticity (relative) (`vo`)	s-1
specific_humidity Specific humidity (`q`)	1
temperature Temperature (`t`)	degree_Celsius
vertical_velocity Vertical velocity (`w`)	Pa s-1
wind_u U component of wind (`u`)	m s-1
wind_v V component of wind (`v`)	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 forecast data processed by dynamical.org from ECMWF Open Data.

Or ECMWF IFS ENS from dynamical.org.

Source

The source grib files this archive references are provided by ECMWF Open Data. Data is read directly from ECMWF's copy on Google Cloud (gs://ecmwf-open-data) and AWS Open Data (s3://ecmwf-forecasts).

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 AWS Open Data.

Chunks

This dataset is stored in Zarr format, which splits each variable into a grid of chunks — the smallest unit read from storage. 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
init_time	1 (6 hours)
lead_time	1 (3 hours)
ensemble_member	1
latitude	1801 (180.1°)
longitude	3600 (360°)
uncompressed	49.5 MiB

Validation report

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