ECMWF IFS ENS forecast, 15 day, 0.25 degree

Spatial domain	Global
Spatial resolution	0.25 degrees (~20km)
Time domain	Forecasts initialized 2024-04-01 00:00:00 UTC to Present
Time resolution	Forecasts initialized every 24 hours
Forecast domain	Forecast lead time 0-360 hours (0-15 days) ahead
Forecast resolution	Forecast step 0-144 hours: 3 hourly, 144-360 hours: 6 hourly

STAC (browse)

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 past and present ECMWF IFS 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. Along the lead_time dimension, each forecast begins at a 3 hourly forecast step (0-144 hours) and switches to a 6 hourly step for days 6 through 15 of the forecast (hours 144-360). This dataset contains the 00 UTC initialization times only.

Examples

	Quickstart (Github)
	Quickstart (Colab)

dynamical.org - ECMWF IFS ENS forecast, 15 day...

Maximum temperature in ensemble

import dynamical_catalog  # dynamical-catalog>=0.5.0

ds = dynamical_catalog.open("ecmwf-ifs-ens-forecast-15-day-0-25-degree")
ds["temperature_2m"].sel(init_time="2025-01-01T00", latitude=0, longitude=0).max().compute()

Dimensions

	min	max	units
ensemble_member		Present	1

init_time	2024-04-01T00:00:00Z	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
categorical_precipitation_type_surface (`ptype`) Precipitation type 0=No precipitation; 1=Rain; 2=Thunderstorm; 3=Freezing rain; 4=Mixed/ice; 5=Snow; 6=Wet snow; 7=Mixture of rain and snow; 8=Ice pellets; 9=Graupel; 10=Hail; 11=Drizzle; 12=Freezing drizzle; 13=Hail (less than 5 mm); 14=Hail (greater than or equal to 5 mm); 15-191=Reserved; 192-254=Reserved for local use; 255=Missing	1	init_time × lead_time × ensemble_member × latitude × longitude

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_gust_10m (`10fg`) Maximum 10 metre wind gust since previous post-processing	m s-1	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 [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 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.

Data availability

This dataset contains only forecasts initialized on or after 2024-04-01, which are available at the open data 0.25 degree (~20km) resolution. All variables are available for the full period, save for precipitation_surface, which is filled with NaNs before 2024-11-13 UTC.

Ensemble members

Each forecast contains 51 ensemble members, including a control member (0) and 50 perturbed members (1-50). The control forecast is produced with the best available data and unperturbed models. The other 50 members are each produced with slight perturbations of initial conditions and of the models. Taken together, ensemble of 51 forecasts shows the range of possible outcomes and the likelihood of their occurrence.

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.

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.