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

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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

	Open notebook in github
	Open notebook in colab

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

Maximum temperature in ensemble

import xarray as xr  # xarray>=2025.1.2 and zarr>=3.0.8 for zarr v3 support

ds = xr.open_zarr("https://data.dynamical.org/ecmwf/ifs-ens/forecast-15-day-0-25-degree/[email protected]")
ds["temperature_2m"].sel(init_time="2025-01-01T00", latitude=0, longitude=0).max().compute()

Dimensions

	min	max	units
ensemble_member	0	50	realization

init_time	2024-04-01T00:00:00	Present	seconds since 1970-01-01

latitude	-90	90	degrees_north

lead_time	0 days 00:00:00	15 days 00:00:00	seconds

longitude	-180	179.75	degrees_east

Variables

	units	dimensions
categorical_precipitation_type_surface Categorical precipitation type at surface	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; 13-191=Reserved; 192-254=Reserved for local use; 255=Missing	init_time × lead_time × ensemble_member × latitude × longitude

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

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

expected_forecast_length	seconds	init_time

ingested_forecast_length	seconds	init_time

precipitation_surface Total precipitation Average precipitation rate since the previous forecast step.	mm/s	init_time × lead_time × ensemble_member × latitude × longitude

pressure_reduced_to_mean_sea_level Pressure reduced to MSL	Pa	init_time × lead_time × ensemble_member × latitude × longitude

pressure_surface Surface pressure	Pa	init_time × lead_time × ensemble_member × latitude × longitude

temperature_2m 2 metre temperature	C	init_time × lead_time × ensemble_member × latitude × longitude

valid_time	seconds since 1970-01-01	init_time × lead_time

wind_u_100m 100 metre U wind component	m/s	init_time × lead_time × ensemble_member × latitude × longitude

wind_u_10m 10 metre U wind component	m/s	init_time × lead_time × ensemble_member × latitude × longitude

wind_v_100m 100 metre V wind component	m/s	init_time × lead_time × ensemble_member × latitude × longitude

wind_v_10m 10 metre V wind component	m/s	init_time × lead_time × ensemble_member × latitude × longitude

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

Details

License

This data is based on data and products of the European Centre for Medium-Range Weather Forecasts (ECMWF). Use is governed by the CC-BY-4.0 license and the ECMWF Terms of Use.

Source

The source grib files this archive is contructed 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 meterial.

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.

Storage

Storage for this dataset is generously provided by Source Cooperative, a Radiant Earth initiative.

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.