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) · 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 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) |
import dynamical_catalog # dynamical-catalog>=0.7.0
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 | 0 | 50 | 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
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)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 |
dew_point_temperature_2m
2 metre dewpoint temperature (2d)
|
degree_Celsius |
downward_long_wave_radiation_flux_surface
Surface downward long-wave radiation flux (sdlwrf)
|
W m-2 |
downward_short_wave_radiation_flux_surface
Surface downward short-wave radiation flux (sdswrf)
|
W m-2 |
geopotential_height_500hpa
Geopotential height (gh)
|
m |
geopotential_height_850hpa
Geopotential height (gh)
|
m |
geopotential_height_925hpa
Geopotential height (gh)
|
m |
precipitation_surface
Precipitation rate (prate)Average precipitation rate since the previous forecast step. Units equivalent to mm/s.
|
kg m-2 s-1 |
pressure_reduced_to_mean_sea_level
Pressure reduced to MSL (prmsl)
|
Pa |
pressure_surface
Surface pressure (sp)
|
Pa |
temperature_2m
2 metre temperature (2t)
|
degree_Celsius |
temperature_850hpa
Temperature (t)
|
degree_Celsius |
temperature_925hpa
Temperature (t)
|
degree_Celsius |
total_cloud_cover_atmosphere
Total cloud cover (tcc)
|
percent |
wind_gust_10m
Maximum 10 metre wind gust since previous post-processing (10fg)
|
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 |
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 categorical_precipitation_type_surface and wind_gust_10m, which are filled with NaNs before 2024-11-13 UTC, and total_cloud_cover_atmosphere, which is filled with NaNs before 2025-11-21 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.
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 | 85 (363 hours) | 85 (363 hours) |
| ensemble_member | 51 | 51 |
| latitude | 32 (8°) | 320 (80°) |
| longitude | 32 (8°) | 320 (80°) |
| uncompressed | 16.9 MiB | 1.7 GiB |
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.