| 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 6 hours |
| Forecast domain | Forecast lead time 0-360 hours (0-15 days) ahead |
| Forecast resolution | 6 hourly |
The Artificial Intelligence Forecasting System (AIFS) is a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). This is the non-ensemble configuration of AIFS that produces a single forecast trace. AIFS is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses.
This dataset is an archive of past and present ECMWF AIFS Single forecasts. Forecasts are identified by an initialization time (init_time) denoting the start time of the model run. Each forecast steps forward in time along the lead_time dimension, from 0 to 360 hours (15 days) at a 6 hourly step.
| Quickstart (Github) | |
| Quickstart (Colab) | |
| Heating degree days: GFS vs AIFS (Github) | |
| Heating degree days: GFS vs AIFS (Colab) |
import dynamical_catalog # dynamical-catalog>=0.5.0
ds = dynamical_catalog.open("ecmwf-aifs-single-forecast")
ds["temperature_2m"].sel(init_time="2025-01-01T00", latitude=0, longitude=0).max().compute()
| min | max | units | |
|---|---|---|---|
| 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 |
| units | dimensions | |
|---|---|---|
dew_point_temperature_2m (2d)
|
degree_Celsius | init_time × lead_time × latitude × longitude |
downward_long_wave_radiation_flux_surface (sdlwrf)
|
W m-2 | init_time × lead_time × latitude × longitude |
downward_short_wave_radiation_flux_surface (sdswrf)
|
W m-2 | init_time × lead_time × latitude × longitude |
geopotential_height_500hpa (gh)
|
m | init_time × lead_time × latitude × longitude |
geopotential_height_850hpa (gh)
|
m | init_time × lead_time × latitude × longitude |
geopotential_height_925hpa (gh)
|
m | init_time × lead_time × latitude × longitude |
precipitation_surface (prate)
|
kg m-2 s-1 | init_time × lead_time × latitude × longitude |
pressure_reduced_to_mean_sea_level (prmsl)
|
Pa | init_time × lead_time × latitude × longitude |
pressure_surface (sp)
|
Pa | init_time × lead_time × latitude × longitude |
temperature_2m (2t)
|
degree_Celsius | init_time × lead_time × latitude × longitude |
temperature_850hpa (t)
|
degree_Celsius | init_time × lead_time × latitude × longitude |
temperature_925hpa (t)
|
degree_Celsius | init_time × lead_time × latitude × longitude |
total_cloud_cover_atmosphere (tcc)
|
percent | init_time × lead_time × latitude × longitude |
wind_u_100m (100u)
|
m s-1 | init_time × lead_time × latitude × longitude |
wind_u_10m (10u)
|
m s-1 | init_time × lead_time × latitude × longitude |
wind_v_100m (100v)
|
m s-1 | init_time × lead_time × latitude × longitude |
wind_v_10m (10v)
|
m s-1 | init_time × lead_time × latitude × longitude |
Dataset licensed under CC BY 4.0 and ECMWF Terms of Use.
ECMWF AIFS Single forecast data processed by dynamical.org from ECMWF Open Data.
Or ECMWF AIFS Single from dynamical.org.
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.
AIFS is updated regularly. Find details of recent and upcoming changes to the forecasting system on the ECMWF website.
Storage for this dataset is generously provided by Source Cooperative, a Radiant Earth initiative. Icechunk storage generously provided by AWS Open Data.
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.