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Overview

All forecasts submitted to the AI Weather Quest are openly available via the AI Weather Quest Data Portal. Participants submit quintile-based probabilistic forecasts on a 1.5 degree latitude/longitude grid for near-surface air temperature (tas), mean sea level pressure (mslp), and accumulated precipitation (pr), targeting lead times of days 19 to 25 (week 3) and 26 to 32 (week 4). This guide explains how to download, extract, and use individual forecast files or archives.

The following teams webpage contains information regarding individual teams and their associated models.

Data publication timetable

The first year of the AI Weather Quest is divided into four 13-week competitive periods. After each period, and following the end-of-period webinar and forecast evaluation, submitted forecasts are made publicly available following the schedule below (where each acronym refers to the forecasted months, i.e. SON is September, October, November):

Portal directory structure

Individual files

Forecasts are organised in two ways:

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Each forecast variable and lead time is stored as a separate NetCDF (.nc) file.

Zip/Tar Archives

For simplify downloading, we also provide .tar.gz archives containing all forecasts either:

• submitted by a given team (by_team_zipfiles)
• issued for a given initialisation date (by_fc_date_zipfiles) 

Filename conventions

Each forecast file is in a NetCDF format with the following naming convention:

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• variable: The forecasted variable. Options are:
  • 'tas': Near-surface temperature (weekly-mean)
  • 'mslp': Mean sea level pressure (weekly_mean)
  • 'pr': Precipitation (weekly-accumulated)
• fc_init_date: The forecast initialisation date in format YYYYMMDD (e.g., ‘20250515’ for 15th May 2025).
• fc_window: The selected forecasting window. Valid options are:
  • '1': Weekly-mean forecasts for days 19 to 25 inclusive.
  • '2': Weekly-mean forecasts for days 26 to 32 inclusive.
• teamname : The associated team name with the submitted forecast.
• modelname: The associated model name with the submitted forecast.

Forecast download

Via the web browser

Click on individual files or zip/tar archives to download. Files will download automatically.

Via Linux/Unix terminal

To download forecasts in a Linux environment we recommend using wget or curl. When using either set of functions you will require the top-level URL of the portal (https://data.ecmwf.int/ai-weatherquest/) and the full directory path to the requested file.

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     wget https://data.ecmwf.int/ai-weatherquest/by_team_zipfiles/AIFS.tar.gz

Extracting Archives

Finally, we recommend using the following tar command to unzip zip files.

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     tar -czf AIFS.tar.gz AIFS_forecasts/

Forecast file content

All forecasts are saved in NetCDF files created using the AI_WQ_create_empty_dataarray function in the forecast_submission module of AI Weather Quest Python Package.

All data is stored in a fractional format.

Dimensions

The files include additional attributes to support file storage and forecast visualisation. Attributes important to forecast users include:

Examples of opening forecasts

Forecasts are easy to open in a Python environment. We highly recommend using xarray to load in an individual forecast:

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This output has an additionally dimension called modelname.

Licence information

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full licence terms can viewed here. 

Contact / support information

For support with accessing AI WQ sub-seasonal forecast data, please use one of the following communication channels:

• Public forum.
• Email: aiweatherquest@ecmwf.int

Dataset citation / DOI

When using this dataset, please cite the dataset DOI (provided below) and the following reference:

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