All forecasts submitted to the AI Weather Quest are made openly available via the AI Weather Quest Data Portal. Participants are challenged to submit quintile-based probabilistic forecasts for near-surface air temperature, mean sea level pressure, and accumulated precipitation, targeting lead times of days 19 to 25 (week 3) and 26 to 32 (week 4). This guide is designed to support forecast download and use of individual files.
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, all submitted forecasts from that phase will be made openly available according to the timetable below (where each acronym refers to the forecasted months, i.e. SON is September, October, November):
To facilitate access, forecasts are organised into directories that can be browsed by either:
The following directory structures is followed:
A separate NetCDF file is provided for each forecasted variable and lead time submitted by a team.
For more efficient downloading, we also provide .zip archives containing all forecasts either:
A separate NetCDF file is provided for each forecasted variable submitted by a team. Files are named using the following naming convention:
[variable]_[fc_init_date]_p[fc_window]_[teamname]_[modelname].nc
where:
The following teams webpage contains Information regarding individual teams and their associated models.
Downloading either a set (zip files) or individual forecast through a web browser is simple. Just click on a chosen file and it will automatically download.
This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full licence terms can viewed here.
For support with accessing AI WQ sub-seasonal forecast data, please use one of the following communication channels:
When using this dataset, please cite the dataset DOI (provided below) and the following reference:
Loegel, O., Talib, J., Vitart, F., Hoffmann, J. and Chantry, M., 2025. The AI Weather Quest: an international competition for sub-seasonal forecasting with AI. Machine Learning: Earth, 1(1), p.010701.