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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): Once the last set of forecasts within a competition period have been evaluated, all submitted predictions from that phase are made openly available. Publication dates for each period are: 

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1. Forecast initialisation date (by_fc_date)
2. Participating team (by_team)

The following directory structures are followed:

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1. : by_fc_date/ -> by_team/ -> by_model/ -> forecasts
2. Participating team (by_team): by_team/ -> by_model/ -> by_fc_date/ -> forecasts

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

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• 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:

[variable]_[fc_initstart_date]_p[fc_window]_[teamname]_[modelname].nc 

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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_initstart_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.

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Extracting Archives

Finally, we it is recommend using that the following tar command is used to unzip zip files.

     tar -czf [zip_file] [new_directory]

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Forecasts are easy to open in a Python environment. We It is highly recommend using xarray to load in an individual forecastforecasts using array:

import xarray as xr
fc = xr.load_dataarray([filename])

We It is also recommend using that xarray is used to open multiple forecasts of the same variable and forecasting window from different models. As an example:

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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 Machine Learning: Earth,  11(1), p.010701. https://doi.org/10.1088/3049-4753/adf649