Welcome to the AI Weather Quest confluence hub.

The AI Weather Quest (AI WQ), organised by the European Centre for Medium-Range Weather Forecasts (ECMWF), is an ambitious international competition designed to harness artificial intelligence (AI) and machine learning (ML) in advancing weather forecasting. It challenges participants to produce and submit sub-seasonal weather forecasts – covering the critical weeks between medium-range and seasonal predictions – using AI/ML models.

This confluence hub will share the latest preliminary analysis on AI-based sub-seasonal forecasts, multiple detailed how-to guides including using the forecast portal and AI WQ leaderboards, and an opportunity to develop plans after the AI WQ.

Important links:

Competition overview:

Teams are challenged to submit weekly, real-time sub-seasonal forecasts of at least one of the following variables:
  • Near-surface (2m) temperature (tas)
  • Mean sea level pressure (mslp)
  • Precipitation (pr)

For temperature and pressure forecasts, teams are required to produce weekly averages, which will be evaluated against weekly means calculated using six-hourly data (00, 06, 12 and 18 UTC). For precipitation forecasts, the focus is on weekly accumulations, which will be compared against corresponding reanalysis totals.

Forecasts should include global, quintile probabilities at a 1.5-degree latitude/longitude resolution for one of the following lead times (inclusive):

  • Days 19 to 25 (week 3)
  • Days 26 to 32 (week 4)

To ensure flexibility for AI/ML innovation, participants can:

  • Submit up to six forecasted variables per AI model (three variables × two lead times).
  • Use up to three different AI/ML models, allowing a maximum of 18 submissions per team each week.
  • Develop AI/ML models using any observational or forecast datasets (which may include ECMWF-supported datasets).
  • Develop AI/ML models using any programming language.

Submissions are welcome from various types of ML/AI models, including (but not limited to):

  • Models that post-process numerical weather prediction data.
  • Machine-learning based models specifically designed for weather prediction.
  • Statistical models that focus primarily on generating quintile probabilities.
  • Hybrid models that combine physical simulations with machine-learning techniques.

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