Hi everyone,
I’d like to share a project we’ve been working on at the European Centre for Medium-Range Weather Forecasts (ECMWF) — the AI Weather Quest. It’s an open, global initiative to benchmark AI/ML models for sub-seasonal forecasting, particularly targeting week 3 and 4 lead times (Days 19–25 and 26–32).
Forecasting at these timescales is notoriously difficult. This competition is designed to create a transparent, structured benchmark for AI-based sub-seasonal prediction, and is built entirely around open-source tools and data.
What it’s about:
Participants submit quintile probability forecasts at 1.5° resolution for:
- 2m temperature
- Precipitation
- Mean sea level pressure
Forecasts are evaluated using the Ranked Probability Skill Score (RPSS) and visualised on a public ECMWF-hosted portal.
We support:
- Any AI/ML methodology
- Any programming language
- Any dataset (with open historical datasets provided)
Teams of up to 10 people submit forecasts weekly across four 13-week competitive periods.
Open-Source Tools
We provide a fully open Python package to support participation:
Forecast submission formatting- ERA5-based training data retrieval
- RPSS-based evaluation tools
Currently: Testing JJA Period (May–August 2025)
We’re now entering a non-competitive testing period, where participants can:
- Submit weekly forecasts
- Run evaluations locally using the same scoring system
- Prepare for the competition phase starting in August
We’re hosting a Testing Period Launch Webinar on 7 May:
Thanks for reading — and hope to see some of you in the Quest!