AI Weather Quest - A year of progress in AI-based sub-seasonal prediction - WEBINAR.

 

AI-based sub-seasonal prediction uses machine learning to forecast weather trends two weeks to two months ahead, overcoming the traditional accuracy drop past 10 to 14 days.

Why Sub-Seasonal AI Matters?
Filling the Gap: Traditional computer models lose skill beyond two weeks, but AI models process vast historical data to spot lingering atmospheric and oceanic patterns.
Speed and Cost: AI emulators generate long-range forecasts in a fraction of the time and computational power required by traditional supercomputer simulations.
Key Innovations: Frameworks like DeepMet and Probabilistic Bias Correction (PBC) reduce root-mean-square errors and double the skill of standard sub-seasonal lead times.

Major Initiatives
AI Weather Quest: An international competition organized by the European Centre for Medium-Range Weather Forecasts (ECMWF) to benchmark global AI/ML probabilistic forecasts for weeks 3 and 4.
Sector Applications: Industries like energy trading, agriculture, and water management rely on these advanced week 3–6 outlooks to prepare for sudden heat waves, cold snaps, or droughts.
Explore ongoing datasets at the ECMWF AI Weather Quest.

Forecasts for the weeks ahead inform disaster preparedness, agriculture, water management, energy supply, public health & humanitarian operations. Join WMO & ECMWF to explore how AI advances open new possibilities for sub-seasonal forecasting.

AI Weather Quest - A year of progress in AI-based sub-seasonal prediction - WEBINAR. Learn how to Register to participate!