Insights from the AI Weather Quest: An international machine-learning competition for sub-seasonal prediction

Recent advances in machine learning (ML) illustrate the potential for significant improvements in weather predictive skill. At the same time, ML-based technologies have broadened the range of organisations capable of delivering skilful atmospheric forecasts. Given these developments, the ECMWF AI Weather Quest was designed as an open and transparent international competition, enabling a range of organisations to submit ML-based subseasonal forecasts. With participation from more than 40 competing teams spanning academia, public institutions and private companies, the Quest provides a unique framework for systematically evaluating and comparing multiple ML-based subseasonal forecasting systems.    

Participants of the AI Weather Quest submit global probabilistic quintile forecasts of near-surface temperature, mean sea level pressure, and precipitation at either a 3- or 4-week lead time in an operational-style forecasting environment. This set-up has encouraged model development whilst challenging participants to develop forecasting systems that operate in realistic settings and deliver actionable forecast parameters.

In this presentation we will provide an overview of the AI Weather Quest design and compare subseasonal forecast skill of both ML-based and dynamical prediction systems. Additionally, we will highlight emerging approaches in ML-based post-processing and fully data-driven forecasting. During the first three-month competitive season, ML-based post-processing of dynamical forecasts achieved the highest skill, highlighting contrasting performance across approaches and underscoring the need for further development in both dynamical and ML-based forecasting. We will also share opportunities for wider engagement and discuss future developments planned for the ECMWF AI Weather Quest.