EGU24-17158, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

AIFS – ECMWF’s Data-Driven Probabilistic Forecasting 

Zied Ben Bouallegue1, Mihai Alexe2, Matthew Chantry1, Mariana Clare2, Jesper Dramsch2, Simon Lang1, Christian Lessig2, Linus Magnusson1, Ana Prieto Nemesio2, Florian Pinault2, Baudouin Raoult1, and Steffen Tietsche2
Zied Ben Bouallegue et al.
  • 1ECMWF, Reading, UK
  • 2ECMWF, Bonn, Germany

In just two years, the idea of an operational data-driven system for medium-range weather forecasting has been transformed from dream to very real possibility. This has occurred through a series of publications from innovators, which have rapidly improved deterministic forecast skill. Our own evaluation confirms that these forecasts have comparable deterministic skill to NWP models across a range of variables. However, on medium-range timescales probabilistic forecasting, typically achieved through ensembles, is key for providing actionable insights to users. ECMWF is building on top of these recent works to develop a probabilistic forecasting system, AIFS. We will showcase results from our progress towards this system and outline our roadmap to operationalisation.

How to cite: Ben Bouallegue, Z., Alexe, M., Chantry, M., Clare, M., Dramsch, J., Lang, S., Lessig, C., Magnusson, L., Prieto Nemesio, A., Pinault, F., Raoult, B., and Tietsche, S.: AIFS – ECMWF’s Data-Driven Probabilistic Forecasting , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17158,, 2024.