AIFS Single 1.1.0: an update to ECMWF's machine-learned weather forecast model AIFS

Moldovan, Gabriel; Pinnington, Ewan; Prieto Nemesio, Ana; Lang, Simon; Ben Bouallègue, Zied; Dramsch, Jesper; Alexe, Mihai; Santa Cruz, Mario; Hahner, Sara; Cook, Harrison; Theissen, Helen; Clare, Mariana; O'Brien, Cathal; Polster, Jan; Magnusson, Linus; Mertes, Gert; Pinault, Florian; Raoult, Baudouin; de Rosnay, Patricia; Forbes, Richard; Chantry, Matthew

doi:10.5194/gmd-19-4703-2026

Articles | Volume 19, issue 10

https://doi.org/10.5194/gmd-19-4703-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-4703-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 10

Model description paper

|

01 Jun 2026

Model description paper |

| 01 Jun 2026

AIFS Single 1.1.0: an update to ECMWF's machine-learned weather forecast model AIFS

Gabriel Moldovan, Ewan Pinnington, Ana Prieto Nemesio, Simon Lang, Zied Ben Bouallègue, Jesper Dramsch, Mihai Alexe, Mario Santa Cruz, Sara Hahner, Harrison Cook, Helen Theissen, Mariana Clare, Cathal O'Brien, Jan Polster, Linus Magnusson, Gert Mertes, Florian Pinault, Baudouin Raoult, Patricia de Rosnay, Richard Forbes, and Matthew Chantry

Data sets

Open data ECMWF https://doi.org/10.21957/OPEN-DATA

ERA5 hourly data on single levels from 1940 to present Hans Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

Model code and software

AIFS 1.1.0: Permanent Archive of Checkpoints and Source Code for Training and Inference European Centre for Medium-Range Weather Forecasts https://doi.org/10.5281/ZENODO.17349820

aifs-single-1.1 (Revision 7976552) ECMWF https://doi.org/10.57967/hf/6415

Short summary

We present the latest release of the Artificial Intelligence Forecasting System, AIFS 1.1.0, which shows improved headline forecasting skill through an expanded dataset and enhanced training schedule. The model also incorporates hard physical constraints that facilitate training and improve rainfall prediction. Finally, we extend the set of forecasted variables to include soil conditions and energy-related fields, strengthening the operational value of AIFS.