[2604.03300] AIFS-COMPO: A Global Data-Driven Atmospheric Composition Forecasting System

Physics > Atmospheric and Oceanic Physics arXiv:2604.03300 (physics) [Submitted on 29 Mar 2026] Title:AIFS-COMPO: A Global Data-Driven Atmospheric Composition Forecasting System Authors:Paula Harder, Johannes Flemming, Mihai Alexe, Gert Mertes, Baudouin Raoult, Matthew Chantry View a PDF of the paper titled AIFS-COMPO: A Global Data-Driven Atmospheric Composition Forecasting System, by Paula Harder and 5 other authors View PDF HTML (experimental) Abstract:We introduce AIFS-COMPO, a skilful medium-range data-driven global forecasting system for aerosols and reactive gases. Building on the ECMWF Artificial Intelligence Forecast System (AIFS), AIFS-COMPO employs a transformer-based encoder-processor-decoder architecture to jointly model meteorological and atmospheric composition variables. The model is trained on Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, analysis, and forecast data to learn the coupled dynamics of weather, emissions, transport, and atmospheric chemistry. We evaluate AIFS-COMPO against a range of atmospheric composition observations and compare its performance with the operational CAMS global forecasting system IFS-COMPO. The results show that AIFS-COMPO achieves comparable or improved forecast skill for several key species while requiring only a fraction of the computational resources. Furthermore, the efficiency of the approach enables forecasts beyond the current operational horizon, demonstrating the potential of AI-based systems for fast...