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arXiv · 2606.19093

AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning

Ewan Pinnington·Peter Lean·Mihai Alexe·Eulalie Boucher·Simon Lang·Patrick Laloyaux·Gert Mertes·Tomas Kral·Patricia de Rosnay·Matthew Chantry·Anthony McNally
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Abstract

We introduce the Artificial Intelligence Forecasting System for Direct Observation Prediction (AIFS-DOP). AIFS-DOP is trained on a 40-year harmonized dataset of gridded observations, without using numerical weather prediction (NWP) reanalysis or model data. The resulting model is competitive with ECMWF's Integrated Forecasting System (IFS) when scored on a one year period of forecasts across 2021/2022. This progress on Direct Observation Prediction represents the first time that a data-driven model, trained solely on observations, is competitive with the IFS at medium ranges for several key upper-air and surface headline scores, when verified against observation data.

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Ewan Pinnington, Peter Lean, Mihai Alexe, Eulalie Boucher, Simon Lang, Patrick Laloyaux, Gert Mertes, Tomas Kral, Patricia de Rosnay, Matthew Chantry, Anthony McNally. 2026-06-17. AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning. https://arxiv.org/abs/2606.19093

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