Sustainability & AI
AI weather forecasting
Machine-learning models that generate weather forecasts, increasingly matching or extending conventional numerical methods.
Definition
Machine-learning models that predict weather from atmospheric data, now matching or exceeding conventional numerical prediction on key metrics at a fraction of the computing cost.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
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Overview
What it means
Landmark systems include GraphCast (Science 2023) and Pangu-Weather (Nature 2023), trained on decades of reanalysis data. The field crossed into operations in February 2025, when ECMWF began running its machine-learning model AIFS alongside its physics-based system, adding an ensemble version later that year.
How it is used
Forecasts support renewable-energy scheduling, agriculture, disaster preparedness and logistics. AI systems generate medium-range global forecasts in seconds on modest hardware, enabling wider access and experimentation by national meteorological services.
Why it matters
Weather prediction is the first planetary-scale physical system where AI has matched supercomputing incumbents — a proof point for AI-for-climate claims, and a template for hybrid physical–statistical modelling elsewhere in Earth science.
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