Weather prediction has rapidly changed in recent years with the emergence of forecasting systems that leverage artificial intelligence. Such AI models display an impressive computational speed-up of weather forecasts compared with traditional models. New research published in Weather assessed the energy consumption, and therefore the carbon footprint, of such weather forecasting models.
Investigators found that the training aspect of AI models consumes considerable energy, but this consumption is offset by the models’ rapid forecasting ability compared with traditional models. Considering one-year usage, AI data-driven models are estimated to consume at least 21 times less energy than traditional models. The findings suggest opportunities to significantly reduce the carbon footprint of weather forecasting.
“This study provides simple orders of magnitude on the energy consumption of AI in meteorology,” said corresponding author Thomas Rieutord, PhD, who conducted this work while at Met Éireann, in Ireland, and is currently at the Centre National de Recherche Meteorologique, in France. “We hope there will be future studies on the topic to provide more accurate estimates, so that developers of future weather models will have energy consumption reduction as a target, alongside with models' performances.”
URL upon publication: https://onlinelibrary.wiley.com/doi/10.1002/wea.70035
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About the Journal
Weather publishes articles written for a broad audience, including those having a professional and a general interest in the weather, as well as those working in related fields such as climate science, oceanography, hydrometeorology and other related atmospheric and environmental sciences.
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Weather
Energy and carbon footprint considerations for data-driven weather forecasting models
11-Mar-2026