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Forecast-based attribution of extratropical cyclones using AI weather models
- Truvace record
- TRV-2026-2629raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
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Abstract Extratropical cyclones are among the most damaging weather systems in midlatitudes; however, quantifying the influence of anthropogenic climate change (ACC) on individual storms remains challenging due to the interplay between thermodynamic and dynamical processes. Here, we assess the capacity of artificial intelligence-based weather prediction (AIWP) models to both forecast and attribute two high-impact European extratropical cyclones with contrasting characteristics: storm Ciarán (November 2023), whic…
The gain
In a study of two European storms, four AI weather models reproduced the large-scale evolution several days ahead and supported event-specific, computationally efficient attribution of human-driven climate change within the forecast window.
What this doesn’t fix
AI model performance was event-dependent, and wind and circulation attribution responses varied more between models than moisture signals did.
Sources
1 on file- Environmental Research: Climatedoi:10.1088/2752-5295/ae714b
Cite this record
v1Truvace Impact Record TRV-2026-2629, v1: “Forecast-based attribution of extratropical cyclones using AI weather models.” Truvace, 2026-11-12. /record/TRV-2026-2629 (accessed on the day you copy it). sha256 e24afc9271281e5f…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
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