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Atmospheric Rivers in Machine Learning Weather Models
- Truvace record
- TRV-2026-2620raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
- sha256
- ef991fff6928…check it
Abstract This study examines the representation of atmospheric rivers (ARs) in medium-range forecasts (1–10-day lead time) from machine learning weather prediction (MLWP) models. ARs pose high-impact consequences, both in terms of flooding and their contributions to water resources. As MLWP models proliferate, there is a pressing need to understand MLWP model skill for AR prediction. ARs provide a useful test case for MLWP models because they are filamentary features exhibiting large gradients in thermodynamic a…
The problem
In a comparison of GraphCast and Pangu-Weather with NOAA's GFS and ECMWF's IFS, researchers found the AI models' atmospheric rivers were slightly too weak and too small versus reanalysis and traditional models.
What this doesn’t fix
The evaluation covers only two deterministic AI models and wintertime atmospheric rivers in the northern Pacific, so results may not extend to other models, regions or seasons.
Sources
1 on file- Weather and Forecastingdoi:10.1175/waf-d-25-0142.1
Cite this record
v1Truvace Impact Record TRV-2026-2620, v1: “Atmospheric Rivers in Machine Learning Weather Models.” Truvace, 2026-11-12. /record/TRV-2026-2620 (accessed on the day you copy it). sha256 ef991fff69289326…
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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