Evaluating Machine Learning Weather Models for Data Assimilation: Fundamental Limitations in Tangent Linear and Adjoint Properties

Abstract Machine learning (ML) weather models like GraphCast and NeuralGCM show forecasting promise but face fundamental limitations for data assimilation (DA) integration. This study reveals critical problems in error covariance representation and adjoint sensitivity patterns challenging their oper...

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Detalhes bibliográficos

Principais autores:	Xiaoxu Tian, Daniel Holdaway, Daryl Kleist
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Wiley 2026-01-01
coleção:	Geophysical Research Letters
Assuntos:	machine learning weather models data assimilation tangent linear models adjoint sensitivity error covariance GraphCast
Acesso em linha:	https://doi.org/10.1029/2025GL119402
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