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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Bibliografske podrobnosti

Principais autores:	Xiaoxu Tian, Daniel Holdaway, Daryl Kleist
Format:	Artigo
Jezik:	Inglês
Izdano:	Wiley 2026-01-01
Serija:	Geophysical Research Letters
Teme:	machine learning weather models data assimilation tangent linear models adjoint sensitivity error covariance GraphCast
Online dostop:	https://doi.org/10.1029/2025GL119402
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