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<p>Data assimilation (DA) combines observations with numerical models to estimate evolving Earth system states for forecasting and monitoring. Machine learning (ML) enables surrogate modeling, pattern recognition and Bayesian inference. These fields are converging: ML accelerates DA, while DA provides uncertainty quantification and physical constraints. Hybrid DA-ML systems are promising, yet challenges persist in generalization, consistency and reproducibility. These approaches are increasingly integrated, shaping next-generation prediction systems and observing networks.</p>

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The convergence of machine learning and data assimilation in Earth system science

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月23日

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  • Rossella Arcucci,
  • Sean Healy,
  • Sarah Dance,
  • Lili Lei,
  • Eviatar Bach,
  • Anthony T. Weaver,
  • Takemasa Miyoshi,
  • Maria Eugenia Dillon,
  • Clara Draper,
  • Rochelle Schneider,
  • Simon Lang,
  • Peter Dueben,
  • Niels Bormann,
  • Peter Lean,
  • Alan Geer,
  • Massimo Bonavita,
  • Peter Jan van Leeuwen,
  • Sibo Cheng,
  • Marc Bocquet,
  • Nedjeljka Zagar,
  • Haroldo Fraga de Campos Velho,
  • Juan Jose Ruiz,
  • Peter Bauer,
  • Sid Ahmed Boukabara,
  • Alberto Carrassi,
  • Russ Treadon,
  • Andrew Collard,
  • Daryl Kleist,
  • Azadeh Gholoubi,
  • Xuguang Wang,
  • Nahidul Samrat,
  • Gemma Ralton,
  • Andrew M. Moore,
  • Katia Lamer,
  • Nico Caltabiano

摘要

Data assimilation (DA) combines observations with numerical models to estimate evolving Earth system states for forecasting and monitoring. Machine learning (ML) enables surrogate modeling, pattern recognition and Bayesian inference. These fields are converging: ML accelerates DA, while DA provides uncertainty quantification and physical constraints. Hybrid DA-ML systems are promising, yet challenges persist in generalization, consistency and reproducibility. These approaches are increasingly integrated, shaping next-generation prediction systems and observing networks.