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

npj Artificial Intelligence 2026 Open access

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Authors: Rossella Arcucci, Sean Healy, Sarah Dance, Lili Lei, Eviatar Bach, Anthony Weaver, Takemasa Miyoshi, María Eugenia Dillon, Clara Draper, Rochelle Schneider, Simon Lang, Peter Dueben
+23 more Niels Bormann, Peter Lean, Alan Geer, Massimo Bonavita, Peter Jan van Leeuwen, Sibo Cheng, Marc Bocquet, Nedjeljka Žagar, Haroldo Fraga de Campos Velho, Juan José 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
DOI: 10.1038/s44387-026-00107-0
Cited by: 3
References: 57

Abstract

Abstract 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.

Fields of Study & Subjects

Meteorological Phenomena and Simulations Climate variability and models Tropical and Extratropical Cyclones Research

Publication details

Journal / source
npj Artificial Intelligence
Publisher
Springer Science and Business Media LLC
Volume
2
Issue
1
Publication date
23 April 2026
Language
EN