RESEARCH & SOURCES

Neural general circulation models for weather and climate

Nature · 2024.07.22CAS Tier 1 · Top (2025)

Paper authors：Dmitrii Kochkov · Janni Yuval · Ian Langmore · Peter Norgaard · Jamie Smith · Griffin Mooers · Milan Klöwer · James Lottes · Stephan Rasp · Peter Düben · Sam Hatfield · Peter Battaglia · Alvaro Sanchez-Gonzalez · Matthew Willson · Michael P. Brenner · Stephan Hoyer

DOI: 10.1038/s41586-024-07744-y

Earth systems Weather & climate

AI reading guide

AI summaryCombines a differentiable dynamical core with neural-network parameterisations to study weather forecasting and climate simulation, including physical consistency over long integrations.

This reading guide is not the authors’ abstract. Consult the original source.

This record provides source metadata. A detailed editorial analysis has not yet been published.

Read the original source ↗

CAS 2025 major-category tier 1 and Top. This journal-level label does not assess this individual paper.

Related research

Research articleNatureCAS Tier 1 · Top (2025)2025.03.20

End-to-end data-driven weather predictionOriginal title · English

Authors Anna Allen · Stratis Markou · Will Tebbutt · James Requeima · Wessel P. Bruinsma · Tom R. Andersson · Michael Herzog · Nicholas D. Lane · Matthew Chantry · J. Scott Hosking · Richard E. Turner

AI summaryTakes observations directly as input and produces global gridded and local station forecasts, connecting several stages of weather prediction in a trainable system.

AardvarkEnd-to-end learningWeather forecasting

Details & notes Read original ↗

Research articleNatureCAS Tier 1 · Top (2025)2025.05.21

A foundation model for the Earth systemOriginal title · English

Authors Cristian Bodnar · Wessel P. Bruinsma · Ana Lucic · Megan Stanley · Anna Allen · Johannes Brandstetter · Patrick Garvan · Maik Riechert · Jonathan A. Weyn · Haiyu Dong · Jayesh K. Gupta · Kit Thambiratnam · Alexander T. Archibald · Chun-Chieh Wu · Elizabeth Heider · Max Welling · Richard E. Turner · Paris Perdikaris

AI summaryPretrains on diverse geophysical data and adapts to forecasting weather, ocean waves, air quality and tropical cyclone tracks, exploring a foundation model for multiple Earth-system tasks.

AuroraFoundation modelsMultitask forecasting

Details & notes Read original ↗

PerspectiveNature Reviews Earth & EnvironmentCAS Tier 1 · Top (2025)2023.07.11

Differentiable modelling to unify machine learning and physical models for geosciencesOriginal title · English

Authors Chaopeng Shen · Alison P. Appling · Pierre Gentine · Toshiyuki Bandai · Hoshin Gupta · Alexandre Tartakovsky · Marco Baity-Jesi · Fabrizio Fenicia · Daniel Kifer · Li Li · Xiaofeng Liu · Wei Ren · Yi Zheng · Ciaran J. Harman · Martyn Clark · Matthew Farthing · Dapeng Feng · Praveen Kumar · Doaa Aboelyazeed · Farshid Rahmani · Yalan Song · Hylke E. Beck · Tadd Bindas · Dipankar Dwivedi · Kuai Fang · Marvin Höge · Chris Rackauckas · Binayak Mohanty · Tirthankar Roy · Chonggang Xu · Kathryn Lawson

AI summaryReviews how differentiable computation can jointly train physical components and neural networks to support process understanding, parameter estimation and geoscientific prediction.

Differentiable modellingPhysical constraintsHydrological modelling

Details & notes Read original ↗

PerspectiveCommunications Earth & EnvironmentCAS Tier 1 · Top (2025)2026.01.08

On the foundations of Earth foundation modelsOriginal title · English

Authors Xiao Xiang Zhu · Zhitong Xiong · Yi Wang · Adam J. Stewart · Konrad Heidler · Yuanyuan Wang · Zhenghang Yuan · Thomas Dujardin · Qingsong Xu · Yilei Shi

AI summaryProposes 11 features of an ideal Earth foundation model, including multisensor integration, awareness of location and scale, and physical consistency, and discusses evaluation and research directions.

Earth observationFoundation modelsEvaluation

Details & notes Read original ↗