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Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency.
Description of the NCAR Community Atmosphere Model (CAM 3.0)
William Collins, Philip Rasch, Byron Boville, James McCaa, David Williamson, Jeffrey Kiehl, Bruce Briegleb, Cecilia Bitz, S.-J. Lin, Minghua Zhang, and Youngjiu Dai · 2004
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An Introduction to Three-Dimensional Climate Modeling
Warren M. Washington and Claire L. Parkinson · 2005
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Finite-volume transport on various cubed-sphere grids
William M. Putman and Shian-Jiann Lin · 2007
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Atmospheric moisture transports from ocean to land and global energy flows in reanalyses
Kevin E. Trenberth, John T. Fasullo, and Jessica Mackaro · 2011
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On the need of intermediate complexity general circulation models: A “SPEEDY” example
Fred Kucharski, Franco Molteni, Martin P. King, Riccardo Farneti, In-Sik Kang, and Laura Feudale · 2013
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Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization
Veronika Eyring, Sandrine Bony, Gerald A. Meehl, Catherine A. Senior, Bjorn Stevens, Ronald J. Stouffer, and Karl E. Taylor · 2016
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G.E. Karniadakis · 2018
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Deep learning for multi-year ENSO forecasts
Yoo-Geun Ham, Jeong-Hwan Kim, and Jing-Jia Luo · 2019
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Toward convective-scale prediction within the next generation global prediction system
Linjiong Zhou, Shian-Jiann Lin, Jan-Huey Chen, Lucas M. Harris, Xi Chen, and Shannon L. Rees · 2019
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The ERA5 global reanalysis
Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, Adrian Simmons, Cornel Soci, Saleh Abdalla, Xavier Abellan, Gianpaolo Balsamo, Peter Bechtold, Gionata Biavati, Jean Bidlot, Massimo Bonavita, Giovanna Chiara, Per Dahlgren, Dick Dee, Michail Diamantakis, Rossana Dragani, Johannes Flemming, Richard Forbes, Manuel Fuentes, Alan Geer, Leo Haimberger, Sean Healy, Robin J. Hogan, Elías Hólm, Marta Janisková, Sarah Keeley, Patrick Laloyaux, Philippe Lopez, Cristina Lupu, Gabor Radnoti, Patricia Rosnay, Iryna Rozum, Freja Vamborg, Sebastien Villaume, and Jean-Noël Thépaut · 2020
Cited alongside, same era.
Unified forecast system (UFS)
UFS Community · 2020
Cited alongside, same era.
Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere
Jonathan A. Weyn, Dale R. Durran, and Rich Caruana · 2020
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Climate-invariant machine learning
Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng, Jerry Lin, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O’Gorman, J. David Neelin, Nicholas J. Lutsko, and Michael Pritchard · 2021
Graphcast: Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed, and Peter Battaglia · 2022
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Fourier neural operator with learned deformations for pdes on general geometries
Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, and Anima Anandkumar · 2022
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Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators
Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, Pedram Hassanzadeh, Karthik Kashinath, and Animashree Anandkumar · 2022
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ClimateBench v1.0: A benchmark for data-driven climate projections
D. Watson-Parris, Y. Rao, D. Olivié, Ø. Seland, P. Nowack, G. Camps-Valls, P. Stier, S. Bouabid, M. Dewey, E. Fons, J. Gonzalez, P. Harder, K. Jeggle, J. Lenhardt, P. Manshausen, M. Novitasari, L. Ricard, and C. Roesch · 2022
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Cited alongside, same era.
Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations
Christopher S. Bretherton, Brian Henn, Anna Kwa, Noah D. Brenowitz, Oliver Watt-Meyer, Jeremy McGibbon, W. Andre Perkins, Spencer K. Clark, and Lucas Harris · 2022
Cited alongside, same era.
Correcting a 200 km resolution climate model in multiple climates by machine learning from 25 km resolution simulations
Spencer K. Clark, Noah D. Brenowitz, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Andre Perkins, Oliver Watt-Meyer, Christopher S. Bretherton, and Lucas M. Harris · 2022
Cited alongside, same era.
Forecasting global weather with graph neural networks
Ryan Keisler · 2022
Cited alongside, same era.
Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian · 2023
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Spherical fourier neural operators: Learning stable dynamics on the sphere
Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, and Anima Anandkumar · 2023
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Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead
Kang Chen, Tao Han, Junchao Gong, Lei Bai, Fenghua Ling, Jing-Jia Luo, Xi Chen, Leiming Ma, Tianning Zhang, Rui Su, Yuanzheng Ci, Bin Li, Xiaokang Yang, and Wanli Ouyang · 2023
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Climax: A foundation model for weather and climate
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, and Aditya Grover · 2023
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