Fetching the paper…
Reading the bibliography…
Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting.
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 · 1942
Earlier work this paper cites.
A Machine Learning Parameterization of Clouds in a Coarse-Resolution Climate Model for Unbiased Radiation
Brian Henn, Yakelyn R. Jauregui, Spencer K. Clark, Noah D. Brenowitz, Jeremy McGibbon, Oliver Watt-Meyer, Andrew G. Pauling, and Christopher S. Bretherton · 1942
Earlier work this paper cites.
Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data-Informed Methods and Climate Change Impacts
Laura A. Mansfield, Aman Gupta, Adam C. Burnett, Brian Green, Catherine Wilka, and Aditi Sheshadri · 1942
Earlier work this paper cites.
Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models
Jonathan A. Weyn, Dale R. Durran, Rich Caruana, and Nathaniel Cresswell-Clay · 1942
Earlier work this paper cites.
Neural-Network Parameterization of Subgrid Momentum Transport in the Atmosphere
Janni Yuval and Paul A. O’Gorman · 1942
Earlier work this paper cites.
The quasi-biennial oscillation
M. P. Baldwin, L. J. Gray, T. J. Dunkerton, K. Hamilton, P. H. Haynes, W. J. Randel, J. R. Holton, M. J. Alexander, I. Hirota, T. Horinouchi, D. B. A. Jones, J. S. Kinnersley, C. Marquardt, K. Sato, and M. Takahashi · 1944
Earlier work this paper cites.
Using Machine Learning to Analyze Physical Causes of Climate Change: A Case Study of U.S. Midwest Extreme Precipitation
Frances V. Davenport and Noah S. Diffenbaugh · 1944
Earlier work this paper cites.
Machine Learning Gravity Wave Parameterization Generalizes to Capture the QBO and Response to Increased CO2
Zachary I. Espinosa, Aditi Sheshadri, Gerald R. Cain, Edwin P. Gerber, and Kevin J. DallaSanta · 1944
Earlier work this paper cites.
Gravity wave dynamics and effects in the middle atmosphere
David C. Fritts and M. Joan Alexander · 1944
Earlier work this paper cites.
Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations
William Gregory, Mitchell Bushuk, Yongfei Zhang, Alistair Adcroft, and Laure Zanna · 1944
Earlier work this paper cites.
Internal gravity waves from atmospheric jets and fronts
Riwal Plougonven and Fuqing Zhang · 1944
Earlier work this paper cites.
Physics-Constrained Machine Learning of Evapotranspiration
Wen Li Zhao, Pierre Gentine, Markus Reichstein, Yao Zhang, Sha Zhou, Yeqiang Wen, Changjie Lin, Xi Li, and Guo Yu Qiu · 1944
Earlier work this paper cites.
A new subgrid-scale orographic drag parametrization: Its formulation and testing
François Lott and Martin J. Miller · 1997
Earlier work this paper cites.
An overview of the past, present and future of gravity-wave drag parametrization for numerical climate and weather prediction models
Young-Joon Kim, S. D. Eckermann, and Hye-Yeong Chun · 2003
Earlier work this paper cites.
An Accurate Spectral Nonorographic Gravity Wave Drag Parameterization for General Circulation Models
John F. Scinocca · 2003
Earlier work this paper cites.
The next generation of scenarios for climate change research and assessment
Richard H Moss, Jae A Edmonds, Kathy A Hibbard, Martin R Manning, Steven K Rose, Detlef P Van Vuuren, Timothy R Carter, Seita Emori, Mikiko Kainuma, Tom Kram, et al · 2010
Earlier work this paper cites.
Era-interim daily climatology
Martin Janoušek · 2011
Earlier work this paper cites.
Merra: Nasa’s modern-era retrospective analysis for research and applications
Michele M Rienecker, Max J Suarez, Ronald Gelaro, Ricardo Todling, Julio Bacmeister, Emily Liu, Michael G Bosilovich, Siegfried D Schubert, Lawrence Takacs, Gi-Kong Kim, et al · 2011
Earlier work this paper cites.
Dynamical Control of the Mesosphere by Orographic and Nonorographic Gravity Wave Drag during the Extended Northern Winters of 2006 and 2009
Charles McLandress, John F. Scinocca, Theodore G. Shepherd, M. Catherine Reader, and Gloria L. Manney · 2012
Earlier work this paper cites.
The cnrm-cm5. 1 global climate model: description and basic evaluation
Aurore Voldoire, Emilia Sanchez-Gomez, Dea Salas y Mélia, B Decharme, Christophe Cassou, Stéphane Sénési, Sophie Valcke, Isabelle Beau, A Alias, Matthieu Chevallier, et al · 2013
Cited alongside, same era.
EURO-CORDEX: new high-resolution climate change projections for European impact research
Daniela Jacob, Juliane Petersen, Bastian Eggert, Antoinette Alias, Ole Bøssing Christensen, Laurens M. Bouwer, Alain Braun, Augustin Colette, Michel Déqué, Goran Georgievski, Elena Georgopoulou, Andreas Gobiet, Laurent Menut, Grigory Nikulin, Andreas Haensler, Nils Hempelmann, Colin Jones, Klaus Keuler, Sari Kovats, Nico Kröner, Sven Kotlarski, Arne Kriegsmann, Eric Martin, Erik van Meijgaard, Christopher Moseley, Susanne Pfeifer, Swantje Preuschmann, Christine Radermacher, Kai Radtke, Diana Rechid, Mark Rounsevell, Patrick Samuelsson, Samuel Somot, Jean-Francois Soussana, Claas Teichmann, Riccardo Valentini, Robert Vautard, Björn Weber, and Pascal Yiou · 2014
Cited alongside, same era.
The modern-era retrospective analysis for research and applications, version 2 (MERRA-2)
Ronald Gelaro, Will McCarty, Max J Suárez, Ricardo Todling, Andrea Molod, Lawrence Takacs, Cynthia A Randles, Anton Darmenov, Michael G Bosilovich, Rolf Reichle, et al · 2017
Cited alongside, same era.
Deep learning to represent subgrid processes in climate models
Stephan Rasp, Michael S. Pritchard, and Pierre Gentine · 2018
Cited alongside, same era.
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, et al · 2023
Later among the works it cites.
AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning
Christian Lessig, Ilaria Luise, Bing Gong, Michael Langguth, Scarlet Stadler, and Martin Schultz · 2023
Later among the works it cites.
Ai foundation models for weather and climate: Applications, design, and implementation
S Karthik Mukkavilli, Daniel Salles Civitarese, Johannes Schmude, Johannes Jakubik, Anne Jones, Nam Nguyen, Christopher Phillips, Sujit Roy, Shraddha Singh, Campbell Watson, et al · 2023
Later among the works it cites.
Hiera: A hierarchical vision transformer without the bells-and-whistles
Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei, Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arkabandhu Chowdhury, Omid Poursaeed, Judy Hoffman, et al · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Modulation of radiative aerosols effects by atmospheric circulation over the euro-mediterranean region
Pierre Nabat, Samuel Somot, Christophe Cassou, Marc Mallet, Martine Michou, Dominique Bouniol, Bertrand Decharme, Thomas Drugé, Romain Roehrig, and David Saint-Martin · 2020
Cited alongside, same era.
Adversarial super-resolution of climatological wind and solar data
Karen Stengel, Andrew Glaws, Dylan Hettinger, and Ryan N King · 2020
Cited alongside, same era.
Swinir: Image restoration using swin transformer
Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte · 2021
Cited alongside, same era.
Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast
Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian · 2022
Cited alongside, same era.
Masked autoencoders as spatiotemporal learners
Christoph Feichtenhofer, Yanghao Li, Kaiming He, et al · 2022
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
Cited alongside, same era.
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, et al · 2022
Cited alongside, same era.
Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al · 2022
Cited alongside, same era.
Johannes Schmude and Juan Nathaniel · 2023
Later among the works it cites.
Aurora: A foundation model of the atmosphere
Cristian Bodnar, Wessel P Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan Weyn, Haiyu Dong, Anna Vaughan, et al · 2024
Closest in time.
Warmer Antarctic summers in recent decades linked to earlier stratospheric final warming occurrences
Hyesun Choi, Hataek Kwon, Seong-Joong Kim, and Baek-Min Kim · 2024
Closest in time.
Pushing the frontiers in climate modelling and analysis with machine learning
Veronika Eyring, William D. Collins, Pierre Gentine, Elizabeth A. Barnes, Marcelo Barreiro, Tom Beucler, Marc Bocquet, Christopher S. Bretherton, Hannah M. Christensen, Katherine Dagon, David John Gagne, David Hall, Dorit Hammerling, Stephan Hoyer, Fernando Iglesias-Suarez, Ignacio Lopez-Gomez, Marie C. McGraw, Gerald A. Meehl, Maria J. Molina, Claire Monteleoni, Juliane Mueller, Michael S. Pritchard, David Rolnick, Jakob Runge, Philip Stier, Oliver Watt-Meyer, Katja Weigel, Rose Yu, and Laure Zanna · 2024
Closest in time.
Machine learning global simulation of nonlocal gravity wave propagation
Aman Gupta, Aditi Sheshadri, Sujit Roy, Vishal Gaur, Manil Maskey, and Rahul Ramachandran · 2024
Closest in time.
Syed Zahid Husain, Leo Separovic, Jean-François Caron, Rabah Aider, Mark Buehner, Stéphane Chamberland, Ervig Lapalme, Ron McTaggart-Cowan, Christopher Subich, Paul Vaillancourt, et al · 2024
Closest in time.
Neural general circulation models for weather and climate
Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, et al · 2024
Closest in time.
Emerging ai-based weather prediction models as downscaling tools
Nikolay Koldunov, Thomas Rackow, Christian Lessig, Sergey Danilov, Suvarchal K Cheedela, Dmitry Sidorenko, Irina Sandu, and Thomas Jung · 2024
Closest in time.
Aifs-ecmwf’s data-driven forecasting system
Simon Lang, Mihai Alexe, Matthew Chantry, Jesper Dramsch, Florian Pinault, Baudouin Raoult, Mariana CA Clare, Christian Lessig, Michael Maier-Gerber, Linus Magnusson, et al · 2024
Closest in time.
Residual diffusion modeling for km-scale atmospheric downscaling
Morteza Mardani, Noah Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Karthik Kashinath, Jan Kautz, and Mike Pritchard · 2024
Closest in time.
Data driven weather forecasts trained and initialised directly from observations
Anthony McNally, Christian Lessig, Peter Lean, Eulalie Boucher, Mihai Alexe, Ewan Pinnington, Matthew Chantry, Simon Lang, Chris Burrows, Marcin Chrust, et al · 2024
Closest in time.
Climatelearn: Benchmarking machine learning for weather and climate modeling
Tung Nguyen, Jason Jewik, Hritik Bansal, Prakhar Sharma, and Aditya Grover · 2024
Closest in time.
Weatherbench 2: A benchmark for the next generation of data-driven global weather models
Stephan Rasp, Stephan Hoyer, Alexander Merose, Ian Langmore, Peter Battaglia, Tyler Russell, Alvaro Sanchez-Gonzalez, Vivian Yang, Rob Carver, Shreya Agrawal, et al · 2024
Closest in time.
Clifford neural operators on atmospheric data influenced partial differential equations
Sujit Roy, Rajat Shinde, Christopher E Phillips, Ankur Kumar, Wei Ji Leong, Manil Maskey, and Rahul Ramachandran · 2024
Closest in time.
Global atmospheric data assimilation with multi-modal masked autoencoders
Thomas J Vandal, Kate Duffy, Daniel McDuff, Yoni Nachmany, and Chris Hartshorn · 2024
Closest in time.