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We present a data-driven approach for forecasting global weather using graph neural networks.
Spatially extended tests of a neural network parametrization trained by coarse-graining
Noah D. Brenowitz and Christopher S. Bretherton · 1942
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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
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The ecmwf operational implementation of four-dimensional variational assimilation. i: Experimental results with simplified physics
F. Rabier, H. Järvinen, E. Klinker, J.-F. Mahfouf, and A. Simmons · 2000
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Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles
David John Gagne, Amy McGovern, Sue Ellen Haupt, Ryan A Sobash, John K Williams, and Ming Xue · 2017
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Using artificial intelligence to improve real-time decision-making for high-impact weather
Amy Mcgovern, Kimberly Elmore, David Gagne, Sue Haupt, Christopher Karstens, Ryan Lagerquist, Travis Smith, and John Williams · 2017
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Relational inductive biases, deep learning, and graph networks, 2018
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
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Challenges and design choices for global weather and climate models based on machine learning
P. D. Dueben and P. Bauer · 2018
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Machine learning for precipitation nowcasting from radar images, 2019
Shreya Agrawal, Luke Barrington, Carla Bromberg, John Burge, Cenk Gazen, and Jason Hickey · 2019
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Learning data-driven discretizations for partial differential equations
Yohai Bar-Sinai, Stephan Hoyer, Jason Hickey, and Michael P. Brenner · 2019
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A machine learning-based global atmospheric forecast model
Troy Arcomano, Istvan Szunyogh, Jaideep Pathak, Alexander Wikner, Brian R. Hunt, and Edward Ott · 2020
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Discovering symbolic models from deep learning with inductive biases, 2020
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, and Shirley Ho · 2020
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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 De 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 de Rosnay, Iryna Rozum, Freja Vamborg, Sebastien Villaume, and Jean-Noël Thépaut · 2020
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Weatherbench: a benchmark data set for data-driven weather forecasting
Stephan Rasp, Peter D Dueben, Sebastian Scher, Jonathan A Weyn, Soukayna Mouatadid, and Nils Thuerey · 2020
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Can deep learning beat numerical weather prediction?
Martin Schultz, Clara Betancourt, Bing Gong, Felix Kleinert, Michael Langguth, Lukas Leufen, Amirpasha Mozaffari, and Scarlet Stadtler · 2020
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Metnet: A neural weather model for precipitation forecasting, 2020
Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver, Tim Salimans, Shreya Agrawal, Jason Hickey, and Nal Kalchbrenner · 2020
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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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Machine learning accelerated computational fluid dynamics
Ayya Alieva, Dmitrii Kochkov, Jamie Alexander Smith, Michael Brenner, Qing Wang, and Stephan Hoyer · 2021
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Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi · 2021
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Gradients are not all you need
Luke Metz, C Daniel Freeman, Samuel S Schoenholz, and Tal Kachman · 2021
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Machine learning emulation of 3d cloud radiative effects, 2021
David Meyer, Robin J. Hogan, Peter D. Dueben, and Shannon L. Mason · 2021
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Learning mesh-based simulation with graph networks, 2021
Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter W. Battaglia · 2021
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Data-driven medium-range weather prediction with a resnet pretrained on climate simulations: A new model for weatherbench
Stephan Rasp and Nils Thuerey · 2021
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Machine learning emulation of gravity wave drag in numerical weather forecasting
Matthew Chantry, Sam Hatfield, Peter Dueben, Inna Polichtchouk, and Tim Palmer · 2021
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Combining distribution-based neural networks to predict weather forecast probabilities
Mariana C.A. Clare, Omar Jamil, and Cyril J. Morcrette · 2021
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Skillful twelve hour precipitation forecasts using large context neural networks, 2021
Lasse Espeholt, Shreya Agrawal, Casper Sønderby, Manoj Kumar, Jonathan Heek, Carla Bromberg, Cenk Gazen, Jason Hickey, Aaron Bell, and Nal Kalchbrenner · 2021
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Variational data assimilation with a learned inverse observation operator, 2021
Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, and Stephan Hoyer · 2021
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Ms-nowcasting: Operational precipitation nowcasting with convolutional lstms at microsoft weather, 2021
Sylwester Klocek, Haiyu Dong, Matthew Dixon, Panashe Kanengoni, Najeeb Kazmi, Pete Luferenko, Zhongjian Lv, Shikhar Sharma, Jonathan Weyn, and Siqi Xiang · 2021
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Fourier neural operator for parametric partial differential equations, 2021
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2021
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Skilful precipitation nowcasting using deep generative models of radar
Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, Rachel Prudden, Amol Mandhane, Aidan Clark, Andrew Brock, Karen Simonyan, Raia Hadsell, Niall Robinson, Ellen Clancy, Alberto Arribas, and Shakir Mohamed · 2021
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Spherical convolution and other forms of informed machine learning for deep neural network based weather forecasts, 2021
Sebastian Scher and Gabriele Messori · 2021
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Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers, 2021
Kiwon Um, Robert Brand, Yun, Fei, Philipp Holl, and Nils Thuerey · 2021
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Correcting weather and climate models by machine learning nudged historical simulations
Oliver Watt-Meyer, Noah D. Brenowitz, Spencer K. Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Andre Perkins, and Christopher S. Bretherton · 2021
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Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision
Janni Yuval, Paul A. O’Gorman, and Chris N. Hill · 2021
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Message passing neural pde solvers, 2022
Johannes Brandstetter, Daniel Worrall, and Max Welling · 2022
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