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Data-driven deep learning models are transforming global weather forecasting.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 1907
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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, Matthew Chantry, Zied Ben Bouallegue, Peter Dueben, Carla Bromberg, Jared Sisk, Luke Barrington, Aaron Bell, and Fei Sha · 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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Scoring rules for continuous probability distributions
James E. Matheson and Robert L. Winkler · 1976
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Computing fourier transforms and convolutions on the 2-sphere
J.R. Driscoll and D.M. Healy · 1994
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2006
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Using numerical weather prediction to assess climate models
M. J. Rodwell and T. N. Palmer · 2007
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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Improving subtropical boundary layer cloudiness in the 2011 NCEP GFS
J. K. Fletcher, C. S. Bretherton, H. Xiao, R. Sun, and J. Han · 2014
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Why should ensemble spread match the rmse of the ensemble mean?
V. Fortin, M. Abaza, F. Anctil, and R. Turcotte · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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The community earth system model (cesm) large ensemble project: A community resource for studying climate change in the presence of internal climate variability
J. E. Kay, C. Deser, A. Phillips, A. Mai, C. Hannay, G. Strand, J. M. Arblaster, S. C. Bates, G. Danabasoglu, J. Edwards, M. Holland, P. Kushner, J.-F. Lamarque, D. Lawrence, K. Lindsay, A. Middleton, E. Munoz, R. Neale, K. Oleson, L. Polvani, and M. Vertenstein · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q. Weinberger · 2016
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The scenario model intercomparison project (ScenarioMIP) for CMIP6
Brian C. O’Neill, Claudia Tebaldi, Detlef P. van Vuuren, Veronika Eyring, Pierre Friedlingstein, George Hurtt, Reto Knutti, Elmar Kriegler, Jean-Francois Lamarque, Jason Lowe, Gerald A. Meehl, Richard Moss, Keywan Riahi, and Benjamin M. Sanderson · 2016
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Deep learning to represent subgrid processes in climate models
Stephan Rasp, Michael S. Pritchard, and Pierre Gentine · 2018
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Estimation of the continuous ranked probability score with limited information and applications to ensemble weather forecasts
Michaël Zamo and Philippe Naveau · 2018
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PyTorch Lightning, March 2019
William Falcon and The PyTorch Lightning team · 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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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Machine learning for weather and climate are worlds apart
D. Watson-Parris · 2020
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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Fourier neural operator for parametric partial differential equations
Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2021
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ClimART: A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models
Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker, and David Rolnick · 2021
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A multi-scale deep learning framework for projecting weather extremes
Antoine Blanchard, Nishant Parashar, Boyko Dodov, Christian Lessig, and Themis Sapsis · 2022
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Impact of warmer sea surface temperature on the global pattern of intense convection: Insights from a global storm resolving model
Kai-Yuan Cheng, Lucas Harris, Christopher Bretherton, Timothy M. Merlis, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer Clark, and Stephan Fueglistaler · 2022
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Score-based diffusion models in function space
Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, and Anima Anandkumar · 2023
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PDE-Refiner: Achieving accurate long rollouts with temporal neural pde solvers
Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E Turner, and Johannes Brandstetter · 2023
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Residual corrective diffusion modeling for km-scale atmospheric downscaling
Morteza Mardani, Noah Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Mohammad Amin Nabian, Tao Ge, Akshay Subramaniam, Karthik Kashinath, Jan Kautz, and Mike Pritchard · 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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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Flexible diffusion modeling of long videos
William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, and Frank Wood · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Forecasting global weather with graph neural networks
Ryan Keisler · 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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Make-a-video: Text-to-video generation without text-video data
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman · 2022
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MCVD: Masked conditional video diffusion for prediction, generation, and interpolation
Vikram Voleti, Alexia Jolicoeur-Martineau, and Christopher Pal · 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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GenCast: Diffusion-based ensemble forecasting for medium-range weather
Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Timo Ewalds, Andrew El-Kadi, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, and Matthew Willson · 2023
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DYffusion: A dynamics-informed diffusion model for spatiotemporal forecasting
Salva Rühling Cachay, Bo Zhao, Hailey Joren, and Rose Yu · 2023
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Debias coarsely, sample conditionally: Statistical downscaling through optimal transport and probabilistic diffusion models
Zhong Yi Wan, Ricardo Baptista, Anudhyan Boral, Yi-Fan Chen, John Anderson, Fei Sha, and Leonardo Zepeda-Nunez · 2023
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ACE: A fast, skillful learned global atmospheric model for climate prediction
Oliver Watt-Meyer, Gideon Dresdner, Jeremy McGibbon, Spencer K Clark, James Duncan, Brian Henn, Matthew Peters, Noah D Brenowitz, Karthik Kashinath, Mike Pritchard, Boris Bonev, and Christopher Bretherton · 2023
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Diffusion probabilistic modeling for video generation
Ruihan Yang, Prakhar Srivastava, and Stephan Mandt · 2023
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A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
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Pushing the frontiers in climate modelling and analysis with machine learning
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Generative learning for forecasting the dynamics of high dimensional complex systems
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Validating climate models with spherical convolutional wasserstein distance
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Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
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Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
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