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Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere.
“Language models are few-shot learners”
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry and Amanda Askell · 1901
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“The general circulation of the atmosphere: A numerical experiment”
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“Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization”
Veronika Eyring, Sandrine Bony, Gerald Meehl, Catherine Senior, Bjorn Stevens, Ronald Stouffer and Karl Taylor · 1958
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“The nature and theory of the general circulation of the atmosphere”
Edward Lorenz · 1967
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“Downscaling general circulation model output: a review of methods and limitations”
Robert Wilby and Thomas Wigley · 1997
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“The coupled model intercomparison project (CMIP)”
Gerald Meehl, George Boer, Curt Covey, Mojib Latif and Ronald Stouffer · 2000
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“Atmospheric modeling, data assimilation and predictability”
Eugenia Kalnay · 2003
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“Atmospheric circulation dynamics and circulation models”
Masaki Satoh · 2004
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“The origins of computer weather prediction and climate modeling”
Peter Lynch · 2008
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“Attributing physical and biological impacts to anthropogenic climate change”
Cynthia Rosenzweig, David Karoly, Marta Vicarelli, Peter Neofotis, Qigang Wu, Gino Casassa, Annette Menzel, Terry Root, Nicole Estrella and Bernard Seguin · 2008
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“The THORPEX interactive grand global ensemble”
Philippe Bougeault, Zoltan Toth, Craig Bishop, Barbara Brown, David Burridge, De Chen, Beth Ebert, Manuel Fuentes, Thomas Hamill and Ken Mylne · 2010
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“Numerical weather and climate prediction”
Thomas Warner · 2010
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“Learning to learn”
Sebastian Thrun and Lorien Pratt · 2012
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“Depletion of fossil fuels and anthropogenic climate change—A review”
Mikael H\"o\"ok and Xu Tang · 2013
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“The community earth system model: a framework for collaborative research”
James Hurrell, Marika Holland, Peter Gent, Steven Ghan, Jennifer Kay, Paul Kushner, J-F Lamarque, William Large, D Lawrence and Keith Lindsay · 2013
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“Climate change 2014 synthesis report”
IPCC Adopted · 2014
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
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“The quiet revolution of numerical weather prediction”
Peter Bauer, Alan Thorpe and Gilbert Brunet · 2015
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“A deep hybrid model for weather forecasting”
Aditya Grover, Ashish Kapoor and Eric Horvitz · 2015
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“Data assimilation”
Kody Law, Andrew Stuart and Konstantinos Zygalakis · 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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“The modelling infrastructure of the Integrated Forecasting System: Recent advances and future challenges”
NP Wedi, P Bauer, W Denoninck, M Diamantakis, M Hamrud, C Kuhnlein, S Malardel, K Mogensen, G Mozdzynski and PK Smolarkiewicz · 2015
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“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“Deep networks with stochastic depth”
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra and Kilian Weinberger · 2016
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“xarray: N-D labeled Arrays and Datasets in Python”
Stephan Hoyer and Joe Hamman · 2017
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“Decoupled weight decay regularization”
Ilya Loshchilov and Frank Hutter · 2017
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“Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations”
Tapio Schneider, Shiwei Lan, Andrew Stuart and Joao Teixeira · 2017
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“Understanding, modeling and predicting weather and climate extremes: Challenges and opportunities”
Jana Sillmann, Thordis Thorarinsdottir, Noel Keenlyside, Nathalie Schaller, Lisa Alexander, Gabriele Hegerl, Sonia Seneviratne, Robert Vautard, Xuebin Zhang and Francis Zwiers · 2017
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“Deepsd: Generating high resolution climate change projections through single image super-resolution”
Thomas Vandal, Evan Kodra, Sangram Ganguly, Andrew Michaelis, Ramakrishna Nemani and Auroop Ganguly · 2017
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“Attention is all you need”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, ukasz Kaiser and Illia Polosukhin · 2017
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“Challenges and design choices for global weather and climate models based on machine learning”
Peter Dueben and Peter Bauer · 2018
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“Bert: Pre-training of deep bidirectional transformers for language understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2018
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“ERA5 hourly data on single levels from 1979 to present”
H Hersbach, B Bell, P Berrisford, G Biavati, A Hor\’anyi, J Mu\˜noz, J Nicolas, C Peubey, R Radu and I Rozum · 2018
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“CatBoost: unbiased boosting with categorical features”
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Dorogush and Andrey Gulin · 2018
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“DeepDownscale: a deep learning strategy for high-resolution weather forecast”
Eduardo Rodrigues, Igor Oliveira, Renato Cunha and Marco Netto · 2018
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“Statistical downscaling of precipitation using machine learning techniques”
DA Sachindra, Khandakar Ahmed, Md Rashid, S Shahid and BJC Perera · 2018
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“Toward data-driven weather and climate forecasting: Approximating a simple general circulation model with deep learning”
Sebastian Scher · 2018
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“Forecasting at scale”
Sean Taylor and Benjamin Letham · 2018
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“The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events”
Fr\’ed\’eric Vitart and Andrew Robertson · 2018
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“xESMF: Universal regridder for geospatial data”, 2018
J Zhuang · 2018
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“Machine learning and artificial intelligence to aid climate change research and preparedness”
Chris Huntingford, Elizabeth Jeffers, Michael Bonsall, Hannah Christensen, Thomas Lees and Hui Yang · 2019
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“Improving subseasonal forecasting in the western US with machine learning”
Jessica Hwang, Paulo Orenstein, Judah Cohen, Karl Pfeiffer and Lester Mackey · 2019
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“Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges”
Diego Miralles, Pierre Gentine, Sonia Seneviratne and Adriaan Teuling · 2019
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“N-BEATS: Neural basis expansion analysis for interpretable time series forecasting”
Boris Oreshkin, Dmitri Carpov, Nicolas Chapados and Yoshua Bengio · 2019
Cited alongside, same era.
“PyTorch: An Imperative Style, High-Performance Deep Learning Library”
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai and Soumith Chintala · 2019
“Learning transferable visual models from natural language supervision”
Alec Radford, Jong Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin and Jack Clark · 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 and Sam Madge · 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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“Can deep learning beat numerical weather prediction?”
Martin Schultz, Clara Betancourt, Bing Gong, Felix Kleinert, Michael Langguth, Lukas Leufen, Amirpasha Mozaffari and Scarlet Stadtler · 2021
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“Training data-efficient image transformers & distillation through attention”
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles and Herv\’e J\’egou · 2021
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Cited alongside, same era.
“Pytorch: An imperative style, high-performance deep learning library”
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein and Luca Antiga · 2019
Cited alongside, same era.
“Deep learning and process understanding for data-driven Earth system science”
Markus Reichstein, Gustau Camps-Valls, Bjorn Stevens, Martin Jung, Joachim Denzler and Nuno Carvalhais · 2019
Cited alongside, same era.
“Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground”
Sebastian Scher and Gabriele Messori · 2019
Cited alongside, same era.
“Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation”
Thomas Vandal, Evan Kodra and Auroop Ganguly · 2019
Cited alongside, same era.
“PyTorch Image Models”
Ross Wightman · 2019
Cited alongside, same era.
“What is the predictability limit of midlatitude weather?”
Fuqing Zhang, Y Sun, Linus Magnusson, Roberto Buizza, Shian-Jiann Lin, Jan-Huey Chen and Kerry Emanuel · 2019
Cited alongside, same era.
“A machine learning-based global atmospheric forecast model”
Troy Arcomano, Istvan Szunyogh, Jaideep Pathak, Alexander Wikner, Brian Hunt and Edward Ott · 2020
Cited alongside, same era.
“Convolutional conditional neural processes for local climate downscaling”
Anna Vaughan, Will Tebbutt, J Hosking and Richard Turner · 2021
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“Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models”
Jonathan Weyn, Dale Durran, Rich Caruana and Nathaniel Cresswell-Clay · 2021
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“Florence: A new foundation model for computer vision”
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li and Chunyuan Li · 2021
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“Informer: Beyond efficient transformer for long sequence time-series forecasting”
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong and Wancai Zhang · 2021
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“Are general circulation models obsolete?”
V Balaji, Fleur Couvreux, Julie Deshayes, Jacques Gautrais, Fr\’ed\’eric Hourdin and Catherine Rio · 2022
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“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
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“Clifford Neural Layers for PDE Modeling”
Johannes Brandstetter, Rianne van Berg, Max Welling and Jayesh Gupta · 2022
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“Message Passing Neural PDE Solvers”
Johannes Brandstetter, Daniel Worrall and Max Welling · 2022
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“PaLM: Scaling language modeling with pathways”
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Chung, Charles Sutton and Sebastian Gehrmann · 2022
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“Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery”
Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell and Stefano Ermon · 2022
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“Towards Multi-spatiotemporal-scale Generalized PDE Modeling”
Jayesh Gupta and Johannes Brandstetter · 2022
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“Rethinking Machine Learning for Climate Science: A Dataset Perspective”
Aditya Grover · 2022
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“Masked autoencoders are scalable vision learners”
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll\’ar and Ross Girshick · 2022
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“Training Compute-Optimal Large Language Models”
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Casas, Lisa Hendricks, Johannes Welbl and Aidan Clark · 2022
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“Forecasting Global Weather with Graph Neural Networks”
Ryan Keisler · 2022
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“GraphCast: Learning skillful medium-range global weather forecasting”
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Alexander Pritzel, Suman Ravuri, Timo Ewalds, Ferran Alet and Zach Eaton-Rosen · 2022
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“Swin Transformer V2: Scaling Up Capacity and Resolution”
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei and Baining Guo · 2022
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“Pretrained transformers as universal computation engines”
Kevin Lu, Aditya Grover, Pieter Abbeel and Igor Mordatch · 2022
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“Practical Conditional Neural Processes Via Tractable Dependent Predictions”
Stratis Markou, James Requeima, Wessel Bruinsma, Anna Vaughan and Richard Turner · 2022
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Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li and Kamyar Azizzadenesheli · 2022
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“Hierarchical text-conditional image generation with clip latents”
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu and Mark Chen · 2022
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“Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning”
Colorado Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Salvatore Candido, Matt Uyttendaele and Trevor Darrell · 2022
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“A Generalist Agent” Featured Certification
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“Webplotdigitizer: Version 4.6”, 2022
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“Complex and yet predictable: The message of the 2021 Nobel Prize in Physics”
AR Ravishankara, David Randall and James Hurrell · 2022
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“Language models generalize beyond natural proteins”
Robert Verkuil, Ori Kabeli, Yilun Du, Basile Wicky, Lukas Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu and Alexander Rives · 2022
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“Outcomes of the WMO Prize Challenge to Improve Subseasonal to Seasonal Predictions Using Artificial Intelligence”
F. Vitart, A.. Robertson, A. Spring, F. Pinault, R. Roskar, W. Cao, S. Bech, A. Bienkowski, N. Caltabiano, E. Coning, B. Denis, A. Dirkson, J. Dramsch, P. Dueben, J. Gierschendorf, H.. Kim, K. Nowak, D. Landry, L. Lled\’o, L. Palma, S. Rasp and S. Zhou · 2022
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