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Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction.
Skill scores and correlation coefficients in model verification
Allan H Murphy and Edward S Epstein · 1989
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Applying the fluctuation–dissipation theorem to a two-layer model of quasigeostrophic turbulence
Nicholas J Lutsko, Isaac M Held, and Pablo Zurita-Gotor · 2015
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Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach
Jaideep Pathak, Brian Hunt, Michelle Girvan, Zhixin Lu, and Edward Ott · 2018
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Can machines learn to predict weather? using deep learning to predict gridded 500-hPa geopotential height from historical weather data
Jonathan A Weyn, Dale R Durran, and Rich Caruana · 2019
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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
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Size of the atmospheric blocking events: Scaling law and response to climate change
Ebrahim Nabizadeh, Pedram Hassanzadeh, Da Yang, and Elizabeth A Barnes · 2019
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
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Data-driven medium-range weather prediction with a resnet pretrained on climate simulations: A new model for weatherbench
Stephan Rasp and Nils Thuerey · 2021
Cited alongside, same era.
Climbing down charney’s ladder: machine learning and the post-dennard era of computational climate science
V Balaji · 2021
Cited alongside, same era.
Can deep learning beat numerical weather prediction?
MG Schultz, C Betancourt, B Gong, F Kleinert, M Langguth, LH Leufen, Amirpasha Mozaffari, and S Stadtler · 2021
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Machine learning for weather and climate are worlds apart
Duncan Watson-Parris · 2021
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Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath · 2022
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Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al · 2022
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Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network
Ashesh Chattopadhyay, Pedram Hassanzadeh, and Devika Subramanian
Cited in the paper.
Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, and Karthik Kashinath
Cited in the paper.
Analog forecasting of extreme-causing weather patterns using deep learning
Ashesh Chattopadhyay, Ebrahim Nabizadeh, and Pedram Hassanzadeh
Cited in the paper.