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Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting.
“Deep learning for Aerosol Forecasting”
Caleb Hoyne, S. Mukkavilli and David Meger · 1910
Earlier work this paper cites.
“The use of model output statistics (MOS) in objective weather forecasting”
Harry Glahn and Dale Lowry · 1972
Earlier work this paper cites.
“The MNIST database of handwritten digits”, http://yann.lecun.com/exdb/mnist/ , 1998
Y. LeCun · 1998
Earlier work this paper cites.
“Scale-dependence of the predictability of precipitation from continental radar images. Part I: Description of the methodology”
Urs Germann and Isztar Zawadzki · 2002
Earlier work this paper cites.
“Language Models are Few-Shot Learners”
Tom. Brown et al · 2005
Earlier work this paper cites.
“HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere”
Krzysztof Gorski et al · 2005
Earlier work this paper cites.
“A new paradigm for parameterizations in numerical weather prediction and other atmospheric models”
R.A. Pielke. et al · 2006
Earlier work this paper cites.
“Imagenet: A large-scale hierarchical image database”
J. Deng et al · 2009
Earlier work this paper cites.
“Numerical Weather and Climate Prediction”
Thomas Warner · 2010
Earlier work this paper cites.
“The NCEP climate forecast system version 2”
Suranjana Saha et al · 2014
Earlier work this paper cites.
“Neural machine translation by jointly learning to align and translate”
Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio · 2014
Earlier work this paper cites.
“The quiet revolution of numerical weather prediction”
Peter Bauer, Alan Thorpe and Gilbert Brunet · 2015
Earlier work this paper cites.
“Machine learning based multi-physical-model blending for enhancing renewable energy forecast-improvement via situation dependent error correction”
Siyuan Lu et al · 2015
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation”
Olaf Ronneberger, Philipp Fischer and Thomas Brox · 2015
Earlier work this paper cites.
“TECA: Petascale Pattern Recognition for Climate Science”
Prabhat et al · 2015
Earlier work this paper cites.
“The pan-Canadian high resolution (2.5 km) deterministic prediction system”
Jason Milbrandt et al · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
Earlier work this paper cites.
“Extreme weather caused by concurrent cyclone, front and thunderstorm occurrences” Number: 1 Publisher: Nature Publishing Group
Andrew. Dowdy and Jennifer. Catto · 2017
Earlier work this paper cites.
“ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events”
Evan Racah et al · 2017
Earlier work this paper cites.
“Convolutional sequence to sequence learning”
Jonas Gehring et al · 2017
Earlier work this paper cites.
“Bert: Pre-training of deep bidirectional transformers for language understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2018
Earlier work this paper cites.
“Deep learning to represent subgrid processes in climate models” Publisher: Proceedings of the National Academy of Sciences
Stephan Rasp, Michael. Pritchard and Pierre Gentine · 2018
Earlier work this paper cites.
“Mesoscale Simulations of Australian Direct Normal Irradiance, Featuring an Extreme Dust Event”
S.. Mukkavilli et al · 2018
Earlier work this paper cites.
“Assessment of atmospheric aerosols from two reanalysis products over Australia”
S.K. Mukkavilli et al · 2018
Earlier work this paper cites.
“Representation learning with contrastive predictive coding”
Aaron Oord, Yazhe Li and Oriol Vinyals · 2018
Earlier work this paper cites.
“Hierarchical graph representation learning with differentiable pooling”
Z. Ying et al · 2018
Earlier work this paper cites.
“Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0. 1)”
Georgy Ayzel, Maik Heistermann and Tanja Winterrath · 2019
Earlier work this paper cites.
“Machine learning for precipitation nowcasting from radar images”
Shreya Agrawal et al · 2019
Earlier work this paper cites.
“Performance Benchmarking of Data Augmentation and Deep Learning for Tornado Prediction”
Carlos. Barajas, Matthias. Gobbert and Jianwu Wang · 2019
Earlier work this paper cites.
“Star-Transformer”
Qipeng Guo et al · 2019
Earlier work this paper cites.
“Graph wavenet for deep spatial-temporal graph modeling”
Z. Wu et al · 2019
Earlier work this paper cites.
“An image is worth 16x16 words: Transformers for image recognition at scale”
Alexey Dosovitskiy et al · 2020
Earlier work this paper cites.
“Pretrained transformers improve out-of-distribution robustness”
Dan Hendrycks et al · 2020
Earlier work this paper cites.
“Metnet: A neural weather model for precipitation forecasting”
Casper Sønderby et al · 2020
Earlier work this paper cites.
“A review of radar-based nowcasting of precipitation and applicable machine learning techniques”
Rachel Prudden et al · 2020
Earlier work this paper cites.
“Adversarial super-resolution of climatological wind and solar data”
Karen Stengel, Andrew Glaws, Dylan Hettinger and Ryan King · 2020
Earlier work this paper cites.
“ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows”
Brian Groenke, Luke Madaus and Claire Monteleoni · 2020
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“Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions”
Janni Yuval and Paul O’Gorman · 2020
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“Attention-based convolutional autoencoders for 3d-variational data assimilation”
Julian Mack, Rossella Arcucci, Miguel Molina-Solana and Yi-Ke Guo · 2020
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“Aviation Turbulence Forecasting at Upper Levels with Machine Learning Techniques Based on Regression Trees”
Domingo Muñoz-Esparza, Robert. Sharman and Wiebke Deierling · 2020
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“Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction” Publisher: American Meteorological Society Section: Monthly Weather Review
Ryan Lagerquist et al · 2020
Cited alongside, same era.
“Tackling Climate Change with Machine Learning”
David Rolnick et al · 2022
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“Flood forecasting with machine learning models in an operational framework”
S. Nevo et al · 2022
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“Masked autoencoders are scalable vision learners”
Kaiming He et al · 2022
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“Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training”
Zhan Tong, Yibing Song, Jue Wang and Limin Wang · 2022
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“ClimFormer–A Spherical Transformer Model for Long-term Climate Projections”
Salvaühling Cachay et al · 2022
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“Spherical Transformer”
Sungmin Cho, Raehyuk Jung and Junseok Kwon · 2022
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Gu-Feng Bian, Gao-Zhen Nie and Xin Qiu · 2020
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Jonathan Weyn, Dale Durran and Rich Caruana · 2020
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“Big bird: Transformers for longer sequences”
Manzil Zaheer et al · 2020
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“Learning to simulate complex physics with graph networks”
Alvaro Sanchez-Gonzalez et al · 2020
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Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez and Peter Battaglia · 2020
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“Fourier neural operator for parametric partial differential equations”
Zongyi Li et al · 2020
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“A simple framework for contrastive learning of visual representations”
Ting Chen, Simon Kornblith, Mohammad Norouzi and Geoffrey Hinton · 2020
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“ResGraphNet: GraphSAGE with embedded residual module for prediction of global monthly mean temperature”
Z. Chen, Z. Wang, Y. Yang and J. Gao · 2022
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