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references.bib
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@article{alexander1999spectral,
title={A spectral parameterization of mean-flow forcing due to breaking gravity waves},
author={Alexander, MJ and Dunkerton, TJ},
journal={Journal of the Atmospheric Sciences},
volume={56},
number={24},
pages={4167--4182},
year={1999}
}
@article{atkinson2024practical,
author = {Atkinson, Jack and Denholm, Jim},
doi = {10.21105/jose.00239},
journal = {Journal of Open Source Education},
month = jun,
number = {76},
pages = {239},
title = {{Practical machine learning with PyTorch}},
url = {https://jose.theoj.org/papers/10.21105/jose.00239},
volume = {7},
year = {2024}
}
@article{bi2022pangu,
title={Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast},
author={Bi, Kaifeng and Xie, Lingxi and Zhang, Hengheng and Chen, Xin and Gu, Xiaotao and Tian, Qi},
journal={arXiv preprint arXiv:2211.02556},
year={2022}
}
@article{bi2023accurate,
title={Accurate medium-range global weather forecasting with 3D neural networks},
author={Bi, Kaifeng and Xie, Lingxi and Zhang, Hengheng and Chen, Xin and Gu, Xiaotao and Tian, Qi},
journal={Nature},
pages={1--6},
year={2023},
publisher={Nature Publishing Group UK London}
}
@article{espinosa2022machine,
title={Machine learning gravity wave parameterization generalizes to capture the QBO and response to increased CO2},
author={Espinosa, Zachary I and Sheshadri, Aditi and Cain, Gerald R and Gerber, Edwin P and DallaSanta, Kevin J},
journal={Geophysical Research Letters},
volume={49},
number={8},
pages={e2022GL098174},
year={2022},
publisher={Wiley Online Library}
}
@unpublished{furner2023iterative,
title= {An iterative data-driven emulator of an ocean general circulation model},
author = {Furner, Rachel and Haynes, Peter and Munday, Dave and Paige, Brooks and Shuckburgh, Emily and others},
year={2023},
note= {EGU General Assembly},
}
@article{giglio2018estimating,
title={Estimating oxygen in the southern ocean using argo temperature and salinity},
author={Giglio, Donata and Lyubchich, Vyacheslav and Mazloff, Matthew R},
journal={Journal of Geophysical Research: Oceans},
volume={123},
number={6},
pages={4280--4297},
year={2018},
publisher={Wiley Online Library}
}
@article{harris2022generative,
title={A generative deep learning approach to stochastic downscaling of precipitation forecasts},
author={Harris, Lucy and McRae, Andrew TT and Chantry, Matthew and Dueben, Peter D and Palmer, Tim N},
journal={Journal of Advances in Modeling Earth Systems},
volume={14},
number={10},
pages={e2022MS003120},
year={2022},
publisher={Wiley Online Library}
}
@article{jucker2017untangling,
title={Untangling the annual cycle of the tropical tropopause layer with an idealized moist model},
author={Jucker, Martin and Gerber, EP},
journal={Journal of Climate},
volume={30},
number={18},
pages={7339--7358},
year={2017}
}
@article{kashinath2021physics,
title={Physics-informed machine learning: case studies for weather and climate modelling},
author={Kashinath, Karthik and Mustafa, M and Albert, Adrian and Wu, JL and Jiang, C and Esmaeilzadeh, Soheil and Azizzadenesheli, Kamyar and Wang, R and Chattopadhyay, A and Singh, A and others},
journal={Philosophical Transactions of the Royal Society A},
volume={379},
number={2194},
pages={20200093},
year={2021},
publisher={The Royal Society Publishing}
}
@inproceedings{ma2021data,
title={Data-driven discovery of the governing equations describing radiation belt dynamics},
author={Ma, Donglai and Bortnik, Jacob and Alves, Edurado and Camporeale, Enrico and Chu, Xiangning and Kellerman, Adam},
booktitle={AGU Fall Meeting Abstracts},
volume={2021},
pages={SA15B--1928},
year={2021}
}
@article{pathak2022fourcastnet,
title={Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators},
author={Pathak, Jaideep and Subramanian, Shashank and Harrington, Peter and Raja, Sanjeev and Chattopadhyay, Ashesh and Mardani, Morteza and Kurth, Thorsten and Hall, David and Li, Zongyi and Azizzadenesheli, Kamyar and others},
journal={arXiv preprint arXiv:2202.11214},
year={2022}
}
@article{rasp2020weatherbench,
title={WeatherBench: a benchmark data set for data-driven weather forecasting},
author={Rasp, Stephan and Dueben, Peter D and Scher, Sebastian and Weyn, Jonathan A and Mouatadid, Soukayna and Thuerey, Nils},
journal={Journal of Advances in Modeling Earth Systems},
volume={12},
number={11},
pages={e2020MS002203},
year={2020},
publisher={Wiley Online Library}
}
@article{shao2021deep,
title={A deep learning model for forecasting sea surface height anomalies and temperatures in the South China Sea},
author={Shao, Qi and Li, Wei and Han, Guijun and Hou, Guangchao and Liu, Siyuan and Gong, Yantian and Qu, Ping},
journal={Journal of Geophysical Research: Oceans},
volume={126},
number={7},
pages={e2021JC017515},
year={2021},
publisher={Wiley Online Library}
}
@article{sheridan2017mountain,
title={Mountain waves in high resolution forecast models: Automated diagnostics of wave severity and impact on surface winds},
author={Sheridan, Peter and Vosper, Simon and Brown, Philip},
journal={Atmosphere},
volume={8},
number={1},
pages={24},
year={2017},
publisher={MDPI}
}
@article{ukkonen2022exploring,
title={Exploring pathways to more accurate machine learning emulation of atmospheric radiative transfer},
author={Ukkonen, Peter},
journal={Journal of Advances in Modeling Earth Systems},
volume={14},
number={4},
pages={e2021MS002875},
year={2022},
publisher={Wiley Online Library}
}
@article{yuval2020stable,
title={Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions},
author={Yuval, Janni and O’Gorman, Paul A},
journal={Nature communications},
volume={11},
number={1},
pages={3295},
year={2020},
publisher={Nature Publishing Group UK London}
}
@article{zanna2020data,
title={Data-driven equation discovery of ocean mesoscale closures},
author={Zanna, Laure and Bolton, Thomas},
journal={Geophysical Research Letters},
volume={47},
number={17},
pages={e2020GL088376},
year={2020},
publisher={Wiley Online Library}
}