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ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models [article]

Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker, David Rolnick
2021 arXiv   pre-print

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https://web.archive.org/web/20211201011539/https://arxiv.org/pdf/2111.14671v1.pdf

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Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators.  ...  However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking.  ...  Acknowledgements This research was in part supported by a Canada CIFAR AI Chair and funding has been provided by Environment and Climate Change Canada.  ... 
arXiv:2111.14671v1 fatcat:rte6f5xvfvgifedloly4gjccy4
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Citation

Salva Rühling Cachay, Venkatesh Ramesh, Jason N. S. Cole, Howard Barker, David Rolnick. "ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models." arXiv (2021)