Articles | Volume 17, issue 8
https://doi.org/10.5194/gmd-17-3533-2024
https://doi.org/10.5194/gmd-17-3533-2024
Model description paper
 | 
02 May 2024
Model description paper |  | 02 May 2024

Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1

Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins

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Cited articles

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Beucler, T., Pritchard, M., Gentine, P., and Rasp, S.: Towards Physically-Consistent, Data-Driven Models of Convection, in: IGARSS 2020 – 2020 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Waikoloa, HI, USA, 3987–3990, https://doi.org/10.1109/igarss39084.2020.9324569, 2020. a
Beucler, T., Pritchard, M., Yuval, J., Gupta, A., Peng, L., Rasp, S., Ahmed, F., O'Gorman, P. A., Neelin, J. D., Lutsko, N. J., and Gentine, P.: Climate-Invariant Machine Learning, arxiv [preprint], https://doi.org/10.48550/ARXIV.2112.08440, 2021. a, b
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Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.