Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3785-2023
https://doi.org/10.5194/gmd-16-3785-2023
Model description paper
 | 
11 Jul 2023
Model description paper |  | 11 Jul 2023

Deep learning for stochastic precipitation generation – deep SPG v1.0

Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier

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Cited articles

Ahn, K.-H.: Coupled annual and daily multivariate and multisite stochastic weather generator to preserve low-and high-frequency variability to assess climate vulnerability, J. Hydrol., 581, 124443, https://doi.org/10.1016/j.jhydrol.2019.124443, 2020. a
Ailliot, P., Allard, D., Monbet, V., and Naveau, P.: Stochastic weather generators: an overview of weather type models, Journal de la Société Française de Statistique, 156, 101–113, 2015. a
Ba, J. L., Kiros, J. R., and Hinton, G. E.: Layer normalization, arXiv preprint arXiv:1607.06450, 2016. a
Bachlechner, T., Majumder, B. P., Mao, H., Cottrell, G., and McAuley, J.: Rezero is all you need: Fast convergence at large depth, in: Uncertainty in Artificial Intelligence, 1352–1361, PMLR, 2021. a
Bird, L. and Walker, M.: bodekerscientific/SPG: Release version 1.0 (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.6801733, 2022. a
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Deriving the statistics of expected future changes in extreme precipitation is challenging due...
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