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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Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.