Articles | Volume 14, issue 8
Geosci. Model Dev., 14, 5205–5215, 2021
https://doi.org/10.5194/gmd-14-5205-2021
Geosci. Model Dev., 14, 5205–5215, 2021
https://doi.org/10.5194/gmd-14-5205-2021

Development and technical paper 18 Aug 2021

Development and technical paper | 18 Aug 2021

Copula-based synthetic data augmentation for machine-learning emulators

David Meyer et al.

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Cited articles

Aas, K., Czado, C., Frigessi, A., and Bakken, H.: Pair-copula constructions of multiple dependence, Insur. Math. Econ., 44, 182–198, https://doi.org/10.1016/j.insmatheco.2007.02.001, 2009. 
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: A System for Large-Scale Machine Learning, in: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, 265–283, 2016. 
Bolton, T. and Zanna, L.: Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization, J. Adv. Model. Earth Syst., 11, 376–399, https://doi.org/10.1029/2018MS001472, 2019. 
Brenowitz, N. D. and Bretherton, C. S.: Prognostic Validation of a Neural Network Unified Physics Parameterization, Geophys. Res. Lett., 45, 6289–6298, https://doi.org/10.1029/2018GL078510, 2018. 
Cheruy, F., Chevallier, F., Morcrette, J.-J., Scott, N. A., and Chédin, A.: Une méthode utilisant les techniques neuronales pour le calcul rapide de la distribution verticale du bilan radiatif thermique terrestre, Comptes Rendus de l'Academie des Sciences Serie II, 322, 665–672, hal-02954375, 1996. 
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Short summary
A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.