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arXiv · 2012.09037

Copula-based synthetic data augmentation for machine-learning emulators

David Meyer·Thomas Nagler·Robin J. Hogan
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Abstract

Can we improve machine-learning (ML) emulators with synthetic data? If data are scarce or expensive to source and a physical model is available, statistically generated data may be useful for augmenting training sets cheaply. Here we explore the use of copula-based models for generating synthetically augmented datasets in weather and climate by testing the method on a toy physical model of downwelling longwave radiation and corresponding neural network emulator. Results show that for copula-augmented datasets, predictions are improved by up to 62 % for the mean absolute error (from 1.17 to 0.44 W m$^{-2}$).

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David Meyer, Thomas Nagler, Robin J. Hogan. 2020-12-16. Copula-based synthetic data augmentation for machine-learning emulators. https://doi.org/10.5194/gmd-14-5205-2021

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