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Copula-based synthetic data augmentation for machine-learning emulators
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AI
DOI：10.5194/gmd-14-5205-2021.png)
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Abstract
En 中文
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)).
Keywords:
NEURAL-NETWORKS
RADIATION
ACCURATE
CLIMATE
SCHEME
MODEL
Journal
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