Reply on RC3 Article Swipe

PDF

Related Concepts

Downwelling Longwave Copula (linguistics) Artificial neural network Computer science Artificial intelligence Machine learning Radiation Econometrics Mathematics Physics Geology Quantum mechanics Oceanography Upwelling

David J. Meyer ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.5194/gmd-2020-427-ac4 · OA: W4231298806

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 62â% for the mean absolute error (from 1.17 to 0.44âWâmâ2).