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Status: Unchecked ×Keyword: Bayesian neural networks ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM

   Sun, Pahlavan, Chattopadhyay et al. · 2023

   The paper tests ways to handle data imbalance, uncertainty and warmer-climate generalization in neural network emulators of gravity wave parameterizations from the WACCM climate model.

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   1. UncheckedResampling and/or weighted loss functions let neural networks emulate WACCM gravity wave parameterizations for orography, convection and fronts.“We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources.”
   2. UncheckedThree uncertainty methods gave ensemble spreads that matched accuracy in testing, showing when a neural network emulating gravity waves may be wrong.“We demonstrate that three UQ methods (Bayesian NNs, variational auto-encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria on when a NN gives inaccurate predictions.”
   3. UncheckedRetraining just one layer of a neural-network emulator on about 1% new warmer-climate data significantly improves its accuracy in that climate.“However, the generalization accuracy is significantly improved by applying transfer learning, e.g., re-training only one layer using ~1% new data from the warmer climate.”