Ensembling geophysical models with Bayesian Neural Networks
Ushnish Sengupta,
Matt Amos,
J. Scott Hosking
et al.
Abstract:Ensembles of geophysical models improve prediction accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bayesian Neural Networks, which infers spatiotemporally varying model weights and bias, while accounting for heteroscedastic uncertainties in the observations. This produces more accurate and uncertaintyaware predictions without sacrificing interpretability. Applied to the prediction of total column ozone from an ensemble of 15 chemistr… Show more
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