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Multimodel ensemble predictions of precipitation using bayesian neural networks...

by Ming Fan, Dan Lu, Deeksha Rastogi
Publication Type
Conference Paper
Book Title
AI for Earth Sciences #ai4earth at virtual @ICLR2022 April 29
Publication Date
Page Number
Conference Name
ICLR2022- AI for Earth and Space Science Workshop
Conference Location
Online, California, United States of America
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Conference Date

Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models. BNN infers spatiotemporally varying model weights and biases through the calibration against observations. This ensemble scheme of BNN sufficiently leverages individual model skill for accurate predictions as well as provides interpretability about which models contribute more to the ensemble prediction at which locations and times to inform model development. Additionally, BNN accurately quantifies epistemic uncertainty to avoid overconfident projections. We demonstrate BNN’s superior prediction performance to three state-of-the-art ensemble approaches and discuss its interpretability and uncertainty quantification.