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Multimodel Ensemble Predictions of Precipitation using Bayesian Neural Networks...

by Ming Fan, Dan Lu, Deeksha Rastogi
Publication Type
Conference Paper
Journal Name
The International Conference on Learning Representations
Book Title
AI for Earth Sciences: #ai4earth at virtual @ICLR2022 April 29
Publication Date
Page Numbers
1 to 10
Publisher Location
California, United States of America
Conference Name
The International Conference on Learning Representations (ICLR): AI for Earth Sciences
Conference Location
Virtual, Tennessee, United States of America
Conference Sponsor
multiple organizations
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.