Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data-Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM

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dc.citation.articleNumbere2023MS004145en_US
dc.citation.issueNumber7en_US
dc.citation.journalTitleJournal of Advances in Modeling Earth Systemsen_US
dc.citation.volumeNumber16en_US
dc.contributor.authorSun, Y. Qiangen_US
dc.contributor.authorPahlavan, Hamid A.en_US
dc.contributor.authorChattopadhyay, Asheshen_US
dc.contributor.authorHassanzadeh, Pedramen_US
dc.contributor.authorLubis, Sandro W.en_US
dc.contributor.authorAlexander, M. Joanen_US
dc.contributor.authorGerber, Edwin P.en_US
dc.contributor.authorSheshadri, Aditien_US
dc.contributor.authorGuan, Yifeien_US
dc.date.accessioned2024-08-09T16:25:23Zen_US
dc.date.available2024-08-09T16:25:23Zen_US
dc.date.issued2024en_US
dc.description.abstractNeural networks (NNs) are increasingly used for data-driven subgrid-scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non-linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large-amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN-based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics-based gravity wave (GW) parameterizations as a test case. WACCM has complex, state-of-the-art parameterizations for orography-, convection-, and front-driven GWs. Convection- and orography-driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto-encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO2). However, their performance is significantly improved by applying transfer learning, for example, re-training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data-driven parameterizations for various processes, including (but not limited to) GWs.en_US
dc.identifier.citationSun, Y. Q., Pahlavan, H. A., Chattopadhyay, A., Hassanzadeh, P., Lubis, S. W., Alexander, M. J., Gerber, E. P., Sheshadri, A., & Guan, Y. (2024). Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data-Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM. Journal of Advances in Modeling Earth Systems, 16(7), e2023MS004145. https://doi.org/10.1029/2023MS004145en_US
dc.identifier.digitalData-Imbalanceen_US
dc.identifier.doihttps://doi.org/10.1029/2023MS004145en_US
dc.identifier.urihttps://hdl.handle.net/1911/117620en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleData Imbalance, Uncertainty Quantification, and Transfer Learning in Data-Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCMen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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