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dc.contributor.authorLunderman, S.
dc.contributor.authorMorzfeld, M.
dc.contributor.authorPosselt, D.J.
dc.date.accessioned2021-07-22T00:47:44Z
dc.date.available2021-07-22T00:47:44Z
dc.date.issued2021
dc.identifier.citationLunderman, S., Morzfeld, M., & Posselt, D. J. (2021). Using global Bayesian optimization in ensemble data assimilation: Parameter estimation, tuning localization and inflation, or all of the above. Tellus, Series A: Dynamic Meteorology and Oceanography, 73(1), 1–16.
dc.identifier.issn0280-6495
dc.identifier.doi10.1080/16000870.2021.1924952
dc.identifier.urihttp://hdl.handle.net/10150/660965
dc.description.abstractGlobal Bayesian optimization (GBO) is a derivative-free optimization method that is used widely in the tech-industry to optimize objective functions that are expensive to evaluate, numerically or otherwise. We discuss the use of GBO in ensemble data assimilation (DA), where the goal is to update the state of a numerical model in view of noisy observations. Specifically, we consider three tasks: (i) the estimation of model parameters; (ii) the tuning of localization and inflation in ensemble DA; (iii) doing both, i.e. estimating model parameters while simultaneously tuning the localization and inflation of the ensemble DA. For all three tasks, the GBO works ‘offline’, i.e. a set of ‘training’ observations are used within GBO to determine appropriate model or localization/inflation parameters, which are subsequently deployed within an ensemble DA system. Because of the offline nature of the technique, GBO can easily be combined with existing DA systems and it can effectively decouple (nearly) linear/Gaussian aspects of a problem from highly nonlinear/non-Gaussian ones. We illustrate the use of GBO in simple numerical experiments with the classical Lorenz problems. Our main goals are to introduce GBO in the context of ensemble DA and to spark an interest in GBO and its uses for streamlining important tasks in ensemble DA. © Tellus A: 2021. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
dc.language.isoen
dc.publisherTaylor and Francis Ltd.
dc.rightsCopyright © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectdata assimilation
dc.subjectensemble Kalman filtering
dc.subjectglobal Bayesian optimization
dc.subjectparameter estimation
dc.titleUsing global Bayesian optimization in ensemble data assimilation: parameter estimation, tuning localization and inflation, or all of the above
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Mathematics, University of Arizona
dc.identifier.journalTellus, Series A: Dynamic Meteorology and Oceanography
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleTellus, Series A: Dynamic Meteorology and Oceanography
refterms.dateFOA2021-07-22T00:47:44Z


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Copyright © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/).
Except where otherwise noted, this item's license is described as Copyright © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/).