Adaptive Correlation- and Distance-Based Localization for Iterative Ensemble Smoothers in a Coupled Nonlinear Multiscale Model.
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| Title: | Adaptive Correlation- and Distance-Based Localization for Iterative Ensemble Smoothers in a Coupled Nonlinear Multiscale Model. |
|---|---|
| Authors: | Vossepoel, Femke C.1 (AUTHOR) f.c.vossepoel@tudelft.nl, Evensen, Geir2,3 (AUTHOR), van Leeuwen, Peter Jan4 (AUTHOR) |
| Source: | Monthly Weather Review. Nov2025, Vol. 153 Issue 11, p2593-2609. 17p. |
| Subjects: | Correlation methods (Signal processing), Localization theory, Ensemble learning, Multiscale modeling, Measurement errors |
| Abstract: | This paper extends the 2024 study of iterative ensemble smoothers by Evensen et al., who used a sizeable 1000-member ensemble configuration, to now using smaller, more affordable ensemble sizes with localization. As is well known, localization is needed to increase the effective ensemble size and avoid degradation of the smoother solutions by spurious correlations. As an alternative to the standard distance-based localization, we propose a reformulation of an adaptive correlation-based localization method that, in a local update, considers only those observations for which the absolute value of the correlation to the model counterpart is larger than a user-defined threshold. In the standard distance-based localization, we update model variables using only nearby observations in physical distance. In correlation-based localization, we update variables using only observations with small correlation distances. We define the correlation distance as one minus the absolute value of the ensemble correlation between a predicted measurement and the variable we are updating. Using the same formulation and implementation as in the 2024 Evensen et al. study, we compare the performance of the two localization strategies in a coupled nonlinear multiscale model and demonstrate the better or at least comparable performance of the adaptive correlation-based localization. We attribute this to an additional measurement error variance inflation for the measurements with a correlation distance close to the truncation distance, effectively leading to smoother updates. Furthermore, it solves the problem of space–time localization that is hard to solve using localization based on physical distance in ensemble smoothers over longer time windows. We also discuss strategies for the efficient implementation of the correlation-based approach. [ABSTRACT FROM AUTHOR] |
| Copyright of Monthly Weather Review is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189362719 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive Correlation- and Distance-Based Localization for Iterative Ensemble Smoothers in a Coupled Nonlinear Multiscale Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Vossepoel%2C+Femke+C%2E%22">Vossepoel, Femke C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> f.c.vossepoel@tudelft.nl</i><br /><searchLink fieldCode="AR" term="%22Evensen%2C+Geir%22">Evensen, Geir</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22van+Leeuwen%2C+Peter+Jan%22">van Leeuwen, Peter Jan</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Monthly+Weather+Review%22">Monthly Weather Review</searchLink>. Nov2025, Vol. 153 Issue 11, p2593-2609. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Correlation+methods+%28Signal+processing%29%22">Correlation methods (Signal processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Localization+theory%22">Localization theory</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Multiscale+modeling%22">Multiscale modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+errors%22">Measurement errors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper extends the 2024 study of iterative ensemble smoothers by Evensen et al., who used a sizeable 1000-member ensemble configuration, to now using smaller, more affordable ensemble sizes with localization. As is well known, localization is needed to increase the effective ensemble size and avoid degradation of the smoother solutions by spurious correlations. As an alternative to the standard distance-based localization, we propose a reformulation of an adaptive correlation-based localization method that, in a local update, considers only those observations for which the absolute value of the correlation to the model counterpart is larger than a user-defined threshold. In the standard distance-based localization, we update model variables using only nearby observations in physical distance. In correlation-based localization, we update variables using only observations with small correlation distances. We define the correlation distance as one minus the absolute value of the ensemble correlation between a predicted measurement and the variable we are updating. Using the same formulation and implementation as in the 2024 Evensen et al. study, we compare the performance of the two localization strategies in a coupled nonlinear multiscale model and demonstrate the better or at least comparable performance of the adaptive correlation-based localization. We attribute this to an additional measurement error variance inflation for the measurements with a correlation distance close to the truncation distance, effectively leading to smoother updates. Furthermore, it solves the problem of space–time localization that is hard to solve using localization based on physical distance in ensemble smoothers over longer time windows. We also discuss strategies for the efficient implementation of the correlation-based approach. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Monthly Weather Review is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/MWR-D-24-0269.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 2593 Subjects: – SubjectFull: Correlation methods (Signal processing) Type: general – SubjectFull: Localization theory Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Multiscale modeling Type: general – SubjectFull: Measurement errors Type: general Titles: – TitleFull: Adaptive Correlation- and Distance-Based Localization for Iterative Ensemble Smoothers in a Coupled Nonlinear Multiscale Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vossepoel, Femke C. – PersonEntity: Name: NameFull: Evensen, Geir – PersonEntity: Name: NameFull: van Leeuwen, Peter Jan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00270644 Numbering: – Type: volume Value: 153 – Type: issue Value: 11 Titles: – TitleFull: Monthly Weather Review Type: main |
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