Improving High-Latitude Sea Surface Height Data Assimilation: Model Based Vertical Covariance.
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| Title: | Improving High-Latitude Sea Surface Height Data Assimilation: Model Based Vertical Covariance. |
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| Authors: | Helber, Robert W.1 (AUTHOR) robert.w.helber.civ@us.navy.mil, Douglass, Elizabeth M.1 (AUTHOR), Xu, Xiaobiao2 (AUTHOR), McClean, Julie L.3 (AUTHOR), Chassignet, Eric P.2 (AUTHOR), Wallcraft, Alan J.2 (AUTHOR), Bozec, Alexandra2 (AUTHOR) |
| Source: | Journal of Atmospheric & Oceanic Technology. May2026, Vol. 43 Issue 5, p1-21. 21p. |
| Subjects: | Data assimilation, Covariance matrices, Oceanography, Variational approach (Mathematics), Ocean dynamics, Satellite-based remote sensing, United States. Navy, Halocline |
| Abstract: | The most important source of information constraining the Navy's operational global ocean forecasting system is sea surface height anomaly (SSHA) as measured by satellite altimetry. These surface observations inform a one-dimensional (1D) variational analysis to create synthetic profiles of temperature and salinity, which approximate the subsurface ocean structure associated with the observed SSHA that is assimilated in a three-dimensional variational analysis. The 1D analysis requires vertical error covariances that relate the differences in values between temperature and salinity at different depths. These vertical covariances are computed empirically from historical in situ observation profiles of temperature and salinity. The approach ensures that the assimilated profiles have realistic structure without drifting. A shortcoming of this approach is the availability of in situ observations extending at least 1000 m deep. Observations are sparser at high latitudes, often do not include salinity, and reach relatively shallow depths. We wish to use model data to address these limitations. Here we show that using a global 30-year model run to compute vertical covariances solves sampling issues while continuing to maintain accuracy. While the covariances derived from the model generally compare well with the observed ones, in some areas of the ocean, the numerical ocean model has different vertical covariances. A new method for determining where synthetics are most valuable is presented. The implication of having model derived covariances is the ability to extend covariance information at high latitude where in situ observations are sparce or have sampling anomalies. Results also suggest that salinity, if observed, would provide substantial improvement to the system. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Atmospheric & Oceanic Technology 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194578177 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Improving High-Latitude Sea Surface Height Data Assimilation: Model Based Vertical Covariance. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Helber%2C+Robert+W%2E%22">Helber, Robert W.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> robert.w.helber.civ@us.navy.mil</i><br /><searchLink fieldCode="AR" term="%22Douglass%2C+Elizabeth+M%2E%22">Douglass, Elizabeth M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Xiaobiao%22">Xu, Xiaobiao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22McClean%2C+Julie+L%2E%22">McClean, Julie L.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chassignet%2C+Eric+P%2E%22">Chassignet, Eric P.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wallcraft%2C+Alan+J%2E%22">Wallcraft, Alan J.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bozec%2C+Alexandra%22">Bozec, Alexandra</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Atmospheric+%26+Oceanic+Technology%22">Journal of Atmospheric & Oceanic Technology</searchLink>. May2026, Vol. 43 Issue 5, p1-21. 21p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+assimilation%22">Data assimilation</searchLink><br /><searchLink fieldCode="DE" term="%22Covariance+matrices%22">Covariance matrices</searchLink><br /><searchLink fieldCode="DE" term="%22Oceanography%22">Oceanography</searchLink><br /><searchLink fieldCode="DE" term="%22Variational+approach+%28Mathematics%29%22">Variational approach (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+dynamics%22">Ocean dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Satellite-based+remote+sensing%22">Satellite-based remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22United+States%2E+Navy%22">United States. Navy</searchLink><br /><searchLink fieldCode="DE" term="%22Halocline%22">Halocline</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The most important source of information constraining the Navy's operational global ocean forecasting system is sea surface height anomaly (SSHA) as measured by satellite altimetry. These surface observations inform a one-dimensional (1D) variational analysis to create synthetic profiles of temperature and salinity, which approximate the subsurface ocean structure associated with the observed SSHA that is assimilated in a three-dimensional variational analysis. The 1D analysis requires vertical error covariances that relate the differences in values between temperature and salinity at different depths. These vertical covariances are computed empirically from historical in situ observation profiles of temperature and salinity. The approach ensures that the assimilated profiles have realistic structure without drifting. A shortcoming of this approach is the availability of in situ observations extending at least 1000 m deep. Observations are sparser at high latitudes, often do not include salinity, and reach relatively shallow depths. We wish to use model data to address these limitations. Here we show that using a global 30-year model run to compute vertical covariances solves sampling issues while continuing to maintain accuracy. While the covariances derived from the model generally compare well with the observed ones, in some areas of the ocean, the numerical ocean model has different vertical covariances. A new method for determining where synthetics are most valuable is presented. The implication of having model derived covariances is the ability to extend covariance information at high latitude where in situ observations are sparce or have sampling anomalies. Results also suggest that salinity, if observed, would provide substantial improvement to the system. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Atmospheric & Oceanic Technology 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/JTECH-D-25-0014.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1 Subjects: – SubjectFull: Data assimilation Type: general – SubjectFull: Covariance matrices Type: general – SubjectFull: Oceanography Type: general – SubjectFull: Variational approach (Mathematics) Type: general – SubjectFull: Ocean dynamics Type: general – SubjectFull: Satellite-based remote sensing Type: general – SubjectFull: United States. Navy Type: general – SubjectFull: Halocline Type: general Titles: – TitleFull: Improving High-Latitude Sea Surface Height Data Assimilation: Model Based Vertical Covariance. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Helber, Robert W. – PersonEntity: Name: NameFull: Douglass, Elizabeth M. – PersonEntity: Name: NameFull: Xu, Xiaobiao – PersonEntity: Name: NameFull: McClean, Julie L. – PersonEntity: Name: NameFull: Chassignet, Eric P. – PersonEntity: Name: NameFull: Wallcraft, Alan J. – PersonEntity: Name: NameFull: Bozec, Alexandra IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 07390572 Numbering: – Type: volume Value: 43 – Type: issue Value: 5 Titles: – TitleFull: Journal of Atmospheric & Oceanic Technology Type: main |
| ResultId | 1 |
