Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments.
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| Title: | Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments. |
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| Authors: | Kong, Rong1, Xue, Ming1 mxue@ou.edu, Liu, Chengsi2 |
| Source: | Monthly Weather Review. Jan2018, Vol. 146 Issue 1, p175-198. 24p. |
| Subjects: | Radar meteorology, Decorrelation (Signal processing), Analysis of covariance, Hydrometeorology, Error analysis in mathematics |
| Abstract: | A hybrid ensemble-3DVar (En3DVar) system is developed and compared with 3DVar, EnKF, "deterministic Forecast" EnKF (DfEnKF), and pure En3DVar for assimilating radar data through perfect-model observing system simulation experiments (OSSEs). DfEnKF uses a deterministic forecast as the background and is therefore parallel to pure En3DVar. Different results are found between DfEnKF and pure En3DVar: 1) the serial versus global nature and 2) the variational minimization versus direct filter updating nature of the two algorithms are identified as the main causes for the differences. For 3DVar (EnKF/DfEnKF and En3DVar), optimal decorrelation scales (localization radii) for static (ensemble) background error covariances are obtained and used in hybrid En3DVar. The sensitivity of hybrid En3DVar to covariance weights and ensemble size is examined. On average, when ensemble size is 20 or larger, a 5%-10% static covariance gives the best results, while for smaller ensembles, more static covariance is beneficial. Using an ensemble size of 40, EnKF and DfEnKF perform similarly, and both are better than pure and hybrid En3DVar overall. Using 5% static error covariance, hybrid En3DVar outperforms pure En3DVar for most state variables but underperforms for hydrometeor variables, and the improvement (degradation) is most notable for water vapor mixing ratio qy (snow mixing ratio qs). Overall, EnKF/DfEnKF performs the best, 3DVar performs the worst, and static covariance only helps slightly via hybrid En3DVar. [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.) | |
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| Items | – Name: Title Label: Title Group: Ti Data: Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kong%2C+Rong%22">Kong, Rong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Xue%2C+Ming%22">Xue, Ming</searchLink><relatesTo>1</relatesTo><i> mxue@ou.edu</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Chengsi%22">Liu, Chengsi</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Monthly+Weather+Review%22">Monthly Weather Review</searchLink>. Jan2018, Vol. 146 Issue 1, p175-198. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Radar+meteorology%22">Radar meteorology</searchLink><br /><searchLink fieldCode="DE" term="%22Decorrelation+%28Signal+processing%29%22">Decorrelation (Signal processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+covariance%22">Analysis of covariance</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrometeorology%22">Hydrometeorology</searchLink><br /><searchLink fieldCode="DE" term="%22Error+analysis+in+mathematics%22">Error analysis in mathematics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A hybrid ensemble-3DVar (En3DVar) system is developed and compared with 3DVar, EnKF, "deterministic Forecast" EnKF (DfEnKF), and pure En3DVar for assimilating radar data through perfect-model observing system simulation experiments (OSSEs). DfEnKF uses a deterministic forecast as the background and is therefore parallel to pure En3DVar. Different results are found between DfEnKF and pure En3DVar: 1) the serial versus global nature and 2) the variational minimization versus direct filter updating nature of the two algorithms are identified as the main causes for the differences. For 3DVar (EnKF/DfEnKF and En3DVar), optimal decorrelation scales (localization radii) for static (ensemble) background error covariances are obtained and used in hybrid En3DVar. The sensitivity of hybrid En3DVar to covariance weights and ensemble size is examined. On average, when ensemble size is 20 or larger, a 5%-10% static covariance gives the best results, while for smaller ensembles, more static covariance is beneficial. Using an ensemble size of 40, EnKF and DfEnKF perform similarly, and both are better than pure and hybrid En3DVar overall. Using 5% static error covariance, hybrid En3DVar outperforms pure En3DVar for most state variables but underperforms for hydrometeor variables, and the improvement (degradation) is most notable for water vapor mixing ratio qy (snow mixing ratio qs). Overall, EnKF/DfEnKF performs the best, 3DVar performs the worst, and static covariance only helps slightly via hybrid En3DVar. [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-17-0164.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 175 Subjects: – SubjectFull: Radar meteorology Type: general – SubjectFull: Decorrelation (Signal processing) Type: general – SubjectFull: Analysis of covariance Type: general – SubjectFull: Hydrometeorology Type: general – SubjectFull: Error analysis in mathematics Type: general Titles: – TitleFull: Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kong, Rong – PersonEntity: Name: NameFull: Xue, Ming – PersonEntity: Name: NameFull: Liu, Chengsi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 00270644 Numbering: – Type: volume Value: 146 – Type: issue Value: 1 Titles: – TitleFull: Monthly Weather Review Type: main |
| ResultId | 1 |
