GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments.
Saved in:
| Title: | GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments. |
|---|---|
| Authors: | Wang, Xuguang, Parrish, David, Kleist, Daryl, Whitaker, Jeffrey |
| Source: | Monthly Weather Review. Nov2013, Vol. 141 Issue 11, p4098-4117. 20p. 1 Diagram, 2 Charts, 12 Graphs. |
| Subjects: | Computer simulation of weather forecasting, Kalman filtering, Interpolation, Meteorological satellites, National Centers for Environmental Prediction (U.S.) |
| Abstract: | An ensemble Kalman filter-variational hybrid data assimilation system based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) system was developed. The performance of the system was investigated using the National Centers for Environmental Prediction (NCEP) Global Forecast System model. Experiments covered a 6-week Northern Hemisphere winter period. Both the control and ensemble forecasts were run at the same, reduced resolution. Operational conventional and satellite observations along with an 80-member ensemble were used. Various configurations of the system including one- or two-way couplings, with zero or nonzero weights on the static covariance, were intercompared and compared with the GSI 3DVar system. It was found that the hybrid system produced more skillful forecasts than the GSI 3DVar system. The inclusion of a static component in the background-error covariance and recentering the analysis ensemble around the variational analysis did not improve the forecast skill beyond the one-way coupled system with zero weights on the static covariance. The one-way coupled system with zero static covariances produced more skillful wind forecasts averaged over the globe than the EnKF at the 1-5-day lead times and more skillful temperature forecasts than the EnKF at the 5-day lead time. Sensitivity tests indicated that the difference may be due to the use of the tangent linear normal mode constraint in the variational system. For the first outer loop, the hybrid system showed a slightly slower (faster) convergence rate at early (later) iterations than the GSI 3DVar system. For the second outer loop, the hybrid system showed a faster convergence. [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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 91667296 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Xuguang%22">Wang, Xuguang</searchLink><br /><searchLink fieldCode="AR" term="%22Parrish%2C+David%22">Parrish, David</searchLink><br /><searchLink fieldCode="AR" term="%22Kleist%2C+Daryl%22">Kleist, Daryl</searchLink><br /><searchLink fieldCode="AR" term="%22Whitaker%2C+Jeffrey%22">Whitaker, Jeffrey</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Monthly+Weather+Review%22">Monthly Weather Review</searchLink>. Nov2013, Vol. 141 Issue 11, p4098-4117. 20p. 1 Diagram, 2 Charts, 12 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+simulation+of+weather+forecasting%22">Computer simulation of weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Interpolation%22">Interpolation</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorological+satellites%22">Meteorological satellites</searchLink><br /><searchLink fieldCode="DE" term="%22National+Centers+for+Environmental+Prediction+%28U%2ES%2E%29%22">National Centers for Environmental Prediction (U.S.)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: An ensemble Kalman filter-variational hybrid data assimilation system based on the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) system was developed. The performance of the system was investigated using the National Centers for Environmental Prediction (NCEP) Global Forecast System model. Experiments covered a 6-week Northern Hemisphere winter period. Both the control and ensemble forecasts were run at the same, reduced resolution. Operational conventional and satellite observations along with an 80-member ensemble were used. Various configurations of the system including one- or two-way couplings, with zero or nonzero weights on the static covariance, were intercompared and compared with the GSI 3DVar system. It was found that the hybrid system produced more skillful forecasts than the GSI 3DVar system. The inclusion of a static component in the background-error covariance and recentering the analysis ensemble around the variational analysis did not improve the forecast skill beyond the one-way coupled system with zero weights on the static covariance. The one-way coupled system with zero static covariances produced more skillful wind forecasts averaged over the globe than the EnKF at the 1-5-day lead times and more skillful temperature forecasts than the EnKF at the 5-day lead time. Sensitivity tests indicated that the difference may be due to the use of the tangent linear normal mode constraint in the variational system. For the first outer loop, the hybrid system showed a slightly slower (faster) convergence rate at early (later) iterations than the GSI 3DVar system. For the second outer loop, the hybrid system showed a faster convergence. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=91667296 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/MWR-D-12-00141.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 4098 Subjects: – SubjectFull: Computer simulation of weather forecasting Type: general – SubjectFull: Kalman filtering Type: general – SubjectFull: Interpolation Type: general – SubjectFull: Meteorological satellites Type: general – SubjectFull: National Centers for Environmental Prediction (U.S.) Type: general Titles: – TitleFull: GSI 3DVar-Based Ensemble-Variational Hybrid Data Assimilation for NCEP Global Forecast System: Single-Resolution Experiments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Xuguang – PersonEntity: Name: NameFull: Parrish, David – PersonEntity: Name: NameFull: Kleist, Daryl – PersonEntity: Name: NameFull: Whitaker, Jeffrey IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 00270644 Numbering: – Type: volume Value: 141 – Type: issue Value: 11 Titles: – TitleFull: Monthly Weather Review Type: main |
| ResultId | 1 |
