Ocean Data Assimilation with Background Error Covariance Derived from OGCM Outputs
The background error covariance plays an important role in modern data assimilation and analysissystems by determining the spatial spreading of information in the data. A novel method based on modeloutput is proposed to estimate background error covariance for use in Optimum Interpolation. At everym...
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| Published in | Advances in atmospheric sciences Vol. 21; no. 2; pp. 181 - 192 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Nature B.V
01.03.2004
Nansen-Zhu International Research Centre (NZC), Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0256-1530 1861-9533 |
| DOI | 10.1007/BF02915704 |
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| Abstract | The background error covariance plays an important role in modern data assimilation and analysissystems by determining the spatial spreading of information in the data. A novel method based on modeloutput is proposed to estimate background error covariance for use in Optimum Interpolation. At everymodel level, anisotropic correlation scales are obtained that give a more detailed description of the spatialcorrelation structure. Purthermore, the impact of the background field itself is included in the backgrounderror covariance. The methodology of the estimation is presented and the structure of the covariance isexamined, The results of 20-year assimilation experiments are compared with observations from TOGATAO (The Tropical Ocean-Global Atmosphere~Tropical Atmosphere Ocean) array and other analysis data. |
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| AbstractList | The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data. The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGATAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data. The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.[PUBLICATION ABSTRACT] The background error covariance plays an important role in modern data assimilation and analysissystems by determining the spatial spreading of information in the data. A novel method based on modeloutput is proposed to estimate background error covariance for use in Optimum Interpolation. At everymodel level, anisotropic correlation scales are obtained that give a more detailed description of the spatialcorrelation structure. Purthermore, the impact of the background field itself is included in the backgrounderror covariance. The methodology of the estimation is presented and the structure of the covariance isexamined, The results of 20-year assimilation experiments are compared with observations from TOGATAO (The Tropical Ocean-Global Atmosphere~Tropical Atmosphere Ocean) array and other analysis data. |
| Author | 符伟伟 周广庆 王会军 队干部 |
| AuthorAffiliation | Nansen-ZhuInternationalResearchCentre（NZC）.InstituteofAtmosphericPhysics,ChineseAcademyofSciences,Beijing100029 |
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| CitedBy_id | crossref_primary_10_1007_s13131_013_0333_1 crossref_primary_10_1007_BF02918510 crossref_primary_10_1007_s10236_008_0133_x crossref_primary_10_1007_s13143_015_0063_8 crossref_primary_10_1002_qj_4833 crossref_primary_10_5194_os_17_891_2021 crossref_primary_10_1007_s00376_007_0383_4 |
| Cites_doi | 10.1175/1520-0493(1987)115<1479:CGMFLS>2.0.CO;2 10.1002/qj.49711247414 10.1175/1520-0485(1983)013<1093:NMWSOT>2.0.CO;2 10.1175/1520-0426(1990)007<0233:AOGSOT>2.0.CO;2 10.1175/1520-0485(1989)019<1333:AGODAS>2.0.CO;2 10.1016/0377-0265(89)90049-3 10.1029/92JC00911 10.1175/1520-0493(1998)126<1013:AICMFE>2.0.CO;2 10.1175/1520-0485(1988)018<1713:DOSAIV>2.0.CO;2 10.1175/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2 10.1034/j.1600-0870.2000.d01-3.x 10.1175/1520-0485(1988)018<1601:AGCMFU>2.0.CO;2 10.1007/s00376-001-0047-8 10.1111/j.1600-0870.1986.tb00460.x 10.1016/0079-6611(91)90008-A 10.1002/qj.49712455003 10.1175/1520-0485(2000)030<0294:ASODAA>2.0.CO;2 10.1034/j.1600-0870.1998.00004.x |
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| References_xml | – volume: 115 start-page: 1479 year: 1987 ident: BF02915704_CR15 publication-title: Mon. Wea. Rev. doi: 10.1175/1520-0493(1987)115<1479:CGMFLS>2.0.CO;2 – volume: 112 start-page: 1177 year: 1986 ident: BF02915704_CR11 publication-title: Quart. J. Roy. Meteor. Soc. doi: 10.1002/qj.49711247414 – volume: 13 start-page: 1093 year: 1983 ident: BF02915704_CR8 publication-title: J. Phys. Oceanogr. doi: 10.1175/1520-0485(1983)013<1093:NMWSOT>2.0.CO;2 – volume: 7 start-page: 233 year: 1990 ident: BF02915704_CR4 publication-title: J. Atmos. Ocenanic Technol. doi: 10.1175/1520-0426(1990)007<0233:AOGSOT>2.0.CO;2 – volume: 19 start-page: 1333 year: 1989 ident: BF02915704_CR7 publication-title: J. Phys. Oceanogr. doi: 10.1175/1520-0485(1989)019<1333:AGODAS>2.0.CO;2 – start-page: 393 volume-title: Studies on Short-Term Climate Prediction System in China. II: Studies on Dynamical Models for Operational Short-Term Climate Prediction year: 2000 ident: BF02915704_CR21 – volume: 13 start-page: 441 year: 1989 ident: BF02915704_CR14 publication-title: Dyn. Atmos. Oceans doi: 10.1016/0377-0265(89)90049-3 – ident: BF02915704_CR6 – volume: 97 start-page: 11237 issue: C7 year: 1992 ident: BF02915704_CR20 publication-title: J. Geophys. Res. doi: 10.1029/92JC00911 – volume: 126 start-page: 1013 year: 1998 ident: BF02915704_CR1 publication-title: Mon. Wea. Rev. doi: 10.1175/1520-0493(1998)126<1013:AICMFE>2.0.CO;2 – ident: BF02915704_CR5 – volume: 18 start-page: 1713 year: 1988 ident: BF02915704_CR12 publication-title: J. Phys. Oceanogr. doi: 10.1175/1520-0485(1988)018<1713:DOSAIV>2.0.CO;2 – volume: 6 start-page: 929 year: 1994 ident: BF02915704_CR17 publication-title: J. Climate doi: 10.1175/1520-0442(1994)007<0929:IGSSTA>2.0.CO;2 – volume: 52A start-page: 265 year: 2000 ident: BF02915704_CR24 publication-title: Tellus doi: 10.1034/j.1600-0870.2000.d01-3.x – ident: BF02915704_CR10 – volume: 18 start-page: 1601 year: 1988 ident: BF02915704_CR19 publication-title: J. Phys. Oceanogr. doi: 10.1175/1520-0485(1988)018<1601:AGCMFU>2.0.CO;2 – volume: 18 start-page: 587 year: 2001 ident: BF02915704_CR23 publication-title: Adv. Atmos. Sci. doi: 10.1007/s00376-001-0047-8 – volume: 9 start-page: 374 year: 1999 ident: BF02915704_CR22 publication-title: Progress in Natural Sciences – volume: 38A start-page: 111 year: 1986 ident: BF02915704_CR9 publication-title: Tellus doi: 10.1111/j.1600-0870.1986.tb00460.x – volume: 28 start-page: 189 year: 1991 ident: BF02915704_CR13 publication-title: Progress in Oceanography doi: 10.1016/0079-6611(91)90008-A – volume: 124 start-page: 1809 year: 1998 ident: BF02915704_CR16 publication-title: Quart. J. Roy. Meteor. Soc. doi: 10.1002/qj.49712455003 – start-page: 912 volume-title: 11th Conference on Interations of the Sea and Atmosphere year: 2001 ident: BF02915704_CR2 – volume: 30 start-page: 294 year: 2000 ident: BF02915704_CR3 publication-title: J. Phys. Oceanogr. doi: 10.1175/1520-0485(2000)030<0294:ASODAA>2.0.CO;2 – volume: 50A start-page: 42 year: 1998 ident: BF02915704_CR18 publication-title: Tellus doi: 10.1034/j.1600-0870.1998.00004.x |
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| SubjectTerms | Atmosphere Data assimilation Data collection Meteorology 协方差 数字模拟 数据同化 海洋调查 |
| Title | Ocean Data Assimilation with Background Error Covariance Derived from OGCM Outputs |
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