Using lagged covariances in data assimilation
This paper describes a novel method to incorporate significantly time-lagged data into a sequential variational data assimilation framework. The proposed method can assimilate data that appear many assimilation window lengths in the future, providing a mechanism to gradually dynamically adjust the m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Stockholm University Press
2017-01-01
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| Series: | Tellus: Series A, Dynamic Meteorology and Oceanography |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/16000870.2017.1377589 |
| _version_ | 1818247168575668224 |
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| author | C. M. Thomas K. Haines |
| author_facet | C. M. Thomas K. Haines |
| author_sort | C. M. Thomas |
| collection | DOAJ |
| description | This paper describes a novel method to incorporate significantly time-lagged data into a sequential variational data assimilation framework. The proposed method can assimilate data that appear many assimilation window lengths in the future, providing a mechanism to gradually dynamically adjust the model towards those data. The method avoids the need for an adjoint model, significantly reducing computational requirements compared to standard four-dimensional variational assimilation. Simulation studies are used to test the assimilation methodology in a variety of situations. The use of lagged covariances is shown to provide robust improvements to the assimilation quality, particularly if data at multiple lags are used to influence the cost function in each window. The methodology developed can be used to improve contemporary global reanalyses by incorporating time-lagged observations that may otherwise not be exploited to their full potential. |
| first_indexed | 2024-12-12T15:00:25Z |
| format | Article |
| id | doaj.art-082edaf1591341a9a4a168257b8b9f92 |
| institution | Directory Open Access Journal |
| issn | 1600-0870 |
| language | English |
| last_indexed | 2024-12-12T15:00:25Z |
| publishDate | 2017-01-01 |
| publisher | Stockholm University Press |
| record_format | Article |
| series | Tellus: Series A, Dynamic Meteorology and Oceanography |
| spelling | doaj.art-082edaf1591341a9a4a168257b8b9f922022-12-22T00:20:49ZengStockholm University PressTellus: Series A, Dynamic Meteorology and Oceanography1600-08702017-01-0169110.1080/16000870.2017.13775891377589Using lagged covariances in data assimilationC. M. Thomas0K. Haines1University of ReadingUniversity of ReadingThis paper describes a novel method to incorporate significantly time-lagged data into a sequential variational data assimilation framework. The proposed method can assimilate data that appear many assimilation window lengths in the future, providing a mechanism to gradually dynamically adjust the model towards those data. The method avoids the need for an adjoint model, significantly reducing computational requirements compared to standard four-dimensional variational assimilation. Simulation studies are used to test the assimilation methodology in a variety of situations. The use of lagged covariances is shown to provide robust improvements to the assimilation quality, particularly if data at multiple lags are used to influence the cost function in each window. The methodology developed can be used to improve contemporary global reanalyses by incorporating time-lagged observations that may otherwise not be exploited to their full potential.http://dx.doi.org/10.1080/16000870.2017.1377589long-window data assimilationlagged covariancesreanalysis |
| spellingShingle | C. M. Thomas K. Haines Using lagged covariances in data assimilation Tellus: Series A, Dynamic Meteorology and Oceanography long-window data assimilation lagged covariances reanalysis |
| title | Using lagged covariances in data assimilation |
| title_full | Using lagged covariances in data assimilation |
| title_fullStr | Using lagged covariances in data assimilation |
| title_full_unstemmed | Using lagged covariances in data assimilation |
| title_short | Using lagged covariances in data assimilation |
| title_sort | using lagged covariances in data assimilation |
| topic | long-window data assimilation lagged covariances reanalysis |
| url | http://dx.doi.org/10.1080/16000870.2017.1377589 |
| work_keys_str_mv | AT cmthomas usinglaggedcovariancesindataassimilation AT khaines usinglaggedcovariancesindataassimilation |