A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model
A singular evolutive extended Kalman (SEEK) filter is used to assimilate real in situ data in a water column marine ecosystem model. The biogeochemistry of the ecosystem is described by the European Regional Sea Ecosystem Model (ERSEM), while the physical forcing is described by the Princeto...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2003-01-01
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| Series: | Annales Geophysicae |
| Online Access: | https://www.ann-geophys.net/21/389/2003/angeo-21-389-2003.pdf |
| _version_ | 1818988111673163776 |
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| author | I. Hoteit G. Triantafyllou G. Petihakis J. I. Allen |
| author_facet | I. Hoteit G. Triantafyllou G. Petihakis J. I. Allen |
| author_sort | I. Hoteit |
| collection | DOAJ |
| description | A singular evolutive
extended Kalman (SEEK) filter is used to assimilate real in situ data in a
water column marine ecosystem model. The biogeochemistry of the ecosystem is
described by the European Regional Sea Ecosystem Model (ERSEM), while the
physical forcing is described by the Princeton Ocean Model (POM). In the SEEK
filter, the error statistics are parameterized by means of a suitable basis of
empirical orthogonal functions (EOFs). The purpose of this contribution is to
track the possibility of using data assimilation techniques for state
estimation in marine ecosystem models. In the experiments, real oxygen and
nitrate data are used and the results evaluated against independent chlorophyll
data. These data were collected from an offshore station at three different
depths for the needs of the MFSPP project. The assimilation results show a
continuous decrease in the estimation error and a clear improvement in the
model behavior.</p>
<b>
<p style="line-height: 20px;">Key words. </b>Oceanography: general
(ocean prediction; numerical modelling) – Oceanography: biological and
chemical (ecosystems and ecology) |
| first_indexed | 2024-12-20T19:17:23Z |
| format | Article |
| id | doaj.art-09657e75cd1643b7a7206662b674dba0 |
| institution | Directory Open Access Journal |
| issn | 0992-7689 1432-0576 |
| language | English |
| last_indexed | 2024-12-20T19:17:23Z |
| publishDate | 2003-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Annales Geophysicae |
| spelling | doaj.art-09657e75cd1643b7a7206662b674dba02022-12-21T19:29:05ZengCopernicus PublicationsAnnales Geophysicae0992-76891432-05762003-01-012138939710.5194/angeo-21-389-2003A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem modelI. Hoteit0G. Triantafyllou1G. Petihakis2J. I. Allen3Scripps Institution of Oceanography, 8810 Shell Back Way , La Jolla, California, 92037, USAInstitute of Marine Biology of Crete, P.O.Box 2214, Iraklio, 71003 Crete, GreeceInstitute of Marine Biology of Crete, P.O.Box 2214, Iraklio, 71003 Crete, GreecePlymouth Marine Laboratory, Prospect Place, West Hoe, Plymouth, PL1 3DH, UKA singular evolutive extended Kalman (SEEK) filter is used to assimilate real in situ data in a water column marine ecosystem model. The biogeochemistry of the ecosystem is described by the European Regional Sea Ecosystem Model (ERSEM), while the physical forcing is described by the Princeton Ocean Model (POM). In the SEEK filter, the error statistics are parameterized by means of a suitable basis of empirical orthogonal functions (EOFs). The purpose of this contribution is to track the possibility of using data assimilation techniques for state estimation in marine ecosystem models. In the experiments, real oxygen and nitrate data are used and the results evaluated against independent chlorophyll data. These data were collected from an offshore station at three different depths for the needs of the MFSPP project. The assimilation results show a continuous decrease in the estimation error and a clear improvement in the model behavior.</p> <b> <p style="line-height: 20px;">Key words. </b>Oceanography: general (ocean prediction; numerical modelling) – Oceanography: biological and chemical (ecosystems and ecology)https://www.ann-geophys.net/21/389/2003/angeo-21-389-2003.pdf |
| spellingShingle | I. Hoteit G. Triantafyllou G. Petihakis J. I. Allen A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model Annales Geophysicae |
| title | A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model |
| title_full | A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model |
| title_fullStr | A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model |
| title_full_unstemmed | A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model |
| title_short | A singular evolutive extended Kalman filter to assimilate real in situ data in a 1-D marine ecosystem model |
| title_sort | singular evolutive extended kalman filter to assimilate real in situ data in a 1 d marine ecosystem model |
| url | https://www.ann-geophys.net/21/389/2003/angeo-21-389-2003.pdf |
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