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Titel |
Impacts of mean dynamic topography on a regional ocean assimilation system |
VerfasserIn |
C. Yan, J. Zhu, C. A. S. Tanajura |
Medientyp |
Artikel
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Sprache |
Englisch
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ISSN |
1812-0784
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Digitales Dokument |
URL |
Erschienen |
In: Ocean Science ; 11, no. 5 ; Nr. 11, no. 5 (2015-10-14), S.829-837 |
Datensatznummer |
250117296
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Publikation (Nr.) |
copernicus.org/os-11-829-2015.pdf |
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Zusammenfassung |
An ocean data assimilation system was
developed for the Pacific–Indian oceans with the aim of assimilating
altimetry data, sea surface temperature, and in situ measurements from Argo
(Array for Real-time Geostrophic Oceanography), XBT (expendable
bathythermographs), CTD (conductivity temperature depth), and TAO (Tropical
Atmosphere Ocean). The altimetry data assimilation requires the addition of
the mean dynamic topography to the altimetric sea level anomaly to match the
model sea surface height. The mean dynamic topography is usually computed
from the model long-term mean sea surface height, and is also available from
gravimetric satellite data. In this study, the impact of different mean
dynamic topographies on the sea level anomaly assimilation is
examined. Results show that impacts of the mean dynamic
topography cannot be neglected. The mean dynamic topography from the model
long-term mean sea surface height without assimilating in situ observations
results in worsened subsurface temperature and salinity estimates. Even if
all available observations including in situ measurements, sea surface
temperature measurements, and altimetry data are assimilated, the estimates
are still not improved. This proves the significant impact of the MDT (mean
dynamic topography) on the analysis system, as the other types of
observations do not compensate for the shortcoming due to the altimetry data
assimilation. The gravimeter-based mean dynamic topography results in a good
estimate compared with that of the experiment without assimilation. The mean
dynamic topography computed from the model long-term mean sea surface height
after assimilating in situ observations presents better results. |
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