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Titel |
Application of a hybrid EnKF-OI to ocean forecasting |
VerfasserIn |
F. Counillon, P. Sakov, L. Bertino |
Konferenz |
EGU General Assembly 2009
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Medientyp |
Artikel
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Sprache |
Englisch
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Digitales Dokument |
PDF |
Erschienen |
In: GRA - Volume 11 (2009) |
Datensatznummer |
250026873
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Zusammenfassung |
Data assimilation methods often use an ensemble to represent the background error
covariance. Two approaches are commonly used; a simple one with a static ensemble, or a
more advanced one with a dynamic ensemble. The latter is often non-practical due to its
high computational requirements. Some recent studies suggested using a hybrid
covariance, which is a linear combination of the covariances represented by a static and a
dynamic ensemble. Here, the use of the hybrid covariance is first extensively tested
with a quasi-geostrophic model and with different analysis schemes, namely the
Ensemble Kalman Filter (EnKF) and the Ensemble Square Root Filter (ESRF).
The hybrid covariance ESRF (ESRF-OI) is more accurate and more stable than
the hybrid covariance EnKF (EnKF-OI), but the overall conclusions are similar
regardless of the analysis scheme used. The benefits of using the hybrid covariance are
large compared to both the static and the dynamic methods with a small dynamic
ensemble. The benefits over the dynamic methods become negligible, but remain, for
large dynamic ensembles. The optimal value of the hybrid blending coefficient
appears to decrease exponentially with the size of the dynamic ensemble. Finally, we
consider a realistic application with the assimilation of altimetry data in a hybrid
coordinate ocean model (HYCOM) for the Gulf of Mexico, during the shedding of
Eddy Yankee (2006). A 10-member EnKF-OI is compared to a 10-member EnKF
and a static method called the Ensemble Optimal Interpolation (EnOI). While 10
members seems insufficient for running the EnKF, the 10-member EnKF-OI reduces
the forecast error compared to the EnOI, and improves the positions of the fronts. |
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