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| Titel |
The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model |
| VerfasserIn |
S. G. Penny, E. Kalnay, J. A. Carton, B. R. Hunt, K. Ide, T. Miyoshi, G. A. Chepurin |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 20, no. 6 ; Nr. 20, no. 6 (2013-11-26), S.1031-1046 |
| Datensatznummer |
250086077
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| Publikation (Nr.) |
 copernicus.org/npg-20-1031-2013.pdf |
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| Zusammenfassung |
The most widely used methods of data assimilation in large-scale
oceanography, such as the Simple Ocean Data Assimilation (SODA) algorithm,
specify the background error covariances and thus are unable to refine the
weights in the assimilation as the circulation changes. In contrast, the
more computationally expensive Ensemble Kalman Filters (EnKF) such as the
Local Ensemble Transform Kalman Filter (LETKF) use an ensemble of model
forecasts to predict changes in the background error covariances and thus
should produce more accurate analyses. The EnKFs are based on the
approximation that ensemble members reflect a Gaussian probability
distribution that is transformed linearly during the forecast and analysis
cycle. In the presence of nonlinearity, EnKFs can gain from replacing each
analysis increment by a sequence of smaller increments obtained by
recursively applying the forecast model and data assimilation procedure over
a single analysis cycle. This has led to the development of the "running in
place" (RIP) algorithm by Kalnay and Yang (2010) and Yang et al. (2012a,b) in
which the weights computed at the end of each analysis cycle are used
recursively to refine the ensemble at the beginning of the analysis cycle.
To date, no studies have been carried out with RIP in a global domain with
real observations.
This paper provides a comparison of the aforementioned assimilation methods
in a set of experiments spanning seven years (1997–2003) using identical
forecast models, initial conditions, and observation data. While the
emphasis is on understanding the similarities and differences between the
assimilation methods, comparisons are also made to independent ocean station
temperature, salinity, and velocity time series, as well as ocean
transports, providing information about the absolute error of each.
Comparisons to independent observations are similar for the assimilation
methods but the observation-minus-background temperature differences are
distinctly lower for LETKF and RIP. The results support the potential for
LETKF to improve the quality of ocean analyses on the space and timescales
of interest for seasonal prediction and for RIP to accelerate the spin up of
the system. |
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