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| Titel |
Joint state and parameter estimation with an iterative ensemble Kalman smoother |
| VerfasserIn |
M. Bocquet, P. Sakov |
| 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. 5 ; Nr. 20, no. 5 (2013-10-23), S.803-818 |
| Datensatznummer |
250086057
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| Publikation (Nr.) |
 copernicus.org/npg-20-803-2013.pdf |
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| Zusammenfassung |
Both ensemble filtering and variational data assimilation methods have proven
useful in the joint estimation of state variables and parameters of
geophysical models. Yet, their respective benefits and drawbacks in this task
are distinct. An ensemble variational method, known as the iterative ensemble
Kalman smoother (IEnKS) has recently been introduced. It is based on an
adjoint model-free variational, but flow-dependent, scheme. As such, the IEnKS
is a candidate tool for joint state and parameter estimation that may inherit
the benefits from both the ensemble filtering and variational approaches.
In this study, an augmented state IEnKS is tested on its estimation of the
forcing parameter of the Lorenz-95 model. Since joint state and parameter
estimation is especially useful in applications where the forcings are
uncertain but nevertheless determining, typically in atmospheric chemistry,
the augmented state IEnKS is tested on a new low-order model that takes its
meteorological part from the Lorenz-95 model, and its chemical part from the
advection diffusion of a tracer. In these experiments, the IEnKS is compared
to the ensemble Kalman filter, the ensemble Kalman smoother, and a 4D-Var,
which are considered the methods of choice to solve these joint estimation
problems. In this low-order model context, the IEnKS is shown to
significantly outperform the other methods regardless of the length of the
data assimilation window, and for present time analysis as well as
retrospective analysis. Besides which, the performance of the IEnKS is even
more striking on parameter estimation; getting close to the same performance
with 4D-Var is likely to require both a long data assimilation window and a
complex modeling of the background statistics. |
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