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| Titel |
Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data |
| VerfasserIn |
C. Skandrani, J.-M. Brankart, N. Ferry, J. Verron, P. Brasseur, B. Barnier |
| 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 ; 5, no. 4 ; Nr. 5, no. 4 (2009-10-16), S.403-419 |
| Datensatznummer |
250002720
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| Publikation (Nr.) |
 copernicus.org/os-5-403-2009.pdf |
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| Zusammenfassung |
| In the context of stand alone ocean models,
the atmospheric forcing is generally computed
using atmospheric parameters that are derived
from atmospheric reanalysis data and/or satellite products.
With such a forcing, the sea surface temperature
that is simulated by the ocean model is usually
significantly less accurate than the synoptic maps
that can be obtained from the satellite observations.
This not only penalizes the realism of the ocean
long-term simulations, but also the accuracy of the reanalyses
or the usefulness of the short-term operational forecasts
(which are key GODAE and MERSEA objectives).
In order to improve the situation, partly resulting
from inaccuracies in the atmospheric forcing parameters,
the purpose of this paper is to investigate a way
of further adjusting the state of the atmosphere
(within appropriate error bars), so that an explicit
ocean model can produce a sea surface temperature
that better fits the available observations.
This is done by performing idealized assimilation
experiments in which Mercator-Ocean reanalysis data
are considered as a reference simulation
describing the true state of the ocean.
Synthetic observation datasets for sea surface temperature
and salinity are extracted from the reanalysis
to be assimilated in a low resolution global ocean model.
The results of these experiments show that it is possible
to compute piecewise constant parameter corrections,
with predefined amplitude limitations, so that long-term
free model simulations
become much closer to the reanalysis data,
with misfit variance typically divided by a factor 3.
These results are obtained by applying a Monte Carlo
method to simulate the joint parameter/state
prior probability distribution.
A truncated Gaussian assumption is used
to avoid the most extreme and non-physical parameter corrections.
The general lesson of our experiments is indeed
that a careful specification of the prior information
on the parameters and on their associated uncertainties
is a key element in the computation of realistic
parameter estimates, especially if the system is affected
by other potential sources of model errors. |
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