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| Titel |
Towards an improved description of ocean uncertainties: effect of local anamorphic transformations on spatial correlations |
| VerfasserIn |
J.-M. Brankart, C.-E. Testut, D. Béal, M. Doron, C. Fontana, M. Meinvielle, P. Brasseur, J. Verron |
| 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 ; 8, no. 2 ; Nr. 8, no. 2 (2012-03-06), S.121-142 |
| Datensatznummer |
250005504
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| Publikation (Nr.) |
 copernicus.org/os-8-121-2012.pdf |
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| Zusammenfassung |
| The objective of this paper is to investigate
if the description of ocean uncertainties
can be significantly improved by applying
a local anamorphic transformation to each model variable,
and by making the assumption of joint Gaussianity
for the transformed variables, rather than for the original variables.
For that purpose, it is first argued that a significant improvement
can already be obtained by deriving the local transformations
from a simple histogram description of the marginal distributions.
Two distinctive advantages of this solution for large size applications
are the conciseness and the numerical efficiency of the description.
Second, various oceanographic examples are used to evaluate the effect
of the resulting piecewise linear local anamorphic transformations
on the spatial correlation structure.
These examples include (i) stochastic ensemble descriptions
of the effect of atmospheric uncertainties on the ocean mixed layer, and
of wind uncertainties or parameter uncertainties on the ecosystem, and
(ii) non-stochastic ensemble descriptions of forecast
uncertainties in current sea ice and ecosystem pre-operational developments.
The results indicate that (i) the transformation is accurate enough
to faithfully preserve the correlation structure if the joint distribution
is already close to Gaussian, and
(ii) the transformation has the general tendency of increasing
the correlation radius as soon as the spatial dependence
between random variables becomes nonlinear,
with the important consequence of reducing the number of degrees
of freedom in the uncertainties, and thus increasing the benefit
that can be expected from a given observation network. |
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