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A dynamically based data assimilation for targeted observations

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**
Alberto Carrassi
**

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Department of Physics*

University of Ferrara, Italy

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Abstract:
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A recently developed data assimilation method is discussed and applied
to a quasi-geostrophic atmospheric model. The method exploits the
flow-dependent unstable structures of the observationally forced
analysis cycle solution to estimate and reduce the background error.
The unstable subspace is estimated by breeding on the data assimilation
cycle, so that the analysis update has the same structure as the forced
bred modes. Use of adaptive observations, made at locations where bred
vectors have maximum amplitude, enhances the efficiency of the procedure
and allows the use of a very limited number of observations and modes.

The performance of the assimilation scheme is tested in an idealized
context, under perfect model conditions. The observational network
consists of fixed observations completely covering one third of the
domain plus a single adaptive observation in the otherwise data void
region. Statistics accumulated over two years analysis cycle
experiments show that a major reduction of the RMS analysis error is
obtained with this procedure compared to 3D-Var.

The reduction of the error, whose amplitude is large at the beginning of
the experiment, is accompained by a corresponding stabilization of the
analysis cycle solution. The initially positive exponent, corresponding
to a typical doubling time of two days, becomes negative, pointing to an
obvious reduction of the dimension of the unstable subspace. This
finding confirms previous conclusions about the stabilizing effect of
the observational forcing and the observability of the data assimilation
system.