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	<journal>
		<journal_title>Nonlinear Processes  in Geophysics</journal_title>
		<journal_url>www.nonlin-processes-geophys.net</journal_url>
		<issn>1023-5809</issn>
		<eissn>1607-7946</eissn>
		<volume_number>13</volume_number>
		<issue_number>1</issue_number>
		<publication_year>2006</publication_year>
	</journal>
	<doi>10.5194/npg-13-67-2006</doi>
	<article_url>http://www.nonlin-processes-geophys.net/13/67/2006/</article_url>
	<abstract_html>http://www.nonlin-processes-geophys.net/13/67/2006/npg-13-67-2006.html</abstract_html>
	<fulltext_pdf>http://www.nonlin-processes-geophys.net/13/67/2006/npg-13-67-2006.pdf</fulltext_pdf>
	<start_page>67</start_page>
	<end_page>81</end_page>
	<publication_date>2006-03-24</publication_date>
	<article_title content_type="html">Detecting unstable structures and controlling error growth by assimilation of standard and adaptive observations in a primitive equation ocean model</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>F. Uboldi</name>
			<email>uboldi@magritte.it</email>
		</author>
		<author numeration="2" affiliations="2">
			<name>A. Trevisan</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">no current affiliation</affiliation>
		<affiliation numeration="2" content_type="html">CNR-ISAC, Bologna, Italy</affiliation>
	</affiliations>
	<abstract content_type="html">Oceanic and atmospheric prediction is based on cyclic analysis-forecast
systems that assimilate new observations as they become available.
In such observationally forced systems, errors amplify depending on
their components along the unstable directions; these can be estimated
by Breeding on the Data Assimilation System (BDAS). Assimilation in
the Unstable Subspace (AUS) uses the available observations to estimate
the amplitude of the unstable structures (computed by BDAS), present
in the forecast error field, in order to eliminate them and to control
the error growth. For this purpose, it is crucial that the observational
network can detect the unstable structures that are active in the
system. These concepts are demonstrated here by twin experiments with
a large state dimension, primitive equation ocean model and an observational
network having a fixed and an adaptive component. The latter consists
of observations taken each time at different locations, chosen to
target the estimated instabilities, whose positions and features depend
on the dynamical characteristics of the flow. The adaptive placement and
the dynamically consistent assimilation of observations (both relying
upon the estimate of the unstable directions of the data-forced system),
allow to obtain a remarkable reduction of errors with respect to a
non-adaptive setting. The space distribution of the positions chosen
for the observations allows to characterize the evolution of instabilities,
from deep layers in western boundary current regions, to near-surface
layers in the eastward jet area.</abstract>
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</article>

