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<article language="en">
	<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>12</volume_number>
		<issue_number>4</issue_number>
		<publication_year>2005</publication_year>
	</journal>
	<doi>10.5194/npg-12-515-2005</doi>
	<article_url>http://www.nonlin-processes-geophys.net/12/515/2005/</article_url>
	<abstract_html>http://www.nonlin-processes-geophys.net/12/515/2005/npg-12-515-2005.html</abstract_html>
	<fulltext_pdf>http://www.nonlin-processes-geophys.net/12/515/2005/npg-12-515-2005.pdf</fulltext_pdf>
	<start_page>515</start_page>
	<end_page>525</end_page>
	<publication_date>2005-05-19</publication_date>
	<article_title content_type="html">Nonlinear data-assimilation using implicit models</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>A. D. Terwisscha van Scheltinga</name>
		</author>
		<author numeration="2" affiliations="2">
			<name>H. A. Dijkstra</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Institute for Marine and Atmospheric research Utrecht, Department of Physics and Astronomy, Utrecht University, The Netherlands</affiliation>
		<affiliation numeration="2" content_type="html">Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA</affiliation>
	</affiliations>
	<abstract content_type="html">We show how the traditional 4D-Var method can be adapted for implicit
time-integration and extended for multi-parameter estimation. We present the
algorithm for this new method, which we call I4D-Var, and demonstrate its
performance using a fully-implicit barotropic quasi-geostrophic model of the
wind-driven double-gyre ocean circulation. For the latter model, the
different regimes of flow behavior and the regime boundaries (i.e.
bifurcation points) are well known and hence the parameter estimation problem
can be systematically studied. It turns out that I4D-Var is able to correctly
estimate parameter values, even when background flow and &quot;observations&quot; are
in different dynamical regimes.</abstract>
	<references>
	</references>
</article>

