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<article language="en">
	<journal>
		<journal_title>Atmospheric Chemistry and Physics Discussions</journal_title>
		<journal_url>www.atmos-chem-phys-discuss.net</journal_url>
		<issn>1680-7367</issn>
		<eissn>1680-7375</eissn>
		<volume_number>7</volume_number>
		<issue_number>6</issue_number>
		<publication_year>2007</publication_year>
	</journal>
	<doi>10.5194/acpd-7-17511-2007</doi>
	<article_url>http://www.atmos-chem-phys-discuss.net/7/17511/2007/</article_url>
	<abstract_html>http://www.atmos-chem-phys-discuss.net/7/17511/2007/acpd-7-17511-2007.html</abstract_html>
	<fulltext_pdf>http://www.atmos-chem-phys-discuss.net/7/17511/2007/acpd-7-17511-2007.pdf</fulltext_pdf>
	<start_page>17511</start_page>
	<end_page>17536</end_page>
	<publication_date>2007-12-03</publication_date>
	<article_title content_type="html">A data assimilation method of the Ensemble Kalman Filter for use in severe dust storm forecasts over China</article_title>
	<authors>
		<author numeration="1" affiliations="1,2">
			<name>C. Lin</name>
		</author>
		<author numeration="2" affiliations="1">
			<name>Z. Wang</name>
			<email>zifawang@mail.iap.ac.cn</email>
		</author>
		<author numeration="3" affiliations="1">
			<name>J. Zhu</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">LAPC/NZC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China</affiliation>
		<affiliation numeration="2" content_type="html">Graduate University of Chinese Academy of Sciences, Beijing, China</affiliation>
	</affiliations>
	<abstract content_type="html">An Ensemble Kalman Filter (EnKF) data assimilation system was developed for
a regional dust transport model. This paper applied the EnKF method to
investigate modeling severe dust storm episodes occurred in March 2002 over
China based on surface observations of dust concentrations to explore its
impacts on forecast improvement. A series of sensitivity experiments using
our system reveals that the EnKF is an advanced assimilation method to
afford better initial conditions with surface observed PM&lt;sub&gt;10&lt;/sub&gt; in North
China and lead to improved forecasts of dust storms, but forecast with large
errors can be made by model errors. This result illustrates that it requires
identifying and correcting model errors during the assimilation procedure in
order to significantly improve forecasts. Results also show that the EnKF
should use a large inflation parameter to obtain better model performance
and forecast potential. Furthermore, the ensemble perturbations generated at
the initial time should include enough ensemble spreads to represent the
background error after several assimilation cycles.</abstract>
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</article>

