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<article language="en">
	<journal>
		<journal_title>Geoscientific Model Development Discussions</journal_title>
		<journal_url>www.geosci-model-dev-discuss.net</journal_url>
		<issn>1991-9611</issn>
		<eissn>1991-962X</eissn>
		<volume_number>2</volume_number>
		<issue_number>2</issue_number>
		<publication_year>2009</publication_year>
	</journal>
	<doi>10.5194/gmdd-2-889-2009</doi>
	<article_url>http://www.geosci-model-dev-discuss.net/2/889/2009/</article_url>
	<abstract_html>http://www.geosci-model-dev-discuss.net/2/889/2009/gmdd-2-889-2009.html</abstract_html>
	<fulltext_pdf>http://www.geosci-model-dev-discuss.net/2/889/2009/gmdd-2-889-2009.pdf</fulltext_pdf>
	<start_page>889</start_page>
	<end_page>933</end_page>
	<publication_date>2009-07-09</publication_date>
	<article_title content_type="html">Automatic generation of large ensembles for air quality forecasting using the Polyphemus system</article_title>
	<authors>
		<author numeration="1" affiliations="1,2">
			<name>D. Garaud</name>
			<email>damien.garaud@cerea.enpc.fr</email>
		</author>
		<author numeration="2" affiliations="1,2">
			<name>V. Mallet</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">CEREA, joint research laboratory ENPC – EDF R&amp;D, Université Paris-Est, Marne-La-Vallée, France</affiliation>
		<affiliation numeration="2" content_type="html">INRIA, Rocquencourt, France</affiliation>
	</affiliations>
	<abstract content_type="html">This paper describes a method to automatically generate a large ensemble of air quality
      simulations. This is achieved using the Polyphemus system, which is flexible enough to build
      various different models. The system offers a wide range of options in the construction of
      a model: many physical parameterizations, several numerical schemes and different input data
      can be combined. In addition, input data can be perturbed. In this paper, some
      30 alternatives are available for the generation of a model. For each alternative, the
      options are given a probability, based on how reliable they are supposed to be. Each model
      of the ensemble is defined by randomly selecting one option per alternative. In order to
      decrease the computational load, as many computations as possible are shared by the models
      of the ensemble. As an example, an ensemble of 101 photochemical models is generated and run
      for the year 2001 over Europe. The models&apos; performance is quickly reviewed, and the ensemble
      structure is analyzed. We found a strong diversity in the results of the models and a wide
      spread of the ensemble. It is noteworthy that many models turn out to be the best model in
      some regions and some dates.</abstract>
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</article>

