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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-2-1375-2009</doi>
	<article_url>http://www.geosci-model-dev-discuss.net/2/1375/2009/</article_url>
	<abstract_html>http://www.geosci-model-dev-discuss.net/2/1375/2009/gmdd-2-1375-2009.html</abstract_html>
	<fulltext_pdf>http://www.geosci-model-dev-discuss.net/2/1375/2009/gmdd-2-1375-2009.pdf</fulltext_pdf>
	<start_page>1375</start_page>
	<end_page>1406</end_page>
	<publication_date>2009-12-08</publication_date>
	<article_title content_type="html">Assessment of bias-adjusted PM&lt;sub&gt;2.5&lt;/sub&gt; air quality forecasts over the continental United  States during 2007</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>D. Kang</name>
			<email>kang.daiwen@epa.gov</email>
		</author>
		<author numeration="2" affiliations="2">
			<name>R. Mathur</name>
		</author>
		<author numeration="3" affiliations="2">
			<name>S. Trivikrama Rao</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Computer Science Corporation, Research Triangle Park, 79 T.W. Alexander Drive, NC 27709, USA</affiliation>
		<affiliation numeration="2" content_type="html">Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory, US Environmental  Protection Agency, Research Triangle Park, NC, USA</affiliation>
	</affiliations>
	<abstract content_type="html">To develop fine particular matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) air quality forecasts, a National Air
      Quality Forecast Capability (NAQFC) system, which linked NOAA&apos;s North American Mesoscale
      (NAM) meteorological model with EPA&apos;s Community Multiscale Air Quality (CMAQ) model, was
      deployed in the developmental mode over the continental United States during 2007. This
      study investigates the operational use of a bias-adjustment technique called the Kalman
      Filter Predictor approach for improving the accuracy of the PM&lt;sub&gt;2.5&lt;/sub&gt; forecasts at
      monitoring locations. The Kalman Filter Predictor bias-adjustment technique is a recursive
      algorithm designed to optimally estimate bias-adjustment terms using the information
      extracted from previous measurements and forecasts.
&lt;br&gt;&lt;br&gt;
      The bias-adjustment technique is found to improve PM&lt;sub&gt;2.5&lt;/sub&gt; forecasts (i.e. reduced
      errors and increased correlation coefficients) for the entire year at almost all
      locations. The NAQFC tends to overestimate PM&lt;sub&gt;2.5&lt;/sub&gt; during the cool season and
      underestimate during the warm season in the eastern part of the continental US domain, but
      the opposite is true for the pacific coast. In the Rocky Mountain region, the NAQFC system
      overestimates PM&lt;sub&gt;2.5&lt;/sub&gt; for the whole year. The bias-adjustment forecasts can quickly
      (after 2–3 days&apos; lag) adjust to reflect the transition from one regime to the other. The
      modest computational requirements and systematical improvements in forecast results across
      all seasons suggest that this technique can be easily adapted to perform bias-adjustment for
      real-time PM&lt;sub&gt;2.5&lt;/sub&gt; air quality forecasts.</abstract>
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</article>

