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	<journal>
		<journal_title>Advances in Geosciences</journal_title>
		<journal_url>www.adv-geosci.net</journal_url>
		<issn>1680-7340</issn>
		<eissn>1680-7359</eissn>
		<volume_number>10</volume_number>
		<volume_title>Observation, Prediction and Verification of Precipitation (EGU Session 2006)</volume_title>
		<publication_year>2007</publication_year>
	</journal>
	<doi>10.5194/adgeo-10-99-2007</doi>
	<article_url>http://www.adv-geosci.net/10/99/2007/</article_url>
	<abstract_html>http://www.adv-geosci.net/10/99/2007/adgeo-10-99-2007.html</abstract_html>
	<fulltext_pdf>http://www.adv-geosci.net/10/99/2007/adgeo-10-99-2007.pdf</fulltext_pdf>
	<start_page>99</start_page>
	<end_page>102</end_page>
	<publication_date>2007-04-26</publication_date>
	<article_title content_type="html">Assignment of rainfall confidence values using multispectral satellite data at mid-latitudes: first results</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>T. Nauss</name>
			<email>nauss@lcrs.de</email>
		</author>
		<author numeration="2" affiliations="2">
			<name>A. A. Kokhanovsky</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Laboratory of Climatology and Remote Sensing, University of Marburg, Germany</affiliation>
		<affiliation numeration="2" content_type="html">Institute of Remote Sensing, University of Bremen, Germany</affiliation>
	</affiliations>
	<abstract content_type="html">The authors propose a new method for the assignment of rainfall confidences
on a pixel basis using cloud properties derived from optical satellite data
during daytime. This approach is based on the concept model that the
probability for precipitation is a function of the liquid water path, which
in turn can be computed using the satellite-retrieved cloud optical thickness
and the cloud effective droplet radius. In order to evaluate the principal
potential of this idea, scenes from the Terra-MODIS sensor during the severe
European summer floods in 2002 have been analysed in order to derive a
corresponding regression function that interlinks the liquid water path with
the rainfall probability or better with the confidence that a pixel which is
classified as raining does actually rain. A first evaluation against
ground-based radar data during March 2004 shows good skill of this new
method.</abstract>
	<references>
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</article>

