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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>26</volume_number>
		<volume_title>11th EGU Plinius Conference on Mediterranean Storms (2009)</volume_title>
		<publication_year>2010</publication_year>
	</journal>
	<doi>10.5194/adgeo-26-39-2010</doi>
	<article_url>http://www.adv-geosci.net/26/39/2010/</article_url>
	<abstract_html>http://www.adv-geosci.net/26/39/2010/adgeo-26-39-2010.html</abstract_html>
	<fulltext_pdf>http://www.adv-geosci.net/26/39/2010/adgeo-26-39-2010.pdf</fulltext_pdf>
	<start_page>39</start_page>
	<end_page>44</end_page>
	<publication_date>2010-07-02</publication_date>
	<article_title content_type="html">Precipitation downscaling using random cascades: a case study in Italy</article_title>
	<authors>
		<author numeration="1" affiliations="1">
			<name>B. Groppelli</name>
			<email>bibiana.groppelli@polimi.it</email>
		</author>
		<author numeration="2" affiliations="1">
			<name>D. Bocchiola</name>
		</author>
		<author numeration="3" affiliations="1">
			<name>R. Rosso</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Politecnico di Milano, Milano, Italy</affiliation>
	</affiliations>
	<abstract content_type="html">We present a Stochastic Space Random Cascade (SSRC) approach to downscale
precipitation from a Global Climate Model (hereon, &lt;i&gt;GCM&lt;/i&gt;s) for an Italian Alpine
watershed, the Oglio river (1440 km&lt;sup&gt;2&lt;/sup&gt;). The SSRC model is locally tuned
upon Oglio river for spatial downscaling (approx. 2 km) of daily
precipitation from the NCAR Parallel Climate Model. We use a 10 years
(1990–1999) series of observed daily precipitation data from 25 rain gages.
Scale Recursive Estimation coupled with Expectation Maximization algorithm
is used for model estimation. Seasonal parameters of the multiplicative
cascade are accommodated by statistical distributions conditioned upon
climatic forcing, based on regression analysis. The main advantage of the
SSRC is to reproduce spatial clustering, intermittency, self-similarity of
precipitation fields and their spatial correlation structure, with low
computational burden.</abstract>
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</article>

