An entropic-stochastic representation of rainfall intermittency: The origin of clustering and persistence

Koutsoyiannis, D

dc.contributor.author	Koutsoyiannis, D	en
dc.date.accessioned	2014-03-01T01:23:34Z
dc.date.available	2014-03-01T01:23:34Z
dc.date.issued	2006	en
dc.identifier.issn	0043-1397	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17025
dc.subject	Stochastic Representation	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Limnology	en
dc.subject.classification	Water Resources	en
dc.subject.other	Entropy	en
dc.subject.other	Mathematical models	en
dc.subject.other	Optimization	en
dc.subject.other	Probability	en
dc.subject.other	Clustering	en
dc.subject.other	Empirical data	en
dc.subject.other	Persistence	en
dc.subject.other	Principle of maximum entropy	en
dc.subject.other	Rain	en
dc.subject.other	Entropy	en
dc.subject.other	Mathematical models	en
dc.subject.other	Optimization	en
dc.subject.other	Probability	en
dc.subject.other	Rain	en
dc.subject.other	entropy	en
dc.subject.other	precipitation assessment	en
dc.subject.other	rainfall	en
dc.title	An entropic-stochastic representation of rainfall intermittency: The origin of clustering and persistence	en
heal.type	journalArticle	en
heal.identifier.primary	10.1029/2005WR004175	en
heal.identifier.secondary	http://dx.doi.org/10.1029/2005WR004175	en
heal.identifier.secondary	W01401	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	The well-established physical and mathematical principle of maximum entropy, interpreted as maximum uncertainty, is used to explain the observed dependence properties of the rainfall occurrence process, including the clustering behavior and persistence. The conditions used for the maximization of entropy are as simple as possible, i.e., that the rainfall processes is intermittent with dependent occurrences. Intermittency is quantified by the probability that a time interval is dry, and dependence is quantified by the probability that two consecutive intervals are dry. These two probabilities are used as constraints in a multiple-scale entropy maximization framework, which determines any conditional or unconditional probability of any sequence of dry and wet intervals at any timescale. Thus the rainfall occurrence process including its dependence structure is described by only two parameters. This dependence structure appears to be non-Markovian. Application of this theoretical framework to the rainfall data set of Athens indicates good agreement of theoretical predictions and empirical data at the entire range of scales for which probabilities dry and wet can be estimated (from one hour to several months). Copyright 2006 by the American Geophysical Union.	en
heal.publisher	AMER GEOPHYSICAL UNION	en
heal.journalName	Water Resources Research	en
dc.identifier.doi	10.1029/2005WR004175	en
dc.identifier.isi	ISI:000234997000001	en
dc.identifier.volume	42	en
dc.identifier.issue	1	en