2004
DOI: 10.1016/j.jhydrol.2004.03.024
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On the scaling behavior of rain event sequence recorded in Basilicata region (Southern Italy)

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Cited by 20 publications
(16 citation statements)
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“…Furthermore, the conditional recurrence intervals also verified the clustering phenomenon of recurrence intervals since both P(τ i+1 >τ |τ i >τ ) and P(τ i+1 <τ |τ i <τ ) were 1 much larger than those of random cases [17,30]. 2 This paper is devoted to the statistical analysis of recurrence intervals of rare events, including the distribution and 3 correlation structure, for long-range persistent records, long-range anti-persistent records and short-range autocorrelated 4 records. We find that the long-range anti-persistent records generated by ARFIMA processes do not yield stretched 5 exponential distribution.…”
mentioning
confidence: 78%
“…Furthermore, the conditional recurrence intervals also verified the clustering phenomenon of recurrence intervals since both P(τ i+1 >τ |τ i >τ ) and P(τ i+1 <τ |τ i <τ ) were 1 much larger than those of random cases [17,30]. 2 This paper is devoted to the statistical analysis of recurrence intervals of rare events, including the distribution and 3 correlation structure, for long-range persistent records, long-range anti-persistent records and short-range autocorrelated 4 records. We find that the long-range anti-persistent records generated by ARFIMA processes do not yield stretched 5 exponential distribution.…”
mentioning
confidence: 78%
“…To detect the presence of clustering of events in a time series, several methods can be used among which is the Fano factor calculation, which estimates the value of the fractal exponent  of the study process. According to such authors as Telesca et al (2004), assuming a sequence of events is the result of a point process defined by the set of occurrence times. You can use a statistical measure such as the Fano factor () FF  to characterize the process.…”
Section: Fractal Analysis (Fano Factor)mentioning
confidence: 99%
“…In order to detect the presence of time clustering in the time-occurrence sequence of rain events, many analyses can be used. Following Telesca et al (2004), the Fano, FF(t), and Allan, AF(t), factors are useful for characterizing the precipitation if it is assumed to be a realization of a point process, defined by the set of event occurrence times. In order to calculate them, the box counting method (e.g.…”
Section: Time-clustering Analysis Of Rain Eventsmentioning
confidence: 99%
“…Hourly rainfall data series of different lengths were available in all the weather stations (from 1980to 2004in Huelva, Malaga and Almeria, and from 1982to 2003. They were recorded by identical Helmann rain gauges (world Meteorological Organization Standard) with a horizontal opening of 200 cm 2 at a height of 1Ð2 m. The resolution of the rain gauge was 0Ð1 mm meaning that any amount of rain lesser than this value was not recorded.…”
Section: Rainfall Datamentioning
confidence: 99%