Fluctuation analysis of meteo-marine data

Roman, H. E.; Celi, Alessio; Filippi, Giulia

doi:10.1140/epjst/e2008-00761-4

Cited by 2 publications

(9 citation statements)

References 10 publications

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“…Several studies have reported long-range correlations of wind speed [Schulz et al, 2001;Kocak, 2008;Roman et al, 2008]. We find a similar scaling exponent for the wind speed of the Gulf Eilat, b = 0.86.…”

Section: C09009supporting

confidence: 76%

Long‐range temporal correlations of ocean surface currents

Ashkenazy¹,

Gildor²

2009

J. Geophys. Res.

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[1] We study the temporal correlations of sea surface currents at the Gulf of Eilat (also known as Gulf of Aqaba) and find long-range temporal correlations, from a timescale of several hours to a timescale of several months. This is done using the Fourier transform and the Detrended Fluctuation Analysis methods. We also find weak volatility correlations that indicate nonlinearity of surface currents. We use the time-dependent surface Ekman layer model to test whether the source of these correlations is the wind. It is found that the wind by itself actually leads to stronger temporal correlations than observed, as well as enhanced diurnal periodicity; other nonlinear terms as well as tides, convection, and spatial variability may weaken the temporal correlations imposed by the wind. Our results show significant spatial variability of correlation exponents even in this small region (6 Â 10 km); in addition, stronger correlations are observed during winter.

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Section: C09009supporting

confidence: 76%

Long‐range temporal correlations of ocean surface currents

Ashkenazy¹,

Gildor²

2009

J. Geophys. Res.

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“…Persistence is defined as the tendency that subsequent values in a time series are similar: large values tend to be followed by large values and small values tend to be followed by small values. Such behavior has been reported for water levels in rivers [Hurst, 1951] and in river runoff records [Kantelhardt et al, 2003, in climatological temperature recordings [Koscielny- Bunde et al, 1998;Pelletier and Turcotte, 1997;Huybers and Curry, 2006] and for temperature fluctuations in oceans [Monetti et al, 2002], as well as in marine data [Roman et al, 2008], to name only a few examples.…”

Section: Introductionmentioning

confidence: 69%

“…In this paper, we exclusively focus on symmetric α-stable distributed processes with long-range memory, for which the block maxima asymptotically converge in distribution to the Fréchet extreme value distribution in Eq. (3b) [Samorodnitsky, 2004].…”

Section: Classical Extreme Value Statisticsmentioning

confidence: 99%

“…This has led to a powerful statistical theory (see, e.g., [Embrechts et al, 2004;Coles, 2007;de Haan and Ferreira, 2006]), which has been successfully applied in many cases (see, e.g., [Easterling et al, 2000;Glaser and Stangl , 2004;van den Brink and Können, 2008]). The latter is also related to the fact that some parts of the theory -for example, the limit distributions of block maxima -can be extended to a wide class of dependent stationary stochastic processes and their associated time series [Berman, 1964;Leadbetter et al, 1983;Leadbetter and Rootzén, 1988;Samorodnitsky, 2004]. However, we are still far away from a general understanding of extreme events generated by such dependent processes, which are abundant in nature.…”

Section: Introductionmentioning

confidence: 99%

“…These are particularly relevant in the context of the solar wind as discussed, for example, by Moloney and Davidsen [2010] for the energy influx into the magnetosphere, which is captured by the Akasofu ǫ parameter. While it is known that the limit distribution of the block maxima remains a Fréchet distribution for all α and H [Samorodnitsky, 2004], we systematically quantify the finite size corrections. As expected, they are particularly pronounced for strong persistence but they also play a role in the presence of antipersistence.…”

Section: Introductionmentioning

confidence: 99%

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Extreme Value and Record Statistics in Heavy-Tailed Processes With Long-Range Memory

Schumann

Moloney

Davidsen

2012

Extreme Events and Natural Hazards: The Complexity Perspective

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Abstract. Extreme events are an important theme in various areas of science because of their typically devastating effects on society and their scientific complexities. The latter is particularly true if the underlying dynamics does not lead to independent extreme events as often observed in natural systems. Here, we focus on this case and consider stationary stochastic processes that are characterized by long-range memory and heavytailed distributions, often called fractional Lévy noise. While the size distribution of extreme events is not affected by the long-range memory in the asymptotic limit and remains a Fréchet distribution, there are strong finite-size effects if the memory leads to persistence in the underlying dynamics. Moreover, we show that this persistence is also present in the extreme events, which allows one to make a time-dependent hazard assessment of future extreme events based on events observed in the past. This has direct applications in the field of space weather as we discuss specifically for the case of the solar power influx into the magnetosphere. Finally, we show how the statistics of records, or record-breaking extreme events, is affected by the presence of long-range memory.

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Fluctuation analysis of meteo-marine data

Cited by 2 publications

References 10 publications

Long‐range temporal correlations of ocean surface currents

Long‐range temporal correlations of ocean surface currents

Extreme Value and Record Statistics in Heavy-Tailed Processes With Long-Range Memory

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