Environmental superstatistics

Yalçın, G. Çiğdem; Beck, Christian

doi:10.1016/j.physa.2013.06.057

Cited by 16 publications

(13 citation statements)

References 32 publications

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“…Rapisarda et al [19] and Kantz et al [23] investigated superstatistical aspects of wind velocity fluctuations. Yalcin et al [24] did a data analysis of relevant surface temperature distributions on the earth, which is important if one wants to understand the effective statistical mechanics for thermodynamic devices (or local ecosystems) that are kept in the open air outside a constanttemperature environment. Porporato et al [25] looked at rainfall statistics and developed a model where the rate parameter of the underlying Poisson process fluctuates in a superstatistical way.…”

Section: Introductionmentioning

confidence: 99%

Superstatistical analysis of sea-level fluctuations

Rabassa

Beck

2015

Physica A: Statistical Mechanics and its Applications

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We perform a statistical analysis of measured time series of sea levels at various coastal locations in the UK, measured at time differences of 15 minutes over the past 20 years. When the astronomical tide and other deterministic components are removed from the record, a stochastic signal corresponding to the meteorological component remains, and this is well-described by a superstatistical model. We do various tests on the measured time series, and compare the data at 5 different UK locations. Overall the χ 2 -superstatistics is best suitable to describe the data, in particular when one looks at the dynamics of sea-level differences on short time scales.

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Section: Introductionmentioning

confidence: 99%

Superstatistical analysis of sea-level fluctuations

Rabassa

Beck

2015

Physica A: Statistical Mechanics and its Applications

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“…As high-level models for describing how the mean temperature changes on long time-scales, we implement three different climate scenarios: constant, accelerating or plateauing global warming. Although the distribution of temperature fluctuations has been described in the superstatistical context before 7 , this example focuses on identifying the onset of global warming and its functional form, and further provides objective probability weights for all the three scenarios.…”

Section: Resultsmentioning

confidence: 99%

“…Superstatistics has been successfully applied to a variety of financial 4 – 6 , environmental 7 – 9 , social 10 , 11 , and biological systems 12 . Moreover, current superstatistical methods can determine not only how frequently certain parameter values are realized but can also pin-point when parameter values change 13 – 16 .…”

Section: Introductionmentioning

confidence: 99%

Bayesian model selection for complex dynamic systems

et al. 2018

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Time series generated by complex systems like financial markets and the earth’s atmosphere often represent superstatistical random walks: on short time scales, the data follow a simple low-level model, but the model parameters are not constant and can fluctuate on longer time scales according to a high-level model. While the low-level model is often dictated by the type of the data, the high-level model, which describes how the parameters change, is unknown in most cases. Here we present a computationally efficient method to infer the time course of the parameter variations from time-series with short-range correlations. Importantly, this method evaluates the model evidence to objectively select between competing high-level models. We apply this method to detect anomalous price movements in financial markets, characterize cancer cell invasiveness, identify historical policies relevant for working safety in coal mines, and compare different climate change scenarios to forecast global warming.

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“…There are numerous applications of superstatistical theory on data analyses, such as sea-level fluctuations [14], high energy physics [15], modeling train delays [16], environmental atmospheric turbulence [17], temperature fluctuations [18], metastasis and cancer survival [19], currents in complex polymers [20], and headway distributions [21].…”

Section: Introductionmentioning

confidence: 99%

A superstatistical model of vehicular traffic flow

Koşun

Ozdemir

2016

Physica A: Statistical Mechanics and its Applications

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In the analysis of vehicular traffic flow, a myriad of techniques have been implemented. In this study, superstatistics is used in modeling the traffic flow on a highway segment. Traffic variables such as vehicular speeds, volume, and headway were collected for three days. For the superstatistical approach, at least two distinct time scales must exist, so that a superposition of nonequilibrium systems assumption could hold. When the slow dynamics of the vehicle speeds exhibit a Gaussian distribution in between the fluctuations of the system at large, one speaks of a relaxation to a local equilibrium. These Gaussian distributions are found with corresponding standard deviations 1/β. This translates into a series of fluctuating beta values, hence the statistics of statistics, superstatistics. The traffic flow model has generated an inverse temperature parameter (beta) distribution as well as the speed distribution. This beta distribution has shown that the fluctuations in beta are distributed with respect to a chi-square distribution. It must be mentioned that two distinct Tsallis q values are specified: one is time-dependent and the other is independent. A ramification of these q values is that the highway segment and the traffic flow generate separate characteristics. This highway segment in question is not only nonadditive in nature, but a nonequilibrium driven system, with frequent relaxations to a Gaussian

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Environmental superstatistics

Cited by 16 publications

References 32 publications

Superstatistical analysis of sea-level fluctuations

Superstatistical analysis of sea-level fluctuations

Bayesian model selection for complex dynamic systems

A superstatistical model of vehicular traffic flow

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