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Dynamical analysis and visualization of tornadoes time series.

Lopes AM, Tenreiro Machado JA - PLoS ONE (2015)

Bottom Line: Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects.The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics.Clustering techniques are then adopted to identify and visualize the emerging patterns.

View Article: PubMed Central - PubMed

Affiliation: Institute of Mechanical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal.

ABSTRACT
In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

No MeSH data available.


Evolution of the number of reported tornadoes that occurred in the U.S. versus year, during the time period 1950–2013.
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pone.0120260.g002: Evolution of the number of reported tornadoes that occurred in the U.S. versus year, during the time period 1950–2013.

Mentions: Fig. 2 represents the evolution of the number of occurrences versus year, where we can see an increasing trend. However, the statement that we should expect an increasing number of events in the next years is not justified by this simple statistic, as we cannot ignore the fact that there has been a change in the detection capabilities and reporting of the events over time. In fact, in the last decades there has been a more systematic reporting of weak tornadoes over time [21, 23].


Dynamical analysis and visualization of tornadoes time series.

Lopes AM, Tenreiro Machado JA - PLoS ONE (2015)

Evolution of the number of reported tornadoes that occurred in the U.S. versus year, during the time period 1950–2013.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4366018&req=5

pone.0120260.g002: Evolution of the number of reported tornadoes that occurred in the U.S. versus year, during the time period 1950–2013.
Mentions: Fig. 2 represents the evolution of the number of occurrences versus year, where we can see an increasing trend. However, the statement that we should expect an increasing number of events in the next years is not justified by this simple statistic, as we cannot ignore the fact that there has been a change in the detection capabilities and reporting of the events over time. In fact, in the last decades there has been a more systematic reporting of weak tornadoes over time [21, 23].

Bottom Line: Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects.The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics.Clustering techniques are then adopted to identify and visualize the emerging patterns.

View Article: PubMed Central - PubMed

Affiliation: Institute of Mechanical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal.

ABSTRACT
In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

No MeSH data available.