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Dynamical Analysis and Visualization of Tornadoes Time Series
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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.
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PLOS