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This study analyzes U.S. tornado time-series using dynamical systems, revealing power-law behaviors and long-range memory effects characteristic of complex systems. Findings offer insights into tornado dynamics and patterns.

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Area of Science:

  • Atmospheric science
  • Complex systems analysis
  • Dynamical systems theory

Background:

  • Tornadoes exhibit complex behaviors not fully explained by traditional meteorological models.
  • Understanding tornado time-series can provide insights into the underlying dynamics of severe weather events.
  • Previous research has hinted at fractal or power-law characteristics in natural phenomena.

Purpose of the Study:

  • To analyze U.S. tornado time-series data using dynamical systems principles.
  • To identify power-law functions and long-range memory effects in tornado behavior.
  • To explore collective tornado behavior and emerging patterns through advanced analytical techniques.

Main Methods:

  • Analysis of 64 years of U.S. tornado time-series data.
  • Application of Fourier transform for frequency domain analysis.
  • Modeling tornadoes as Dirac impulses with amplitude proportional to size.
  • Utilizing circular time concepts and clustering techniques for pattern identification.

Main Results:

  • Tornado time-series exhibit amplitude spectra well-approximated by power-law functions.
  • Identified parameters of power-law functions as signatures of system dynamics.
  • Circular time analysis revealed collective behaviors and emergent patterns in tornado occurrences.
  • Clustering techniques successfully visualized these identified patterns.

Conclusions:

  • Tornado behavior aligns with characteristics of complex systems, including power-law distributions and long-range memory.
  • Dynamical systems perspective offers a novel framework for understanding tornado phenomena.
  • Further research into these complex system dynamics could improve forecasting and mitigation strategies.