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Area of Science:

  • Meteorology
  • Complex Systems Science
  • Network Science

Background:

  • Tropical cyclones are hazardous extreme weather events.
  • The interaction and merger of binary cyclones pose forecasting challenges.
  • Understanding dynamic transitions during cyclone mergers is crucial.

Purpose of the Study:

  • To develop an innovative method for analyzing vortical interactions between binary cyclones undergoing complete merger (CM).
  • To utilize time-evolving induced velocity-based unweighted directed networks to study CM events.
  • To identify network-based indicators for classifying interaction stages and predicting CM.

Main Methods:

  • Constructing unweighted directed networks based on induced velocities between cyclones.
  • Analyzing time-evolving network properties, specifically in-degree and out-degree.
  • Applying network measures to case studies of cyclone mergers (Noru-Kulap and Seroja-Odette).

Main Results:

  • Network indicators (in-degree, out-degree) effectively quantify cyclone interactions and classify interaction stages.
  • These indicators help identify the dominant cyclone during interaction and track merged cyclone strength.
  • Network measures provide early warnings for complete merger events.

Conclusions:

  • Network-based analysis offers a novel approach to understanding binary cyclone interactions and mergers.
  • In-degree and out-degree are valuable metrics for predicting complete mergers and assessing cyclone dynamics.
  • This method enhances the predictability of hazardous cyclone merger events.