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Complexity entropy-analysis of monthly rainfall time series in northeastern Brazil

Author

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  • Silva, Antonio Samuel Alves
  • Menezes, Rômulo Simões Cezar
  • Rosso, Osvaldo A.
  • Stosic, Borko
  • Stosic, Tatijana

Abstract

In this work we analyze predictability and complexity of monthly rainfall temporal series recorded from 1950 to 2012, at 133 gauging stations in Pernambuco state, northeastern Brazil. To this end we use the complexity entropy causality plane (CECP) and Fisher Shannon plane (FS) formed by information quantifiers permutation entropy, permutation statistical complexity, and Fisher information measure, which serve to evaluate randomness and structural organization of the underlying process. By comparing the locations of analyzed stations in CECP and FS and performing statistical significance test, we distinguish rainfall regime in the deep inland, drier Sertão region, from the intermediate inland Agreste, and coastal, tropical humid Zona da Mata regions. We also perform time dependent CECP and FS analysis and for Sertão region identify the periods of higher entropy (lower complexity and Fisher information measure) which are related to El Niño episodes and historical droughts in northeastern Brazil. Our work represents a contribution to establishing the use of information theory based methods in climatological studies.

Suggested Citation

  • Silva, Antonio Samuel Alves & Menezes, Rômulo Simões Cezar & Rosso, Osvaldo A. & Stosic, Borko & Stosic, Tatijana, 2021. "Complexity entropy-analysis of monthly rainfall time series in northeastern Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:chsofr:v:143:y:2021:i:c:s0960077920310146
    DOI: 10.1016/j.chaos.2020.110623
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    Cited by:

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    2. Boaretto, Bruno R.R. & Budzinski, Roberto C. & Rossi, Kalel L. & Masoller, Cristina & Macau, Elbert E.N., 2023. "Spatial permutation entropy distinguishes resting brain states," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).

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