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Statistical analysis and forecasting of solar wind parameters across solar cycles.
He, Mu; Zhu, Hongbing.
Afiliación
  • He M; College of Artificial Intelligence, Suzhou Chien-Shiung Institute of Technology, Suzhou, 215411, Jiangsu, China.
  • Zhu H; Avant-Courier Laboratory, Hiroshima, 736-0067, Japan. chinmumu@gmail.com.
Sci Rep ; 14(1): 19529, 2024 Aug 22.
Article en En | MEDLINE | ID: mdl-39174644
ABSTRACT
This study investigated the statistical properties of solar wind parameters spanning Solar Cycles 20-24, elucidating periodicities that closely aligned with the solar cycle. Significantly, correlations between the smoothed 27-day average value of solar wind parameters and sunspot numbers (SSN) were discerned, shedding light on the intricate interplay between solar activity and solar wind characteristics. Furthermore, the study employed an optimized Long Short-Term Memory (LSTM+) model for forecasting Solar Cycle 25, demonstrating promising predictive capabilities. The analysis predicted the occurrence time of the peak value of SSN in Solar Cycle 25 to be on 27 October 2024 ± 136 days, based on the average relationship with the occurrence time of the trough of Plasma Beta. Notably, observations revealed a double peak in SC-25's solar activity, introducing uncertainty regarding the relative magnitude of each peak.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido