Atmosphere (May 2024)

Complexity and Nonlinear Dependence of Ionospheric Electron Content and Doppler Frequency Shifts in Propagating HF Radio Signals within Equatorial Regions

  • Aderonke Akerele,
  • Babatunde Rabiu,
  • Samuel Ogunjo,
  • Daniel Okoh,
  • Anton Kascheyev,
  • Bruno Nava,
  • Olawale Bolaji,
  • Ibiyinka Fuwape,
  • Elijah Oyeyemi,
  • Busola Olugbon,
  • Jacob Akinpelu,
  • Olumide Ajani

DOI
https://doi.org/10.3390/atmos15060654
Journal volume & issue
Vol. 15, no. 6
p. 654

Abstract

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The abundance of ions within the ionosphere makes it an important region for both long range and satellite communication systems. However, characterizing the complexity in the ionosphere within the equatorial region of Abuja, with geographic coordinates of 8.99° N and 7.39° E and a geomagnetic latitude of −1.60, and Lagos, with geographic coordinates of 3.27° E and 6.48° N and a dip latitude of −1.72°, is a challenging and daunting task due to the intrinsic and external forces involved. In this study, chaos theory was applied on data from both an HF Doppler sounding system and the Global Navigation Satellite System (GNSS) for the characterization of the ionosphere over these two tropical locations during 2020–2021 with respect to the quality of high-frequency radio signals between the two locations. Our results suggest that the ionosphere at the two locations is chaotic, with its largest Lyapunov exponent values being greater than 0 (0.011≤λ≤0.041) and its correlation dimension being in the range of 1.388≤D2≤1.775. Furthermore, it was revealed that there exists a negative correlation between the state of the ionosphere and signal quality at the two locations. Using transfer entropy, it was confirmed that the ionosphere interfered more with signals during 2020, a year of lower solar activity (sunspot number, 8.8) compared to 2021 (sunspot number, 29.6). On a monthly scale, the influence of the ionosphere on signal quality was found to be complicated. The results obtained in this study will be useful in communication systems design, modelling, and prediction.

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