What is it about?

Traveling ionospheric disturbances (TIDs) are sudden wave-like propagation irregularities that perturbate the state of the ionospheric plasma. They are often observed during geomagnetically disturbed and quiet periods respectively and can infer a phenomenon where the ionospheric plasma density is reshaped and destabilized thereby pose an operational hazard on radio communication, navigation and imaging system. This paper applied Neural Network Entropy (NNetEn) to examine the complexity levels associated with the occurrence of TIDs during major geomagnetic storms. Eight GPS stations situated within the Eastern Africa sector were investigated. NNetEn which measures the degree of dynamical complexity was applied to the detrended TEC time series data to capture the dynamical characteristic associated with the occurrence of TIDs. The results of the NNetEn were able to track distinct features associated with TIDs occurrence such that reductions in the degree of dynamical complexity responses were associated with the emergence of TIDs while increments in the response of dynamical complexity was observed during the absence of TIDs. Reduction in dynamical complexity response associated with the occurrence of TIDs is more evident in the Southern Hemisphere compared to Northern Hemisphere. Furthermore, we found that the propagation of TIDs is more prominent at Equinoctial season compared to solstitial season.

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Why is it important?

The Africa equatorial ionosphere possesses high degree of irregularities due to its consistent driving of plasma instabilities. Therefore, the propagation of TIDs across Eastern Africa sector needs a special attention. Because the Eastern Africa sector is known to exhibit strong structures of Equatorial Ionization Anomaly (EIA). This inturn has a strong driving influence on the propagation of TIDs within this sector.


The need for additional practical method for investigating and tracing the ionospheric behavior during TIDs has led to the use of dynamical complexity method such as Neural Network Entropy (NNetEn) for examining the responses of the ionosphere to space weather phenomenon.

Dr. Andrei Velichko
Petrozavodsk State University

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This page is a summary of: Dynamical Complexity Response in Traveling Ionospheric Disturbances Across Eastern Africa Sector During Geomagnetic Storms Using Neural Network Entropy, Journal of Geophysical Research Space Physics, September 2022, American Geophysical Union (AGU),
DOI: 10.1029/2022ja030630.
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