The Influences of Solar Activities on TEC Variations of Equatorial Ionosphere over Sri Lanka

Jenan, Rajavarathan; Dammalage, Thilantha Lakmal; Kealy, Allison

doi:10.31401/sungeo.2019.02.05

Cited by 2 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Solar activity can significantly affect the electron density of the ionosphere, resulting in delayed signal propagation through the ionosphere and ultimately degrading GNSS performance. There is a high correlation between solar activity and ionospheric TEC (Jenan et al., 2019; Mansoori et al., 2016). In this paper, we make the SSN as the measure of solar activity.…”

Section: Resultsmentioning

confidence: 99%

Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion

Chen,

Wang,

et al. 2024

Space Weather

View full text Add to dashboard Cite

Geomagnetic storms induce ionospheric disturbances, affecting short‐wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short‐wave radio environment of the ionosphere. We use the Multi‐Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi‐LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI‐2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and R2 for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI‐2016 models.

show abstract

Section: Resultsmentioning

confidence: 99%