Geomagnetic storms significantly disturb the ionosphere,impacting satellitebased systems such as the Global Navigation Satellite System(GNSS),communication links,and power infrastructure.This study models the ionosphe...Geomagnetic storms significantly disturb the ionosphere,impacting satellitebased systems such as the Global Navigation Satellite System(GNSS),communication links,and power infrastructure.This study models the ionospheric response to geomagnetic storms over East Africa using GNSS-derived Total Electron Content(TEC)data from five International GNSS Service(IGS)stations during solar cycle 24(2008-2019).We identified geomagnetic storms using the criteria of Disturbance storm time(Dst)≤-30nT and kp≥3,yielding 802 events,of which 787 were CIR-driven and 15 CMEdriven.To determine the optimal background method for ionospheric storm extraction,five approaches were tested.The monthly median vertical TEC(VTEC)method provided the best performance(Root mean square error,RMSE=26.42 TECU;Mean absolute error,MAE=17.10 TECU),while the five internationally quietest days gave the least performance(RMSE=50.82 TECU;MAE=30.96 TECU).We then developed a storm-time Artificial Neural Network(ANN)model for ionospheric storms.The inputs include solar activity factor(10.7P F),hour of day(HR),day of year(DOY),latitude,longitude,z-component of the interplanetary magnetic field(IMF Bz),and Dst index,representing solar,diurnal,seasonal,spatial,and geomagnetic dependencies.The output wasΔVTEC,with storm conditions defined as deviations with a magnitude greater than 45%.The optimum ANN model had a configuration of 9 inputs,16 hidden neurons,1 output,with an RMSE of 23.49%.The ANN model performance was robust under high solar activity and quiet to moderate geomagnetic conditions with an average RMSE of 23%and MAE of 16.5%,though errors increased during intense geomagnetic storm periods.These results demonstrate that ANN models can reliably capture diurnal and seasonal ionospheric variability in East Africa and provide a foundation for regional space weather forecasting and mitigation strategies.展开更多
文摘Geomagnetic storms significantly disturb the ionosphere,impacting satellitebased systems such as the Global Navigation Satellite System(GNSS),communication links,and power infrastructure.This study models the ionospheric response to geomagnetic storms over East Africa using GNSS-derived Total Electron Content(TEC)data from five International GNSS Service(IGS)stations during solar cycle 24(2008-2019).We identified geomagnetic storms using the criteria of Disturbance storm time(Dst)≤-30nT and kp≥3,yielding 802 events,of which 787 were CIR-driven and 15 CMEdriven.To determine the optimal background method for ionospheric storm extraction,five approaches were tested.The monthly median vertical TEC(VTEC)method provided the best performance(Root mean square error,RMSE=26.42 TECU;Mean absolute error,MAE=17.10 TECU),while the five internationally quietest days gave the least performance(RMSE=50.82 TECU;MAE=30.96 TECU).We then developed a storm-time Artificial Neural Network(ANN)model for ionospheric storms.The inputs include solar activity factor(10.7P F),hour of day(HR),day of year(DOY),latitude,longitude,z-component of the interplanetary magnetic field(IMF Bz),and Dst index,representing solar,diurnal,seasonal,spatial,and geomagnetic dependencies.The output wasΔVTEC,with storm conditions defined as deviations with a magnitude greater than 45%.The optimum ANN model had a configuration of 9 inputs,16 hidden neurons,1 output,with an RMSE of 23.49%.The ANN model performance was robust under high solar activity and quiet to moderate geomagnetic conditions with an average RMSE of 23%and MAE of 16.5%,though errors increased during intense geomagnetic storm periods.These results demonstrate that ANN models can reliably capture diurnal and seasonal ionospheric variability in East Africa and provide a foundation for regional space weather forecasting and mitigation strategies.
