Abstract
Modeling and forecasting of Total Electron Content (TEC) values by an Artificial Neural Network model (ANNm) have high agreement on November 2003, 2004 superstorms. The work discusses Solar Wind Parameters (SWp) from OMNI (Operating Missions as a Node on the Internet) and TEC (TECU) data (International Reference Ionosphere) IRI-2012, IRI-2016 on November 20, 2003 (Dst = –422 nT) and on November 08, 2004 (Dst = –374 nT) Geomagnetic Storms (GSs). The paper commences with a 120-hour GS exhibition of SWp and proceeds with the correlation data of the variables, their hierarchical tracks, and inner dispersions. The ANNm with SWp as the input and TEC data as the output are introduced. The performance of the ANNm for 2003 and 2004 superstorms is adequate. The Correlation Coefficient (R) and Root Mean Square Error (RMSE) of the ANNm are 97.5%, 1.17 TECU (IRI-2012), and 97.9%, 1.09 TECU (IRI-2016) for the 2003 GS and 97.0%, 0.89 TECU (IRI-2012), and 98.0%, 1.61 TECU (IRI-2016) for 2004 GS. Parameters effect of the R constant of TEC data points out to the dynamic pressure (nPa), the magnetic field Bz component (nT), the flow speed (km/s), and the proton density (1/cm3). Besides, the absolute total error and the variance of the predicted TEC data for November 2003 and November 2004 GSs are 0.06 (0.30%) with 0.013 variance (IRI-2012), 0.09 (0.49%) with 0.016 variance (IRI-2016) for 2003 storm and 0.13 (0.73%) with 0.033 variance (IRI-2012), and 0.11 (1.06%) with 0.035 variance (IRI-2016) for 2004. It means that the paper models TEC data with considerable consistency over the SWp.
Highlights
A geomagnetic storm (GS) [1–11] is the effect of the solar wind to the magnetic field of the Earth. e solar wind traveling through the interplanetary medium has tremendous energy-charge density. e Geomagnetic Storms (GSs) is named by the southward peak value of the disturbance storm time (Dst) zonal geomagnetic index after the Bz magnetic field is rushed from the northward to the southward. e GS initiates with the deceleration of the flow wind velocity v
The dynamic pressure P and the proton density N (1/ cm3) respond with the sudden acceleration called coronal mass ejection (CME). e high-speed solar wind reaches the ionosphere with CME and causes ionospheric instabilities through intense currents [12]. e ionosphere coat, which covers from 50 to 1000 km, is the layer of the Earth’s upper atmosphere. e total electron content (TEC) is one of the principal ionospheric parameters. e TEC (TECU) describes the electron density at a cross-section zone of 1 m2 during the transferring of the signal. e unit of TEC is specified by 1016 electrons/m2 [1, 13–15]. e electric field fluctuation in lower latitudes induces ionospheric-magnetospheric disturbances that may be classified as negative or positive
Ionospheric storms, which alter depending on the solar action, the Earth’s turning, and spatial, regular, monthly, and seasonal circumstances, have different impacts in the ionosphere [17, 18]. e TEC values, which change over time and ought to be evaluated along with their location in space, are the principal factors for solar activity and ionosphere-magnetosphere-Sun interaction [3, 19–36]. is essay predicts the TEC values through an artificial neural network model (ANNm) [7, 8, 37–45] over the superstorms of November 20, 2003 (Dst –422 nT) and November 08, 2004 (Dst –374 nT). e predicted TEC values are criss cross checked with the values attained from the IRI-2012 and 2016 [31, 46–48] model. e IRI model, which has been incessantly improved after its first version
Summary
Received 22 September 2021; Revised 29 January 2022; Accepted 1 February 2022; Published 28 February 2022. The absolute total error and the variance of the predicted TEC data for November 2003 and November 2004 GSs are 0.06 (0.30%) with 0.013 variance (IRI-2012), 0.09 (0.49%) with 0.016 variance (IRI-2016) for 2003 storm and 0.13 (0.73%) with 0.033 variance (IRI-2012), and 0.11 (1.06%) with 0.035 variance (IRI-2016) for 2004. It means that the paper models TEC data with considerable consistency over the SWp
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