Modelling storm-time TEC changes using linear and non-linear techniques - Uwamahoro, Jean Claude (Rhodes University, Faculty of Science, Department of Physics and Electronics, 2019)
Statistical models based on empirical orthogonal functions (EOF) analysis and non-linear regression analysis (NLRA) were developed for the purpose of estimating the ionospheric total electron content (TEC) during geomagnetic storms. The well-known least squares method (LSM) and Metropolis-Hastings algorithm (MHA) were used as optimization techniques to determine the unknown coefficients of the developed analytical expressions. Artificial Neural Networks (ANNs), the International Reference Ionosphere (IRI) model, and the Multi-Instrument Data Analysis System (MIDAS) tomographic inversion algorithm were also applied to storm-time TEC modelling/reconstruction for various latitudes of the African sector and surrounding areas. This work presents some of the first statistical modeling of the mid-latitude and low-latitude ionosphere during geomagnetic storms that includes solar, geomagnetic and neutral wind drivers.Development and validation of the empirical models were based on storm-time TEC data derived from the global positioning system (GPS) measurements over ground receivers within Africa and surrounding areas. The storm criterion applied was Dst 6 âˆ’50 nT and/or Kp > 4. The performance evaluation of MIDAS compared with ANNs to reconstruct storm-time TEC over the African low- and mid-latitude regions showed that MIDAS and ANNs provide comparable results. Their respective mean absolute error (MAE) values were 4.81 and 4.18 TECU. The ANN model was, however, found to perform 24.37 % better than MIDAS at estimating storm-time TEC for low latitudes, while MIDAS is 13.44 % more accurate than ANN for the mid-latitudes. When their performances are compared with the IRI model, both MIDAS and ANN model were found to provide more accurate storm-time TEC reconstructions for the African low- and mid-latitude regions. A comparative study of the performances of EOF, NLRA, ANN, and IRI models to estimate TEC during geomagnetic storm conditions over various latitudes showed that the ANN model is about 10 %, 26 %, and 58 % more accurate than EOF, NLRA, and IRI models, respectively, while EOF was found to perform 15 %, and 44 % better than NLRA and IRI, respectively. It was further found that the NLRA model is 25 % more accurate than the IRI model. We have also investigated for the first time, the role of meridional neutral winds (from the Horizontal Wind Model) to storm-time TEC modelling in the low latitude, northern and southern hemisphere mid-latitude regions of the African sector, based on ANN models. Statistics have shown that the inclusion of the meridional wind velocity in TEC modelling during geomagnetic storms leads to percentage improvements of about 5 % for the low latitude, 10 % and 5 % for the northern and southern hemisphere mid-latitude regions, respectively. High-latitude storm-induced winds and the inter-hemispheric blows of the meridional winds from summer to winter hemisphere have been suggested to be associated with these improvements.
Keywords: Magnetic storms; Astronomy -- Computer programs; Imaging systems in astronomy; Ionospheric storms; Electrons -- Measurement; Magnetosphere -- Observations