Application of neural networks to South African GPS TEC modelling

The propagation of radio signals in the Earth’s atmosphere is dominantly affected by the ionosphere due to its dispersive nature. Global Positioning System (GPS) data provides relevant information that leads to the derivation of total electron content (TEC) which can be considered as the ionosphere’s measure of ionisation. This paper presents part of a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. The South African GPS receiver network is operated and maintained by the Chief Directorate Surveys and Mapping (CDSM) in Cape Town, South Africa. Vertical total electron content (VTEC) was calculated for four GPS receiver stations using the Adjusted Spherical Harmonic (ASHA) model. Factors that influence TEC were then identified and used to derive input parameters for the NN. The well established factors used are seasonal variation, diurnal variation, solar activity and magnetic activity. Comparison of diurnal predicted TEC values from both the NN model and the International Reference Ionosphere (IRI-2001) with GPS TEC revealed that the IRI provides more accurate predictions than the NN model during the spring equinoxes. However, on average the NN model predicts GPS TEC more accurately than the IRI model over the GPS locations considered within South Africa

Statistical analysis of the correlation between the equatorial electrojet and the occurrence of the equatorial ionisation anomaly over the East African sector

This study presents statistical quantification of the correlation between the equatorial electrojet (EEJ) and the occurrence of the equatorial ionisation anomaly (EIA) over the East African sector. The data used were for quiet geomagnetic conditions (Kp  ≤  3) during the period 2011–2013. The horizontal components, H, of geomagnetic fields measured by magnetometers located at Addis Ababa, Ethiopia (dip lat.  ∼ 1° N), and Adigrat, Ethiopia (dip lat.  ∼ 6° N), were used to determine the EEJ using differential techniques. The total electron content (TEC) derived from Global Navigation Satellite System (GNSS) signals using 19 receivers located along the 30–40° longitude sector was used to determine the EIA strengths over the region. This was done by determining the ratio of TEC over the crest to that over the trough, denoted as the CT : TEC ratio. This technique necessitated characterisation of the morphology of the EIA over the region. We found that the trough lies slightly south of the magnetic equator (0–4° S). This slight southward shift of the EIA trough might be due to the fact that over the East African region, the general centre of the EEJ is also shifted slightly south of the magnetic equator. For the first time over the East African sector, we determined a threshold daytime EEJ strength of  ∼  40 nT that is mostly associated with prominent EIA occurrence during a high solar activity period. The study also revealed that there is a positive correlation between daytime EEJ and EIA strengths, with a strong positive correlation occurring during the period 13:00–15:00 LT

Investigating the performance of neural network backpropagation algorithms for TEC estimations using South African GPS data

In this work, results obtained by investigating the application of different neural network backpropagation training algorithms are presented. This was done to assess the performance accuracy of each training algorithm in total electron content (TEC) estimations using identical datasets in models development and verification processes. Investigated training algorithms are standard backpropagation (SBP), backpropagation with weight delay (BPWD), backpropagation with momentum (BPM) term, backpropagation with chunkwise weight update (BPC) and backpropagation for batch (BPB) training. These five algorithms are inbuilt functions within the Stuttgart Neural Network Simulator (SNNS) and the main objective was to find out the training algorithm that generates the minimum error between the TEC derived from Global Positioning System (GPS) observations and the modelled TEC data. Another investigated algorithm is the MatLab based Levenberg-Marquardt backpropagation (L-MBP), which achieves convergence after the least number of iterations during training. In this paper, neural network (NN) models were developed using hourly TEC data (for 8 years: 2000–2007) derived from GPS observations over a receiver station located at Sutherland (SUTH) (32.38° S, 20.81° E), South Africa. Verification of the NN models for all algorithms considered was performed on both "seen" and "unseen" data. Hourly TEC values over SUTH for 2003 formed the "seen" dataset. The "unseen" dataset consisted of hourly TEC data for 2002 and 2008 over Cape Town (CPTN) (33.95° S, 18.47° E) and SUTH, respectively. The models' verification showed that all algorithms investigated provide comparable results statistically, but differ significantly in terms of time required to achieve convergence during input-output data training/learning. This paper therefore provides a guide to neural network users for choosing appropriate algorithms based on the availability of computation capabilities used for research

A recurrent neural network approach to quantitatively studying solar wind effects on TEC derived from GPS; preliminary results

This paper attempts to describe the search for the parameter(s) to represent solar wind effects in Global Positioning System total electron content (GPS TEC) modelling using the technique of neural networks (NNs). A study is carried out by including solar wind velocity (Vsw), proton number density (Np) and the Bz component of the interplanetary magnetic field (IMF Bz) obtained from the Advanced Composition Explorer (ACE) satellite as separate inputs to the NN each along with day number of the year (DN), hour (HR), a 4-month running mean of the daily sunspot number (R4) and the running mean of the previous eight 3-hourly magnetic A index values (A8). Hourly GPS TEC values derived from a dual frequency receiver located at Sutherland (32.38° S, 20.81° E), South Africa for 8 years (2000–2007) have been used to train the Elman neural network (ENN) and the result has been used to predict TEC variations for a GPS station located at Cape Town (33.95° S, 18.47° E). Quantitative results indicate that each of the parameters considered may have some degree of influence on GPS TEC at certain periods although a decrease in prediction accuracy is also observed for some parameters for different days and seasons. It is also evident that there is still a difficulty in predicting TEC values during disturbed conditions. The improvements and degradation in prediction accuracies are both close to the benchmark values which lends weight to the belief that diurnal, seasonal, solar and magnetic variabilities may be the major determinants of TEC variability

Daytime twin-peak structures observed at southern African and European middle latitudes on 8–13 April 2012

Daytime twin-peak structures, also known as bite-out or diurnal double-maxima structures, are ionospheric phenomena in which the diurnal ionospheric trend shows two peaks (instead of the normal one) during the daytime. This study reports on first simultaneous observations of these structures in the Global Positioning System and ionosonde measurements from the southern African and European middle-latitude stations during a mostly quiet geomagnetic condition period of 8–13 April 2012, which indicates that their occurrence and therefore driving mechanism(s) may not be localised. It is found that the daytime twin-peak structures generally appear later in the Northern Hemisphere with a 1–3 h latency although they propagate mostly equatorward in both hemispheres. Proxies of meridional neutral winds were calculated from available manually scaled ionosonde measurements and used to explore their potential as drivers of the structures. Bite-out events were linked to downward drifts of the vertical component of equivalent neutral winds causing plasma depletions. In addition, evidence of sporadic E layers at the same time as enhancements of daytime twin-peak structures suggests that the tides had influence via the meridional wind shear in generating these structures through the dynamo electric field which resulted in upward <b>E</b>  ×  <b>B</b> drifts

Determinations of ionosphere and plasmasphere electron content for an African chain of GPS stations

The confluence of recent instrumentation deployments in Africa with developments for the determination of plasmasphere electron content using Global Positioning System (GPS) receivers has provided new opportunities for investigations in that region. This investigation, using a selected chain of GPS stations, extends the method (SCORPION) previously applied to a chain of GPS stations in North America in order to separate the ionosphere and plasmasphere contributions to the total electron content (TEC) during a day (24 July) in 2011. The results span latitudes from the southern tip of Africa, across the Equator, to the southern Arabian Peninsula, providing a continuous latitudinal profile for both the ionosphere and plasmasphere during this day.The peak diurnal vertical ionosphere electron content (IEC) increases from about 14 TEC units (1 TEC unit  =  1016 electrons m−2) at the southernmost station to about 32 TEC units near the geographic equator, then decreases to about 28 TEC units at the Arabian Peninsula. The peak diurnal slant plasmasphere electron content (PEC) varies between about 4 and 7 TEC units among the stations, with a local latitudinal profile that is significantly influenced by the viewing geometry at the station location, relative to the magnetic field configuration. In contrast, the peak vertical PEC varies between about 1 and 6 TEC units among the stations, with a more uniform latitudinal variation.Comparisons to other GPS data analyses are also presented for TEC, indicating the influence of the PEC on the determination of latitudinal TEC variations and also on the absolute TEC levels, by inducing an overestimate of the receiver bias. The derived TEC latitudinal profiles, in comparison to global map profiles, tend to differ from the map results only about as much as the map results differ among themselves. A combination of ionosonde IEC and alternative GPS TEC measurements, which in principle permits a PEC determination through their difference, was compared to the composite and separate ionosphere and plasmasphere contributions derived solely by the SCORPION method for one station. Although there is considerably more scatter in the PEC values derived from the difference of the GPS TEC and ionosonde IEC measurements compared to the PEC values derived by the SCORPION method, the average overhead values for this day are comparable for the two methods, near 2 TEC units, at the South African site examined.This initial investigation provides a basis for day-to-day TEC monitoring for Africa, with separate ionosphere and plasmasphere electron content determinations

Unexpected Southern Hemisphere ionospheric response to geomagnetic storm of 15 August 2015

Geomagnetic storms are the most pronounced phenomenon of space weather. When studying ionospheric response to a storm of 15 August 2015, an unexpected phenomenon was observed at higher middle latitudes of the Southern Hemisphere. This phenomenon was a localized total electron content (TEC) enhancement (LTE) in the form of two separated plumes, which peaked southward of South Africa. The plumes were first observed at 05:00 UT near the southwestern coast of Australia. The southern plume was associated with local time slightly after noontime (1–2 h after local noon). The plumes moved with the Sun. They peaked near 13:00 UT southward of South Africa. The southern plume kept constant geomagnetic latitude (63–64° S); it persisted for about 10 h, whereas the northern plume persisted for about 2 h more. Both plumes disappeared over the South Atlantic Ocean. No similar LTE event was observed during the prolonged solar activity minimum period of 2006–2009. In 2012–2016 we detected altogether 26 LTEs and all of them were associated with the southward excursion of Bz. The negative Bz excursion is a necessary but not sufficient condition for the LTE occurrence as during some geomagnetic storms associated with negative Bz excursions the LTE events did not appear

Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks

Estimating the geoeffectiveness of solar events is of significant importance for space weather modelling and prediction. This paper describes the development of a neural network-based model for estimating the probability occurrence of geomagnetic storms following halo coronal mass ejection (CME) and related interplanetary (IP) events. This model incorporates both solar and IP variable inputs that characterize geoeffective halo CMEs. Solar inputs include numeric values of the halo CME angular width (AW), the CME speed (<I>V</I>_cme), and the comprehensive flare index (cfi), which represents the flaring activity associated with halo CMEs. IP parameters used as inputs are the numeric peak values of the solar wind speed (<I>V</I>_sw) and the southward Z-component of the interplanetary magnetic field (IMF) or <I>B</I>_s. IP inputs were considered within a 5-day time window after a halo CME eruption. The neural network (NN) model training and testing data sets were constructed based on 1202 halo CMEs (both full and partial halo and their properties) observed between 1997 and 2006. The performance of the developed NN model was tested using a validation data set (not part of the training data set) covering the years 2000 and 2005. Under the condition of halo CME occurrence, this model could capture 100% of the subsequent intense geomagnetic storms (Dst &le; −100 nT). For moderate storms (−100 < Dst &le; −50), the model is successful up to 75%. This model's estimate of the storm occurrence rate from halo CMEs is estimated at a probability of 86%