1. Mpho Tshisaphungo,
Lee-Anne McKinnell and John Bosco Habarulema
Development of an Ionospheric Storm-time Index over South African Region
ESWW Oostende, Belgium – 18 November 2016

2. OUTLINE Research Objective Neural Network Modelling Technique
Modelling ionospheric storm with SymH and Bz
Summary and Future Work

3. Research Objective
Objective: Modelling of the South African regional ionosphere during storm conditions.

4. foF2 = ((foF2 – foF2med)/foF2med)*100
Data Sources
Storm time foF2 data from 4 ionosonde stations: Grahamstown (1996 – 2014), Hermanus ( ), Louisvale ( ), Madimbo ( ).
foF2 data was used to derive the changes in foF2 during storm conditions.
foF2 = ((foF2 – foF2med)/foF2med)*100
Storm criterion: Dst index ≤ -50 nT
Model inputs
dn = day number of the year: seasonal, annual and semi-annual
hr = UT hour: diurnal variation of foF2
F10.7p = (F F10.7A)/2: solar activity
F10.7A is the average of F10.7 over the previous 81 days.
symH : Mid-latitude geomagnetic indices.
sources: and
4 Ionosonde Stations in South Africa

5. Modelling Techniques
Modelling techniques considered: Regression Analysis (RA) and Neural Networks (NN)
Regression Analysis is a statistical tool used to model the relationship between dependent and independent variable.
Results from RA techniques are not shown here because they were not suitable.
Neural Networks are software tools capable of learning data patterns given both input and output parameters.
NN (feed forward network) has three types of layers: input, hidden and output.

6. Modelling Techniques
dn was decomposed into 4 components (dnc, dns, dnca, dnsa).
dnc and dns are the cosine and sine component of day number.
dnca and dnsa are the semi-cosine and –sine of day number.
hr was decomposed into 2 components (hrc, hrs).
hrc and hrs are the cosine and sine component of hour.
Need number of hidden nodes which will give me the optimum output
Initial number of hidden nodes was equal to number of input parameters.
NN training output was selected based on lowest RMSE
Model 1
𝑓𝑜𝐹2  𝑓 𝑑𝑛𝑐,𝑑𝑛𝑠,𝑑𝑛𝑐𝑎,𝑑𝑛𝑠𝑎,ℎ𝑟𝑐, ℎ𝑟𝑠, 𝐹10.7𝑝, 𝑠𝑦𝑚𝐻
RMSE =
hr𝑐=cos⁡( 2π∗ℎ𝑟 24 )
hrs=sine⁡( 2π∗ℎ𝑟 24 )
d𝑛𝑐=cos⁡( 2π∗𝑑𝑛 )
d𝑛𝑐𝑎=cos⁡( 4π∗𝑑𝑛 )
d𝑛𝑠=sine⁡( 2π∗𝑑𝑛 )
d𝑛𝑠𝑎=sine⁡( 4π∗𝑑𝑛 )

7. Preliminary Results

8. Testing foF2 modelling during storms of 1999 and 2001
Results are based on data from Grahamstown station covering the period 1996 – 2014.
Red line represents the NN model 1 output (inputs: dn, hr, F10.7p, symH ).
Toward the model improvement: IMF Bz was introduced as additional input to take into account the effect of the planetary magnetic field within the magnetosphere. NN model 2 inputs: dn, hr, F10.7p, symH , IMF Bz.
Following the same procedure, modelled 𝑓𝑜𝐹2 with IMF Bz as input is shown in yellow.
Negative storms: NN model is following the trend but not sufficiently.
Positive Storms: NN model is failing to capture positive enhancement of 𝑓𝑜𝐹2.

9. Testing foF2 modelling during storms of 2003, 2004, 2007 and 2010

10. 𝑝𝑒𝑟𝑓𝑜𝑚𝑎𝑛𝑐𝑒= 𝑅𝑀𝑆𝐸1 𝑎𝑣𝑔 −𝑅𝑀𝑆𝐸2𝑎𝑣𝑔 𝑅𝑀𝑆𝐸2𝑎𝑣𝑔 ∗100%
Statistical Analysis
𝑝𝑒𝑟𝑓𝑜𝑚𝑎𝑛𝑐𝑒= 𝑅𝑀𝑆𝐸1 𝑎𝑣𝑔 −𝑅𝑀𝑆𝐸2𝑎𝑣𝑔 𝑅𝑀𝑆𝐸2𝑎𝑣𝑔 ∗100%
= 0.82 %
𝑅𝑀𝑆𝐸= 1 𝑁 𝑖=1 𝑁 ∆𝑓𝑜𝐹2𝑚𝑜𝑑𝑒𝑙 − ∆𝑓𝑜𝐹2 2
RMSE1avg = Average RMSE over 6 storms for model 1.
RMSE2 avg= Average RMSE over 6 storms for model 2.
Model 2 performs 0.82 % better than model 1. The improvement is not significant.

11. Future Work and References
Model improvement in term of additional inputs.
Explore other modelling techniques.
Include other ionosonde stations in South Africa.
Aggarwal, Malini, et al. "Day-to-day variability of equatorial anomaly in GPS-TEC during low solar activity period." Advances in Space Research (2012):
Lee, H‐B., et al. "Characteristics of global plasmaspheric TEC in comparison with the ionosphere simultaneously observed by Jason‐1 satellite." Journal of Geophysical Research: Space Physics (2013):
Wanliss, James A., and Kristin M. Showalter. "High‐resolution global storm index: Dst versus SYM‐H." Journal of Geophysical Research: Space Physics 111.A2 (2006).