Mpho Tshisaphungo, Lee-Anne McKinnell and John Bosco Habarulema

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Presentation transcript:

Mpho Tshisaphungo, Lee-Anne McKinnell and John Bosco Habarulema Development of an Ionospheric Storm-time Index over South African Region ESWW13 Oostende, Belgium 14 – 18 November 2016

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

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

foF2 = ((foF2 – foF2med)/foF2med)*100 Data Sources Storm time foF2 data from 4 ionosonde stations: Grahamstown (1996 – 2014), Hermanus (2008 - 2014), Louisvale (2000-2014), Madimbo (2000 - 2014). 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 = (F10.7 + F10.7A)/2: solar activity F10.7A is the average of F10.7 over the previous 81 days. symH : Mid-latitude geomagnetic indices. sources: http://omniweb.gsfc.nasa.gov/form/omni_min.html and http://umlcar.uml.edu/DIDBase/ 4 Ionosonde Stations in South Africa

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.

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 = 14.3113 hr𝑐=cos⁡( 2π∗ℎ𝑟 24 ) hrs=sine⁡( 2π∗ℎ𝑟 24 ) d𝑛𝑐=cos⁡( 2π∗𝑑𝑛 365.25 ) d𝑛𝑐𝑎=cos⁡( 4π∗𝑑𝑛 365.25 ) d𝑛𝑠=sine⁡( 2π∗𝑑𝑛 365.25 ) d𝑛𝑠𝑎=sine⁡( 4π∗𝑑𝑛 365.25 )

Preliminary Results

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.

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

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.

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 49.12 (2012): 1709-1720. 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 118.2 (2013): 935-946. 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).