1. Fourth European Space Weather Week 5-9 Nov. 2007 TEC F ORECASTING D URING D ISTURBED S PACE W EATHER C ONDITIONS : A P OSSIBLE A LTERNATIVE TO THE IRI-2001 Yurdanur Tulunay 1, Erdem Turker Senalp 2, Ersin Tulunay 2 ODTU / METU Ankara, TURKEY (1)Dept. of Aerospace Eng., ytulunay@metu.edu.tr (2) Dept. of Electrical and Electronics Eng. ESWW4, 5-9 Nov. 2007, Brussels1

2. CONTENTS 1. Introduction 2. METU-NN-C 3. Data Organisation 4. Results 5. Conclusions 6. Acknowledgements 7. References ESWW4, 5-9 Nov. 2007, Brussels2

3. INTRODUCTION Ionospheric processes: highly nonlinear and dynamic TEC: key parameter in navigation and telecommunication METU Group: specialized on data driven modelling since 1990’s Recently developped:NN and Cascade Model based on the Hammerstein system modelling ESWW4, 5-9 Nov. 2007, Brussels3

4. Objective: to forecast TEC with higher accuracy under the influence of the extreme solar events. A case study: Solar Events of April 2002 A possible alternative to IRI-2001? ESWW4, 5-9 Nov. 2007, Brussels4

5. Why and How? Mathematical models of the ionospheric parameters (i.e. TEC) DIFFICULT Data-driven approaches (i.e. NN modelling) employed in parallel with the mathematical models Therefore, METU-NN-C using Bezier curves to represent nonlinearities ESWW4, 5-9 Nov. 2007, Brussels5

6. METU-NN-C TEC map over Europe constructed by METU-NN in 2004 and 2006 (Tulunay et al. [2004a, 2006] ) to increase the performance, a new technique, METU-NN-C developped [Senalp, 2007] ESWW4, 5-9 Nov. 2007, Brussels6

7. Fig. 1. Construction of the METU-NN-C Models [Senalp et al., 2007] ESWW4, 5-9 Nov. 2007, Brussels7 1 2 3

8. Block 1: METU-NN model estimates the state-like variables for the METU-C ESWW4, 5-9 Nov. 2007, Brussels8 k : Discrete time index u Dp (k) : Inputs x Dq (k) : the internal variables of the METU-C

9. Block 2: Construction of Nonlinear Static Block of METU-C ESWW4, 5-9 Nov. 2007, Brussels9

10. Block 3:Construction of Linear Dynamic Block of METU-C ESWW4, 5-9 Nov. 2007, Brussels10

11. The Generic METU-NN-C Model ESWW4, 5-9 Nov. 2007, Brussels11

12. ESWW4, 5-9 Nov. 2007, Brussels12 Phases of Application of METU-NN-C: ‘Training’ ‘Test’ Inputs: Present value of TEC: TEC(k) Temporal parameters: Trigonometric comp. of time Bezier curves to represent NONLINEARITIES METU-NN: State-like variable estimator Output: Forecast TEC values one hour in advance

13. DATA ORGANIZATION 10-min GPS-TEC data of Chilbolton (51.8˚N; 1.26˚W) Hailsham (50.9˚N; 0.3˚E) Development Step: Training: Chilbolton TEC (April; May 2000, 2001) Validation: Chilbolton TEC (April-May 2000, 2001) Operation Step: Validation: Hailsham TEC (April; May 2002) 2000-2002 SSN max. years ESWW4, 5-9 Nov. 2007, Brussels13

14. RESULTS

15. Fig. 2 Observed and one hour ahead Forecast Hailsham TEC values for April, May 2002 [Senalp et al., 2007] ESWW4, 5-9 Nov. 2007, Brussels15

16. Fig. 3. METU-NN-C and IRI-2001 during disturbed conditions (Hailsham) ESWW4, 5-9 Nov. 2007, Brussels16

17. - Fig. 4. Scatter diagrams and best-fit lines: in 18-19 April 2002 at Hailsham ESWW4, 5-9 Nov. 2007, Brussels17 METU-NN-C IRI-2001

18. Table 1. Performance of models (18-19 April 2002; Hailsham) ESWW4, 5-9 Nov. 2007, Brussels18

19. CONCLUSIONS During disturbed SW conditions, METU-NN-C seems to show better performance over IRI-2001 METU-NN-C Model - more versatile and has got advantages provided that the representative data are available ESWW4, 5-9 Nov. 2007, Brussels19

20. Acknowledgements This work is partially supported by EU action of COST 296 (Mitigation of Ionospheric Effects on Radio Systems) TUBITAK-ÇAYDAG (105Y003) GPS-TEC data are kindly provided by Dr. Lj. R. Cander ESWW4, 5-9 Nov. 2007, Brussels20

21. References Altinay O., E.Tulunay, and Y. Tulunay (1997), Forecasting of ionospheric critical frequency using neural networks, Geophys. Res. Lett., 24(12), 1467-1470, and COST251 TD(96)016. Bézier, P.E. (1972), Numerical Control – Mathematics and Applications, Translated by Forrest A.R. and Pankhurst A.F., pp. 115-136, John Wiley & Sons Ltd., England. Bilitza D. (2001), International Reference Ionosphere 2000, Radio Sci., 36(2), 261-275. Hagan M.T., and M.B. Menhaj (1994), Training Feedforward Networks with the Marquard Algorithm, IEEE T. on Neural Networ., 5(6), 989-993. Haykin S. (1999), Neural Networks: A Comprehensive Foundation, 2nd ed., pp. 2, 10, 21-22, 83-84, 169, 215, Prentice-Hall, Inc., New Jersey, USA. Ikonen E., and K. Najim (1999), Learning control and modelling of complex industrial processes, Overview report of the activities within the European Science Foundation's programme on Control of Complex Systems (COSY) Theme 3: Learning control, February 1999. Narendra K.S., and P.G. Gallman (1966), An Iterative Method for the Identification of Nonlinear Systems Using a Hammerstein Model, IEEE T. Automat. Contr., 546-550. Radicella S.M., and E. Tulunay (2004), Space plasma effects on Earth-space and satellite-to-satellite communications: Working Group 4 overview, Ann. Geophys.-Italy, 47(2/3), 1279-1283. Rogers D.F., and Adams J.A. (1990), Mathematical Elements for Computer Graphics, 2nd ed., pp. 289-308, 379-477, McGraw-Hill, Inc., New York, USA. Senalp E.T., E. Tulunay, and Y. Tulunay (2006a), Neural Networks and Cascade Modeling Technique in System Identification, TAINN’2005, 16-17 June. 2005, Cesme, Izmir, Turkey, 286-293; Lect. Notes Artif. Int., 3949, 84-91. ESWW4, 5-9 Nov. 2007, Brussels21

22. Senalp E.T., E. Tulunay, and Y. Tulunay (2006b), System Identification by using Cascade Modeling Technique with Bezier Curve Nonlinearity Representations, TAINN’2006, 21-23 June 2006, Akyaka, Mugla, Turkey, 75-82. Senalp E.T. (2007), Cascade Modeling of Nonlinear Systems, Ph-D Thesis, (Supervisor: E. Tulunay), Middle East Technical University, Dept. of Electrical and Electronics Eng., Ankara, Turkey, August 2007. Senalp E.T., Y. Tulunay, and E. Tulunay (2007), Total Electron Content (TEC) Forecasting by Cascade Modeling: A Possible Alternative to the IRI-2001, Radio Sci., (submitted) Stamper R., A. Belehaki, D. Buresova, Lj.R. Cander, I. Kutiev, M. Pietrella, I. Stanislawska, S. Stankov, I. Tsagouri, Y.K. Tulunay, and B. Zolesi (2004), Nowcasting, forecasting and warning for ionospheric propagation: tools and methods, Ann. Geophys.-Italy, 47(2/3), 957-983. Tulunay, E. (1991), Introduction to Neural Networks and their Application to Process Control, in Neural Networks Advances and Applications, edited by E. Gelenbe, pp. 241-273, Elsevier Science Publishers B.V., North-Holland. Tulunay E., E.T. Senalp, Lj.R. Cander, Y.K. Tulunay, A.H. Bilge, E. Mizrahi, S.S. Kouris, N. Jakowski (2004a), Development of algorithms and software for forecasting, nowcasting and variability of TEC, Ann. Geophys.-Italy, 47(2/3), 1201-1214. Tulunay E., Y. Tulunay, E.T. Senalp, Lj.R. Cander (2004b), Forecasting GPS TEC Using the Neural Network Technique “A Further Demonstration”, Bulgarian Geophysical Journal, 30(1-4), 53-61. Tulunay E., E.T. Senalp, S.M. Radicella, Y. Tulunay (2006), Forecasting Total Electron Content Maps by Neural Network Technique, Radio Sci., 41(4), RS4016. Tulunay Y., E. Tulunay, and E.T. Senalp (2001), An Attempt to Model the Influence of the Trough on HF Communication by Using Neural Network, Radio Sci., 36(5), 1027-1041. Tulunay Y., E. Tulunay, and E.T. Senalp (2004a), The Neural Network Technique-1: A General Exposition, Adv. Space Res., 33(6), 983-987. Tulunay Y., E. Tulunay, and E.T. Senalp (2004b), The Neural Network Technique-2: An Ionospheric Example Illustrating its Application, Adv. Space Res., 33(6), 988-992. ESWW4, 5-9 Nov. 2007, Brussels22