UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year 2007 hosted by the Korea Astronomy and Space Science Institute on behalf of the Government of the Republic of Korea September 2009, Daejeon, Republic of Korea 1 1 AN ATTEMPT FOR FORECASTING OF SOLAR FLARE INDEX DURING SOLAR MAXIMUM Ersin Tulunay (1), Atila Özgüç (2), Erdem Türker Şenalp (1), Tamer Ataç (2), Yurdanur Tulunay (3) and Saffet Yeşilyurt (2) (1) Dept. of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey (2) Kandilli Observatory and Earthquake Res. Inst., Boğaziçi University, İstanbul, Turkey (3) Dept. of Aerospace Engineering, Middle East Technical University, Ankara, Turkey

2 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea 2 CONTENTS 1. Introduction 2. KOERI-FI-1 3. Data Organisation 4. Results 5. Conclusions 6. Acknowledgements 7. References

3 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea INTRODUCTION KOERI Group:calculate and issue observatory data METU Group: specialized on data driven modelling since 1990’s Background:data and models on key parameters of the Near Earth Space processes Achievements:theoretical and experimental This work mentions the forecast of Solar Flare Index (FI)

4 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Solar activity processes - highly non-linear and time-varying. Mathematical modeling based on first physical principles - extremely difficult if not impossible In such cases, data driven models - i.e. Neural Networks - very promising for using them in parallel to mathematical models

5 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea The Solar Flare Index (FI) calculated and issued internationally by Kandilli Observatory, İstanbul very important because of the increasing awareness of SpW situation and the effects of SpW on biological and technological systems operating on Earth and in the Near Earth Space Forecasting of FI is an important achievement in forecasting of SpW situation

6 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Solar Flare Solar Flare: An enormous explosion in the solar atmosphere defined as a sudden, rapid and intense variation in brightness Believed to result from the sudden release of energy stored in magnetic fields [Atac, 2009]

7 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Solar Flare Index (FI) FI: A measure of the short-lived solar flare activity on the Sun - Atac, 2009; - NASA, Solar Flare Theory; - Daily Solar Flares Images : Ondrejov Observatory

8 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Solar Flare Index (FI) To quantify the daily flare activity, - Kleczek (1952) introduced the quantity "Q = i x t " "i" represents the intensity scale of importance and "t" the duration (in minutes) of the flare Assumpion: The relationship gives roughly the total energy emitted by the flares

9 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea In this work, - NN based model KOERI-FI-1 is designed and operated to forecast daily FI up to 27 days ahead To the best knowledge of the authors, - this is the first attempt to forecast FI

10 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea 1. Introduction 2. KOERI-FI-1 3. Data Organisation 4. Results 5. Conclusions

11 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Background: - Data driven models in parallel with physical models for the Near Earth Space processes [e.g. E. Tulunay, 1991; Altinay et al., 1997; Y. Tulunay et al., 2001; Y. Tulunay et al., 2004a; Y. Tulunay et al., 2004b; E. Tulunay et al., 2004a; E. Tulunay et al., 2004b; E. Tulunay et al., 2006; Senalp et al., 2006; Y. Tulunay et al., 2008a; Y. Tulunay et al., 2008b; Senalp et al., 2008]

12 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea KOERI-FI-1 KOERI-FI-1 is designed to forecast the FI values using a technique based on Neural Networks (NN) - a data-driven modeling approach - consists of - inputs, - neurons in hidden layer and - output layer

13 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea KOERI-FI-1 Architecture: A two-layer feed forward NN Algorithm in training: Levenberg-Marquardt Backpropagation

14 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea 1. Introduction 2. KOERI-FI-1 3. Data Organisation 4. Results 5. Conclusions

15 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea FI Data - The daily flare index for the 21, 22, and 23 st Solar Cycles determined by using the final grouped solar flares compiled by the National Geophysical Data Center - FI data: produced by Dr. T. Ataç and Dr. A. Özgüç, Boğaziçi University, Kandilli Observatory and Earthquake Research Institute [Atac, 2009]

16 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Inputs of the KOERI-FI-1 I/PExplainationNotation 1Solar Flare Index observed at day ‘d’FI(d) 2Sunspot Area at day ‘d’SA(d) 3Sunspot Number at day ‘d’SN(d) 4RF(10.7) index at day ‘d’RF(d) 5Solar Flare Index observed at day ‘d-n’FI(d-n) 6Solar Flare Index observed at day ‘d-2n’FI(d-2n) 7Solar Flare Index First Difference (FD)FI(d) –FI(d-n) 8Solar Flare Index Second Difference (SD)FD(d) – FD(d-n) The output : n days ahead forecast of the Solar Flare Index : FI(d+n)

17 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Data Organisation The data provided by KOERI are grouped in three sets: PhaseData Coverage Training 1 Jan 1988 – 31 Dec 1992 Validation during training 1 Jan 1978 – 31 Dec 1982 Validation during operation 1 Jan 1998 – 31 Dec 2002

18 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea KOERI FI data for selected time intervals

19 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea 1. Introduction 2. KOERI-FI-1 3. Data Organisation 4. Results 5. Conclusions

20 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Case Studies Four different case studies have been performed by developing four different instances of the KOERI-FI-1 The case studies consider forecasting the FI ‘n’ days in advance as follows: 1 day in advance, 3 days in advance, 25 days in advance, and 27 days in advance

21 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea RESULTS The results cover the operation of the instances of the model between 1998 and 2002 The Mean Absolute Errors and the Cross Correlation Coefficients of the observed and forecast FI are presented for four of the case studies

22 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea RESULTS Performance of the KOERI-FI-1 considering FI, SA, SN and RF(10.7) at inputs; and the Forecast FI at output Error Table for 1, 3, 25 and 27 days ahead FI forecasts 1 day ahead 3 days ahead 25 days ahead 27 days ahead Mean Absolute Error Cross Corr. Coeff. (x )

23 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Variation of observed and 1 day ahead forecast FI in between

24 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Variation of observed and 1 d ahead forecast FI in 10 Nov Sep 2001

25 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Scatter diagram of observed and 1 d ahead forecast FI in between

26 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Variation of observed and 3 days ahead forecast FI in between

27 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Variation of observed and 3 d ahead forecast FI in 10 Nov Sep 2001

28 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Scatter diagram of observed and 3 d ahead forecast FI in between

29 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Variation of observed and 25 days ahead forecast FI in between

30 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Scatter diagram of observed and 25 d ahead forecast FI in between

31 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Variation of observed and 27 days ahead forecast FI in between

32 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Scatter diagram of observed and 27 d ahead forecast FI in between

33 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea 1. Introduction 2. KOERI-FI-1 3. Data Organisation 4. Results 5. Conclusions

34 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea CONCLUSIONS Daily SpW related parameters observed during the time periods including the maxima of the 21 st, 22 nd and 23 rd solar cycles were considered FI values have been forecast up to 27 hours in advance using the KOERI-FI-1

35 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea CONCLUSIONS Short term forecast results are promising The cross-correlation coefficient values are higher and the mean absolute error values are smaller in the short- term forecasts (i.e. 1-d and 3-d in advance FI forecasts) The model learned the general shape of the inherent non- linearity

36 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea CONCLUSIONS Considering the extreme FI values, - The model forecasts the tendency towards an increase or decrease in value - However, the forecast values are less accurate quantitatively

37 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea CONCLUSIONS The extreme FI values are very rare; They do not provide enough representative information to the learning process Long-term forecasts in 25-d or 27-d ahead FI forecast case studies have low accuracy

38 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea CONCLUSIONS In summary, in this work, the capability of forecasting FI values using a data driven model, KOERI-FI-1 has been shown To the best knowledge of the authors this has been the first attempt to forecast FI

39 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea Acknowledgements This work is partially supported by COST ES0803 Action COST 296 Action (MIERS) - TUBITAK-ÇAYDAG (105Y003)

40 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar Maximum UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year September 2009, Daejeon, Republic of Korea References Altinay O., E.Tulunay, and Y. Tulunay (1997), Forecasting of ionospheric critical frequency using neural networks, Geophys. Res. Lett., 24(12), , and COST251 TD(96)016. Atac, T.: 2009, What is Flare Index?, web-page (last visited in Sep 2009). Kleczek, J.: 1952, Publ. Inst. Centr. Astron., No. 22, Prague Senalp E.T., E. Tulunay, and Y. Tulunay (2006), Neural Networks and Cascade Modeling Technique in System Identification, TAINN’2005, June. 2005, Cesme, Izmir, Turkey, ; Lect. Notes Artif. Int., 3949, Senalp E.T., E. Tulunay, and Y. Tulunay (2008), Total Electron Content (TEC) Forecasting by Cascade Modeling: A Possible Alternative to the IRI-2001, Radio Sci., RS4016. Tulunay, E. (1991), Introduction to Neural Networks and their Application to Process Control, in Neural Networks Advances and Applications, edited by E. Gelenbe, pp , 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), 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), 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), Tulunay Y., E. Tulunay, and E.T. Senalp (2004a), The Neural Network Technique-1: A General Exposition, Adv. Space Res., 33(6), 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), Tulunay Y., E. Altuntas, E. Tulunay, C. Price, T. Ciloglu, Y. Bahadirlar, E.T. Senalp (2008a), A Case Study on the ELF Characterization of the Earth-Ionosphere Cavity: Forecasting the Schumann Resonance Intensities, Journal of Atmospheric and Solar-Terrestrial Physics, 70, Tulunay Y., E.T. Şenalp, Ş. Öz, L.I. Dorman, E. Tulunay, S.S. Menteş and M.E. Akcan, A Fuzzy Neural Network Model to Forecast the Percent Cloud Coverage and Cloud Top Temperature Maps (2008b), Annales Geophysicae, 26(12),