UN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year 2007...

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 21-25 September 2009, Daejeon, Republic of Korea

11

AN ATTEMPT FOR FORECASTING OFSOLAR 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, [email protected], [email protected]

(2) Kandilli Observatory and Earthquake Res. Inst., Boğaziçi University, İstanbul, [email protected], [email protected], [email protected]

(3) Dept. of Aerospace Engineering, Middle East Technical University, Ankara, [email protected]

2 Tulunay E. et al., An Attempt for Forecasting of Solar Flare Index During Solar MaximumUN/ESA/NASA/JAXA Workshop on Basic Space Science and the International Heliophysical Year 2007 21-25 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


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)


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


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


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]


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


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


In this work,

- NN based model KOERI-FI-1 is designed andoperated to forecast daily FI up to 27 days ahead

To the best knowledge of the authors,

- this is the first attempt to forecast FI


1. Introduction

2. KOERI-FI-1


4. Results

5. Conclusions


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]


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


KOERI-FI-1

Architecture: A two-layer feed forward NN

Algorithm in training: Levenberg-Marquardt Backpropagation


1. Introduction

2. KOERI-FI-1


4. Results

5. Conclusions


FI Data

- The daily flare index for the 21, 22, and 23 st Solar Cyclesdetermined 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]


Inputs of the KOERI-FI-1

I/P Explaination Notation

1 Solar Flare Index observed at day ‘d’ FI(d)

2 Sunspot Area at day ‘d’ SA(d)

3 Sunspot Number at day ‘d’ SN(d)

4 RF(10.7) index at day ‘d’ RF(d)

5 Solar Flare Index observed at day ‘d-n’ FI(d-n)

6 Solar Flare Index observed at day ‘d-2n’ FI(d-2n)

7 Solar Flare Index First Difference (FD) FI(d) –FI(d-n)

8 Solar Flare Index Second Difference (SD) FD(d) – FD(d-n)

The output :

n days ahead forecast of the Solar Flare Index : FI(d+n)


Data Organisation

The data provided by KOERI are grouped in three sets:

Phase Data 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


KOERI FI data for selected time intervals


1. Introduction

2. KOERI-FI-1


4. Results

5. Conclusions


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


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


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 4.92 5.20 7.38 6.92

Cross Corr. Coeff. (x 10-2) 42 26 10 8


Variation of observed and 1 day ahead forecast FI in between 1998-2002


Variation of observed and 1 d ahead forecast FI in 10 Nov 2000 - 6 Sep 2001


Scatter diagram of observed and 1 d ahead forecast FI in between 1998-2002


Variation of observed and 3 days ahead forecast FI in between 1998-2002


Variation of observed and 3 d ahead forecast FI in 10 Nov 2000 - 6 Sep 2001












1. Introduction

2. KOERI-FI-1


4. Results

5. Conclusions


CONCLUSIONS

• Daily SpW related parameters observed during the time periods including the maxima of the 21st, 22nd and 23rd solar cycles were considered

• FI values have been forecast up to 27 hours in advance using the KOERI-FI-1


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


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


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


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


Acknowledgements

This work is partially supported by

• COST ES0803 Action

• COST 296 Action (MIERS) - TUBITAK-ÇAYDAG (105Y003)


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.

Atac, T.: 2009, What is Flare Index?, http://www.kandilli.boun.edu.tr/astronomy/findex.htm, 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, 16-17 June. 2005, Cesme, Izmir, Turkey, 286-293; Lect. Notes Artif. Int., 3949, 84-91.

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. 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.

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, 669-674.

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), 3945-3954.

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