fourth european space weather week 5-9 nov . 2007 tec f orecasting d uring d isturbed
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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 - PowerPoint PPT PresentationTRANSCRIPT
Fourth European Space Weather Week5-9 Nov. 2007
TEC FORECASTING DURING DISTURBED
SPACE WEATHER CONDITIONS:
A POSSIBLE ALTERNATIVE TO THE IRI-2001
Yurdanur Tulunay1, Erdem Turker Senalp2, Ersin Tulunay2
ODTU / METU Ankara, TURKEY
(1) Dept. of Aerospace Eng., [email protected]
(2) Dept. of Electrical and Electronics Eng.
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CONTENTS
1. Introduction
2. METU-NN-C
3. Data Organisation
4. Results
5. Conclusions
6. Acknowledgements
7. References
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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
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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?
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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
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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]
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Fig. 1. Construction of the METU-NN-C Models[Senalp et al., 2007]
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1
2
3
Block 1: METU-NN model estimates the state-like variables for the METU-C
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k : Discrete time index
uDp(k) : Inputs
xDq(k) : the internal variables of the METU-C
Block 2: Construction of Nonlinear Static Block of METU-C
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Block 3: Construction of Linear Dynamic Block of METU-C
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The Generic METU-NN-C Model
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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
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
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RESULTS
Fig. 2 Observed and one hour ahead Forecast Hailsham TEC values for April, May 2002 [Senalp et al., 2007]
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Fig. 3. METU-NN-C and IRI-2001 during disturbed conditions (Hailsham)
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- Fig. 4. Scatter diagrams and best-fit lines: in 18-19 April 2002 at Hailsham
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METU-NN-C IRI-2001
Table 1. Performance of models
(18-19 April 2002; Hailsham)
METU-NN-C IRI-2001Normalized Error (%) 20.04 204.1
Cross Correlation Coefficient (x10-2) 98.7 83.8
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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
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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
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References
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