ICTP-COST-USNSWP-CAWSES-INAF-INFN International Advanced School on Space Weather
2-19 May 2006, Trieste, Italy
FORECASTING TOTAL ELECTRON CONTENT (TEC) FOR CONSTRUCTING
MAPS ESPECIALLY DURING HIGH SOLAR ACTIVITY PERIODS
Erdem Turker Senalp(1), Ersin Tulunay(1), Sandro Maria Radicella(2),
Yurdanur Tulunay(3)
(1)Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara,
Turkey, [email protected], [email protected]
(2)Aeronomy and Radiopropagation Laboratory, Abdus Salam International Centre for Theoretical
Physics, Trieste, Italy, [email protected]
(3)Department of Aerospace Engineering, Middle East Technical University, Ankara, Turkey,
[email protected]
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Contents
- Biological to Artificial Neurons
- Middle East Technical University Neural Network model
(METU NN)
- METU NN Forecasting TEC Maps
- Results
- Conclusions
- References
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Biological to Artificial Neurons
Halici, 2006
Halici, 2006
METU-NN
Tulunay, 2004
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Middle East Technical University Neural Network model (METU NN)
METU NN Forecasting TEC Maps [1]
- power of forecasting non-linear process parameters
- forecast TEC values and the maps [1]
- Observed GPS TEC data [Ciraolo, 2004] :
- Ten minute vertical TEC data
between 1 Nov. 2003 and 11 Dec. 2003
- 104 grid locations centered over Italy
between latitudes of (35.5º N; 47.5º N) and
longitudes of (5.5º E; 19.5º E)
[1] Tulunay E., Senalp E.T., Radicella S.M., Tulunay Y., Forecasting TEC Maps by
Neural Network Technique, Radio Science, submitted in 2005, (2006).
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METU NN technique
- to forecast TEC grid values
- surfaces in mapping the forecast grid values
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Results
-
Figure 1. Observed (dotted) and 1 hour ahead forecast (solid) TEC during
16 Nov. 2003 03:10 UT - 29 Nov. 2003 24:00 UT for single grid point (13.5˚ E;
41.5˚ N)
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- Figure 2. Scatter Diagram (dots) with best-fit line (solid) for the 1 hour ahead
Forecast mapping and Observed TEC values for single grid point (13.5˚ E;
41.5˚ N) for 20 November 2003
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METU NN technique
- to forecast TEC grid values
- surfaces in mapping the forecast grid values
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-
Video 1. Observed and 1 hour ahead forecast TEC Maps by METU-NN
during the afternoon of 20 Nov. 2003
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- Figure 3. Absolute Error Map of observed and 1 hour ahead forecast TEC
during 16-29 Nov. 2003
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Table 1. Error Table for 1 h in advance forecasts by METU-NN for the
validation time period (16-29 Nov. 2003)
11.5˚E
38.5˚N
13.5˚E
41.5˚N
15.5˚E
44.5˚N
Overall
TEC Map
Root Mean Square Error
(TECU)
2.1556
2.0501
2.1444
2.2969
Absolute Error (TECU)
1.5785
1.4846
1.5168
1.6537
Normalized Error
0.1429
0.1522
0.1630
0.1563
Cross Correlation Coefficient
0.9775
0.9746
0.9696
0.9699
Location
The forecast mapping error values - within operational tolerance
[Radicella, 2004].
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Conclusions
- The system reached correct operating point during
training
- METUNN learned the inherent nonlinearities of the
process
- Representable data in inputs
+
Proper construction of METUNN
=
complex nonlinear processes are modelled
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References
Tulunay E., Senalp E.T., Radicella S.M., Tulunay Y. (2006), Forecasting TEC Maps by Neural Network
Technique, Radio Science, (submitted in 2005).
Ciraolo, G. (2004), Private communication.
Radicella S.M. (2004), Private communication.
Tulunay, E. (1991), Introduction to Neural Networks and their Application to Process Control, in Neural
Networks Advances and Applications, edited by E. Gelenbe, 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 (2004),
Development of algorithms and software for forecasting, nowcasting and variability of TEC, Annals of
Geophysics, 47(2/3), 1201-1214.
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 Science, 36(5), 1027-1041, September - October 2001.
Tulunay Y., E. Tulunay, and E.T. Senalp (2004-a), The Neural Network Technique-1: A General Exposition, Adv.
Space Res., 33(6), 983-987.
Tulunay Y., E. Tulunay, and E.T. Senalp (2004-b), The Neural Network Technique-2: An Ionospheric Example
Illustrating its Application, Adv. Space Res., 33(6), 988-992.
Halici, U. (2006), Artificial Neural Networks, Lecture Notes, Ch1, METU EEE, Ankara, Turkey, 3, 8.
“http://vision1.eee.metu.edu.tr/%7Ehalici/543LectureNotes/lecturenotes-pdf/ch1.pdf”.
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