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VTEC prediction using a recursive artificial neural networks approach in Brazil: initial results

Engineer School - University of São Paulo. VTEC prediction using a recursive artificial neural networks approach in Brazil: initial results. Wagner Carrupt Machado Edvaldo Simões da Fonseca Junior. MImOSA workshop – february 26th 2013 – INPE - São José dos Campos - Brazil.

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VTEC prediction using a recursive artificial neural networks approach in Brazil: initial results

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  1. Engineer School - University of São Paulo VTEC prediction using a recursive artificial neural networks approach in Brazil: initial results Wagner Carrupt Machado Edvaldo Simões da Fonseca Junior MImOSA workshop – february 26th 2013 – INPE - São José dos Campos - Brazil

  2. Presentation outline • IBGE interest, infrastructure and needs; • Artificial Neural Networks approach; • Experiments andResults: • Solar activity and geomagnetic field status; • Data processing and results; • Conclusion and future work. 2

  3. IBGE and Ionosphere GNSS positioning. (X,Y,Z)

  4. Ionospheric delay • First-order delay - More than 99% - Proportional to TEC carrier-phase pseudorange

  5. RBMC • Since 1996; • Actually 88 stations; • Needs densification.

  6. RBMC-IP • Real time GNSS data stream on internet (NTRIP – Networked Transport of RTCM via Internet Protocol); • Since 2009; • Actually 28 stations.

  7. On-line PPP service • Double or single frequency data processing; • Global Ionospheric Maps (IONEX) applied to single frequency solutions;

  8. IGS Global Ionospheric Maps • Combination of four different solutions: • CODE (Center for Orbit Determination in Europe); • ESOC (European Space Operations Centre ESA); • UPC (Polytechnical University of Catalonia); • JPL (Jet Propulsion Laboratory).

  9. IBGE collaborations • Providing GNSS data free of charge to ionosphere monitoring projects: • Unesp - Presidente Prudente (Brazil); • INPE/EMBRACE (Brazil); • La Plata University (Argentina); • IGS – currently 9 stations (international). 9

  10. ANN approach • Architecture: • Multilayer Perceptrons; • 1 hidden layer with 16 neurons. • Recursive training: • Updated daily. • samples taken from 3 previous days IGS GIM, resulting in 39 grids with 276 points (10,764 samples) • Output: • 72 hours ahead of regional ionospheric Maps (IONEX).

  11. Experiments • 30 ANNs trained; • Comparison between VTECGIM and VTECANN in four cases: • IGS GIM high: March 21 to April 04 2001 (Day 80 to 94) low: June 16 to june 30 2009 (Day 167 to 181) 1) high solar activity; 2) day of the geomagnetic storm; 3) 3 days after the day of the geomagnetic storm; 4) low solar activity.

  12. Solar activity status Solar Flux 10.7 cm (NOAA - Pentiction station) • High solar activity: • from 139.8 sfu to 273.5 sfu • Low solar activity: • from 66.5 sfu to 68.5 sfu

  13. Geomagnetic field status Dst index (Kyoto) • High solar activity: • Day 90 => -400nT. • Low solar activity: • less than -50 nT.

  14. Differences Increases as approaching to daily VTEC maximum; High solar activity • < 15 TECU: • 86% - geomagnetic storm; • 88% - not disturbed days. Low solar activity: • < 5 TECU: • 99%.

  15. Relative differences () • from 0% to 20% during most of the time in both periods; • Day 90 (case 2) between 2 h and 5 h (Local Time) VTECGIM were pushed down due to the geomagnetic storm;

  16. Conclusion • 70% to 85% of VTECGIM was correctly mapped by the ANN; • Vertical ionospheric delay from 0.24 m to 1.79 m can be expected in L1 observables; • Insufficient for high precision applications (ambiguity resolution); • The proposed approach: • auto-adaptive to seasonal and longer period variations; • real-time GNSS positioning;

  17. Future work • Mod_Ion regional ionospheric maps with spacial resolution of 2° x 4° and 1 hour frequency; • Extend the model coverage to South America; • Use data from the actual solar cycle maximum; • Include solar activity and geomagnetic indices in the model.

  18. Acknowledgments

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