Skip to main content

Comparison Between UKMtrapcast and SPENVIS in Forecasting Distribution of High Energy Protons in the SAA Region

  • Chapter
  • First Online:
Space Science and Communication for Sustainability

Abstract

The distribution of high energy particles in the South Atlantic Anomaly (SAA) region was examined. This study attempted to compare the results between UKMtrapcast and the Space Environment Information System (SPENVIS) in forecasting the distribution of high energy protons in the SAA during severe and quiet periods. Results showed that the accuracy of UKMtrapcast was around 80–90%. The maps of UKMtrapcast also indicated that during the quiet period, the flux value tended to increase and vice versa, and this phenomenon was in line with National Oceanic and Atmospheric Administration (NOAA) observations. In other words, the UKMtrapcast could perform dynamic forecasting. On the other hand, the results of SPENVIS showed a similar pattern for all particles in all periods with an inappropriate position of SAA core. These findings indicated a positive contribution opportunity for UKMtrapcast to study the Earth’s space radiation particles.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baker DN, Blake JB (2013) SAMPEX: a long-serving radiation belt sentinel. Dynamics of the Earth’s radiation belts and inner magnetosphere. Geophys Monogr Ser:21–40. doi:10.1029/2012gm001368

  2. Shao X, Papadopoulos K, Sharma AS (2009) Control of the energetic proton flux in the inner radiation belt by artificial means. J Geophys Res-Space. doi:10.1029/2009ja014066

  3. Štěpánek P, Douša J, Filler V (2013) SPOT-5 DORIS oscillator instability due to South Atlantic Anomaly: mapping the effect and application of data corrective model. Adv Space Res 52:1355–1365. doi:10.1016/j.asr.2013.07.010

    Article  Google Scholar 

  4. Koshiishi H (2014) Space radiation environment in low earth orbit during influences from solar and geomagnetic events in December 2006. Adv Space Res 53:233–236. doi:10.1016/j.asr.2013.11.014

    Article  Google Scholar 

  5. Casadio S, Arino O (2011) Monitoring the South Atlantic Anomaly using ATSR instrument series. Adv Space Res 48:1056–1066. doi:10.1016/j.asr.2011.05.014

    Article  Google Scholar 

  6. Vainio R, Desorgher L, Heynderickx D et al (2009) Dynamics of the Earth’s particle radiation environment. Space Sci Rev 147:187–231. doi:10.1007/s11214-009-9496-7

    Article  Google Scholar 

  7. Johnson-Freese J (2007) Space as a strategic asset. Columbia University Press, New York

    Book  Google Scholar 

  8. Gusrizal (2015) The Development of LEO—NEqO trapped particles forecasting system. Universiti Kebangsaan Malaysia, Thesis

    Google Scholar 

  9. Banerjee S, Carlin BP, Gelfand AE (2015) Hierarchical modeling and analysis for spatial data. CRC Press, Taylor & Francis Group, Boca Raton

    Google Scholar 

  10. Cressie N (2015) Statistics for spatial data, revised edition. Wiley, Hoboken

    Google Scholar 

  11. Heynderickx D, Quaghebeur B, Wera J, et al (2004) New radiation environment and effects models in the European Space Agency’s Space Environment Information System (SPENVIS). Space Weather. doi:10.1029/2004sw000073

  12. Yando K, Millan RM, Green JC, Evans DS (2011) A Monte Carlo simulation of the NOAA POES medium energy proton and electron detector instrument. J Geophys Res-Space. doi:10.1029/2011ja016671

  13. O’brien TP (2009) SEAES-GEO: a spacecraft environmental anomalies expert system for geosynchronous orbit. Space Weather. doi:10.1029/2009sw000473

  14. Bakar KS, Sahu SK (2015) spTimer: spatio-temporal Bayesian modeling using R. J Stat Softw. doi:10.18637/jss.v063.i15

    Google Scholar 

  15. Nychka D, Furrer R, Paige J, Sain S (2015) Fields: tools for spatial data. doi:10.5065/D6W957CT

  16. Suparta W, Gusrizal (2014) The application of a hierarchical Bayesian spatiotemporal model for forecasting the SAA trapped particle flux distribution. J Earth Syst Sci 123:1287–1294. doi:10.1007/s12040-014-0459-3

    Article  Google Scholar 

  17. Suparta W, Gusrizal, Kudela K, Isa Z (2016) A hierarchical Bayesian spatio-temporal model to forecast trapped particle fluxes over the SAA region. Terr Atmos Ocean Sci. doi:10.3319/TAO.2016.02.25.01(AA)

  18. Bakar KS (2012) Bayesian analysis of daily maximum ozone levels. Dissertation, University of Southampton

    Google Scholar 

  19. Kuznetsov NV, Nikolaeva NI, Panasyuk MI (2010) Variation of the trapped proton flux in the inner radiation belt of the earth as a function of solar activity. Cosm Res 48:80–85. doi:10.1134/s0010952510010065

    Article  Google Scholar 

  20. Qin M, Zhang X, Ni B et al (2014) Solar cycle variations of trapped proton flux in the inner radiation belt. J Geophys Res-Space 119:9658–9669. doi:10.1002/2014ja020300

    Article  Google Scholar 

  21. Zou H, Zong QG, Parks GK et al (2011) Response of high-energy protons of the inner radiation belt to large magnetic storms. J Geophys Res-Space. doi:10.1029/2011ja016733

  22. Evans D, Garrett H, Jun I et al (2008) Long-term observations of the trapped high-energy proton population (L < 4) by the NOAA Polar Orbiting Environmental Satellites (POES). Adv Space Res 41:1261–1268. doi:10.1016/j.asr.2007.11.028

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by the Ministry of Science, Technology and Innovation (MOSTI), Malaysia with grant code 06-01-02-SF0808 and from the Ministry of Education (MoE), Malaysia with grant code FRGS/2/2013/SG02/UKM/02/3. Our gratitude is also addressed to the National Oceanic and Atmospheric Administration (NOAA), United States (US) for providing data to be used in the UKMtrapcast system, and European Space Agency (ESA) for their development of the online tool, the Space Environment Information System (SPENVIS). K. Kudela wishes to acknowledge support of APVV agency project APVV-15-0194, and support from OP RDE, MEYS, Czech Republic under the project CRREAT, CZ.02.1.01/0.0/0.0/15_003/0000481.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Gusrizal or Wayan Suparta .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gusrizal, Suparta, W., Kudela, K. (2018). Comparison Between UKMtrapcast and SPENVIS in Forecasting Distribution of High Energy Protons in the SAA Region. In: Suparta, W., Abdullah, M., Ismail, M. (eds) Space Science and Communication for Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-10-6574-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6574-3_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6573-6

  • Online ISBN: 978-981-10-6574-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics