Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T15:56:47.726Z Has data issue: false hasContentIssue false
  • English
  • Français

Data Handling and Assimilation for Solar Event Prediction

Published online by Cambridge University Press:  24 July 2018

Petrus C. Martens  and
Rafal A. Angryk
Petrus C. Martens
Affiliation:
Department of Physics & Astronomy, Georgia State University, 25 Park Place, 6th Floor, Atlanta, GA 30303, USA email: martens@astro.gsu.edu
Rafal A. Angryk
Affiliation:
Department of Computer Science, Georgia State University, 25 Park Place, 7th Floor, Atlanta, GA 30303, USA email: angryk@cs.gsu.edu
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The prediction of solar flares, eruptions, and high energy particle storms is of great societal importance. The data mining approach to forecasting has been shown to be very promising. Benchmark datasets are a key element in the further development of data-driven forecasting. With one or more benchmark data sets established, judicious use of both the data themselves and the selection of prediction algorithms is key to developing a high quality and robust method for the prediction of geo-effective solar activity. We review here briefly the process of generating benchmark datasets and developing prediction algorithms.

Keywords

Astronomical data bases: miscellaneousSun: flarescatalogs
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 13 , Symposium S335: Space Weather of the Heliosphere: Processes and Forecasts , July 2017 , pp. 344 - 347
Copyright
Copyright © International Astronomical Union 2018 

References

Barnes, G. & Leka, K. D. 2008, ApJ Letters, 688, L107CrossRefGoogle Scholar
Bobra, M. G. & Couvidat, S. 2015, ApJ, 798, 135CrossRefGoogle Scholar
Bramer, M. 2013, Principles of Data Mining (Undergraduate Topics in Computer Science), Second Edition (Secaucus, NJ, USA: Springer-Verlag New York, Inc.)CrossRefGoogle Scholar
Falconer, D. A., Moore, R. L., Barghouty, A. F. & Khazanov, I. 2014, Space Weather, 12, 306, 2013SW001024CrossRefGoogle Scholar
Filali Boubrahimi, S., Aydin, B., Martens, P. & Angryk, R. 2017, IEEE Proceedings, pressGoogle Scholar
Hurlburt, N., Cheung, M., Schrijver, C., et al. 2012, Sol. Phys., 275, 67CrossRefGoogle Scholar
Kucuk, A., Banda, J. M. & Angryk, R. A. 2017, Nature: Scientific Data, 4, data DescriptorGoogle Scholar
Lemen, J. R., Title, A. M., Akin, D. J., et al. 2012, Sol. Phys., 275, 17CrossRefGoogle Scholar
Martens, P. C. H., Attrill, G. D. R., Davey, A. R., et al. 2012, Sol. Phys., 275, 79CrossRefGoogle Scholar
Mason, J. P. & Hoeksema, J. T. 2010, ApJ, 723, 634CrossRefGoogle Scholar
Scherrer, P. H., Schou, J., Bush, R. I., et al. 2012, Sol. Phys., 275, 207CrossRefGoogle Scholar
Schuh, M. A., Angryk, R. A. & Martens, P. C. 2015, Astronomy and Computing, 13, 86CrossRefGoogle Scholar
Schuh, M. A., Angryk, R. A. & Martens, P. C. 2016, Journal of Space Weather and Space Climate, 6, A22CrossRefGoogle Scholar