J. Space Weather Space Clim.
Volume 9, 2019
System Science: Application to Space Weather Analysis, Modelling, and Forecasting
Article Number A13
Number of page(s) 11
Published online 08 May 2019
  • Anderson JA. 1995. An introduction to neural networks. MIT Press, Cambridge, MA. [Google Scholar]
  • Balasis G, Daglis IA, Georgiou M, Papadimitriou C, Haagmans R. 2013. Magnetospheric ULF wave studies in the frame of Swarm mission: a time-frequency analysis tool for automated detection of pulsations in magnetic and electric field observations. Earth Planets Space 65(11): 1385–1398. [CrossRef] [Google Scholar]
  • Balasis G, Papadimitriou C, Daglis IA, Pilipenko V. 2015. ULF wave power features in the topside ionosphere revealed by Swarm observations. Geophys Res Lett 42: 6922–6930. DOI:10.1002/2015GL065424. [CrossRef] [Google Scholar]
  • Bishop C. 1995. Neural networks for pattern recognition. Oxford University Press, Oxford. [Google Scholar]
  • Bogoutdinov ShR, Yagova NV, Pilipenko VA, Agayan SM. 2018. A technique for detection of ULF Pc3 waves and their statistical analysis. Russ J Earth Sci 18: ES6006. DOI:10.2205/2018ES000646. [Google Scholar]
  • Bortnik J, Chu X, Ma Q, Li W, Zhang X, et al. 2018. Artificial neural networks for determining magnetospheric conditions. In: Machine learning techniques for space weather. Camporeale E, Wing S, Johnson J. (Eds.), Elsevier, Amsterdam, The Netherlands, pp. 279–300. [CrossRef] [Google Scholar]
  • Boynton RJ, Balikhin MA, Sibeck DG, Walker SN, Billings SA, Ganushkina N. 2016. Electron flux models for different energies at geostationary orbit. Space Weather 14: 846–860. DOI: 10.1002/2016SW001506. [CrossRef] [Google Scholar]
  • Burton RK, McPherron RL, Russell CT. 1975. An empirical relationship between interplanetary conditions and Dst. J Geophys Res 80: 4204–4214. [Google Scholar]
  • Camporeale E, Wing S, Johnson J. 2018. Machine Learning Techniques for Space Weather. Elsevier, Amsterdam, The Netherlands 454 p. DOI:10.1016/C2016-0-01976-9. [Google Scholar]
  • Daglis IA. 2006. Ring current dynamics. Space Sci Rev 124: 183–202. DOI:10.1007/s11214-006-9104-z. [CrossRef] [Google Scholar]
  • Falconer K. 1990. Fractal geometry: mathematical foundations and applications. John Wiley, Chichester, pp. 38–47. ISBN 0-471-92287-0. [Google Scholar]
  • Heidke P. 1926. Berechnung des Erfolges und der Güte der Windstärkevorhersagen im Sturmwarnungsdienst. Geografiska Annaler 8: 301–349. DOI: 10.2307/519729. [Google Scholar]
  • Lundstedt H, Gleisner H, Wintoft P. 2002. Operational forecasts of the geomagnetic Dst index. Geophys Res Lett 29(24): 2181. DOI: 10.1029/2002GL016151. [Google Scholar]
  • Mann IR. 2016. Waves, particles, and storms in geospace: an introduction. In: Waves, particles, and storms in geospace. Balasis G, Daglis IA, Mann IR (Eds.), Oxford University Press, Oxford, pp.1–14. [Google Scholar]
  • O’Brien TP, McPherron RL. 2000. Forecasting the ring current index Dst in real time. J Atmos Terr Phys 62: 1295–1299. [CrossRef] [Google Scholar]
  • Pallocchia G, Amata E, Consolini G, Marcucci MF, Bertello I. 2006. Geomagnetic Dst index forecast based on IMF data only. Ann Geophys 24: 989–999. DOI: 10.5194/angeo-24-989-2006. [CrossRef] [Google Scholar]
  • Papadimitriou C, Balasis G, Daglis IA, Giannakis O. 2018. An initial ULF wave index derived from two years of Swarm observations. Ann Geophys 36: 287–299. DOI: 10.5194/angeo-36-287-2018. [CrossRef] [Google Scholar]
  • Park J, Noja M, Stolle C, Lühr H. 2013. The Ionospheric Bubble Index deduced from magnetic field and plasma observations onboard Swarm. Earth Planets Space 65: 1333–1344. [CrossRef] [Google Scholar]
  • Reed R, Marks RJ. 1999. Neural smithing: supervised learning in feedforward artificial neural networks. MIT Press, Cambridge, MA. [CrossRef] [Google Scholar]
  • Reigber C, Lühr H, Schwintzer P, Wickert J (Eds.). 2005. Earth observation with CHAMP: results from three years in orbit. Springer, Berlin. 628 p. [Google Scholar]
  • Ritter P, Lühr H, Rauberg J. 2013. Determining field-aligned currents with the Swarm constellation mission. Earth Planets Space 65: 1285–1294. [CrossRef] [Google Scholar]
  • Shing J, Jang R. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. [Google Scholar]
  • Sugeno M. 1985. Industrial applications of fuzzy control. Elsevier Science Pub Co., Japan. [Google Scholar]
  • Souza VM, Medeiros C, Koga D, Alves LR, Vieira LEA, Dal Lago A, Da Silva LA, Jauer PR, Baker DN. 2018. Classification of magnetospheric particle distributions via neural networks. In: Machine learning techniques for space weather. Camporeale E, Wing S, Johnson J (Eds.), Elsevier, Amsterdam, The Netherlands, pp. 329–353. [CrossRef] [Google Scholar]
  • Stolle C, Lühr H, Rother M, Balasis G. 2006. Magnetic signatures of equatorial spread F as observed by the CHAMP satellite. J Geophys Res 111: A02304. DOI: 10.1029/2005JA011184. [Google Scholar]
  • Temerin M, Li X. 2002. A new model for the prediction of Dst on the basis of the solar wind. J Geophys Res 107(A12): 1472. DOI: 10.1029/2001JA007532. [CrossRef] [Google Scholar]
  • Temerin M, Li X. 2006. Dst model for 1995–2002. J Geophys Res 111: A04221. DOI:10.1029/2005JA011257. [CrossRef] [Google Scholar]
  • Wei HL, Zhu DQ, Billings SA, Balikhin M. 2007. Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks. Adv Space Res 40: 1863–1870. [CrossRef] [Google Scholar]
  • Williams AB, Taylors FJ. 1988. Electronic filter design handbook. McGraw-Hill, New York, USA. ISBN 0-07-070434-1. [Google Scholar]
  • Wing S, Johnson JR, Jen J, Meng C-I, Sibeck DG, et al. 2005. Kp forecast models. J Geophys Res Space Phys 110: A04203. DOI: 10.1029/2004JA010500. [Google Scholar]
  • Wintoft P, Wik M, Matzka J, Shprits Y. 2017. Forecasting Kp from solar wind data: input parameter study using 3-hour averages and 3-hour range values. J Space Weather Space Clim 7: A29. [CrossRef] [Google Scholar]
  • Zhelavskaya IS, Shprits YY, Spasojevic M. 2018. Reconstruction of plasma electron density from satellite measurements via artificial neural networks. In: Machine learning techniques for space weather. Camporeale E, Wing S, Johnson J (Eds.) Elsevier, Amsterdam, The Netherlands, pp. 301–327. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.