Open Access
Issue
J. Space Weather Space Clim.
Volume 7, 2017
Article Number A29
Number of page(s) 12
DOI https://doi.org/10.1051/swsc/2017027
Published online 22 November 2017
  • Arber T. 2016. Propagation of the solar wind from the Sun to L1. In: 13th European Space Weather Week, Oostende, Belgium. [Google Scholar]
  • Arber T, Longbottom AW, Gerrard CL, Milne AM. 2001. A staggered grid, Lagrangian-Eulerian remap code for 3-D MHD simulations. J Comput Phys 171 (1): 151–181. [Google Scholar]
  • Ayala Solares JR, Wei H-L, Boynton RJ, Walker SN, Billings SA. 2016. Modelling and prediction of global magnetic disturbance in near-Earth space: a case study for Kp index using NARX models. Space Weather 14 (10): 899–916. [CrossRef] [Google Scholar]
  • Bala R, Reiff P. 2012. Improvements in short-term forecasting of geomagnetic activity. Space Weather 10: S06001. DOI: 10.1029/2012SW000779. [CrossRef] [Google Scholar]
  • Bala R, Reiff P. 2014. Validating the Rice neural network and the Wing Kp real-time models. Space Weather 12: 417–425. DOI: 10.1002/2014SW001075. [CrossRef] [Google Scholar]
  • Bartels J, Heck NH, Johnston F. 1939. The three-hour-range index measuring geomagnetic activity. J Geophys Res 44: 411–454. DOI: 10.1029/TE044i004p00411. [Google Scholar]
  • Boberg F, Wintoft P, Lundstedt H. 2000. Real time Kp predictions from solar wind data using neural networks. Phys Chem Earth C: Solar Terr Planet Sci 25: 275–280. [Google Scholar]
  • Borovsky JE, Birn J. 2014. The solar wind electric field does not control the dayside reconnection rate. J Geophys Res: Space Phys 119: 751–760. DOI: 10.1002/2013JA019193. [CrossRef] [Google Scholar]
  • Brautigam D, Albert J. 2000. Radial diffusion analysis of outer radiation belt electrons during the October 9, 1990, magnetic storm. J Geophys Res: Space Phys 105 (A1): 291–309. [CrossRef] [Google Scholar]
  • Bruinsma S 2015. The DTM-2013 thermosphere model. J Space Weather Space Clim 5: A1. DOI: 10.1051/swsc/2015001. [Google Scholar]
  • Chollet F. 2017. Keras: deep learning for Python. URL: https://github.com/fchollet/keras. [Google Scholar]
  • Cliver EW, Kamide Y, Ling AG, Yokoyama N. 2001. Semiannual variation of the geomagnetic Dst index: evidence for a dominant nonstorm component. J Geophys Res 106(A10): 21297–21304. [CrossRef] [Google Scholar]
  • Cybenko G. 1989. Approximation by superposition of a sigmoidal function. Math Control Signals Syst 2: 303–314. [Google Scholar]
  • Heilig B, Lühr H. 2013. New plasmapause model derived from CHAMP field-aligned current signatures. Ann Geophys 31: 529–539. DOI: 10.5194/angeo-31-529-2013. [CrossRef] [Google Scholar]
  • Hunter JD 2007. Matplotlib: a 2D graphics environment. Comput Sci Eng 9: 90–95. [Google Scholar]
  • Ji EY, Moon YJ, Park J, Lee JY, Lee DH. 2013. Comparison of neural network and support vector machine methods for Kp forecasting. J Geophys Res 118: 5109–5117. DOI: 10.1002/jgra.50500. [CrossRef] [Google Scholar]
  • Lundstedt H, Gleisner H, Wintoft P. 2002. Operational forecasts of the geomagnetic Dst index. Geophys Res Lett 29(24): 34-1–34-4. DOI: 10.1029/2002GL016151. [CrossRef] [Google Scholar]
  • Mailyan B, Munteanu C, Haaland S. 2008. What is the best method to calculate the solar wind propagation delay? Ann Geophys 26: 2383–2394. [CrossRef] [Google Scholar]
  • Mayaud PN. 1980. Derivation, meaning, and use of geomagnetic indices. Geophysical monograph, Vol. 22. American Geophysical Union. [Google Scholar]
  • McComas DJ, Bame SJ, Barker P, Feldman WC, Phillips JL, Riley P, Griffee JW. 1998. Solar wind electron proton alpha monitor (SWEPAM) for the Advanced Composition Explorer. Space Sci Rev 86: 563–612 [Google Scholar]
  • McKinney W. 2010. Data structures for statistical computing in Python. In van der Walt S, Millman J, eds. Proceedings of the 9th Python in Science Conference, pp. 51–56. [Google Scholar]
  • Menvielle M, Papitashvili N, Häkkinen L, Sucksdorff C. 1995. Computer production of K indices: review and comparison of methods. Geophys J Int 123: 866–886. [CrossRef] [Google Scholar]
  • Murphy AH. 1993. What is a good forecast? An essay on the nature of goodness in weather forecasting. Am Meteorol Soc 8: 281–293. [Google Scholar]
  • Murphy K, Mann I, Rae I, Sibeck D, Watt C. 2016. Accurately characterizing the importance of wave-particle interactions in radiation belt dynamics: the pitfalls of statistical wave representations. J Geophys Res: Space Phys 121 (8): 7895–7899. [CrossRef] [Google Scholar]
  • Odstrcil D. 2003. Modeling 3-D solar wind structure. Adv Space Res 32: 497–506. DOI: 10.1016/S0273-1177(03)00332-6. [NASA ADS] [CrossRef] [Google Scholar]
  • Orlova K, Shprits Y. 2014. Model of lifetimes of the outer radiation belt electrons in a realistic magnetic field using realistic chorus wave parameters. J Geophys Res: Space Phys 119 (2): 770–780. [CrossRef] [Google Scholar]
  • Orlova K, Shprits Y, Spasojevic M. 2016. New global loss model of energetic and relativistic electrons based on Van Allen Probes measurements. J Geophys Res: Space Phys 121 (2): 1308–1314. [CrossRef] [Google Scholar]
  • Pulkkinen TI, Palmroth M, Tanskanen EI, Ganushkina NY, Shukhtina MA, Dmitrieva NP. 2007. Solar wind-magnetosphere coupling: a review of recent results. J Atmos Solar-Terr Phys 69: 256. [CrossRef] [Google Scholar]
  • Qian L, Burns AG, Emery BA, Foster B, Lu G, Maute A, Richmond AD, Roble RG, Solomon SC, Wang W. 2014. The NCAR TIE-GCM. In Huba J, Schunk R, Khazanov G, eds. Modeling the ionosphere-thermosphere system. Geophysical monograph series, Vol. 201. John Wiley & Sons Ltd., Chap. 7. DOI: 10.1002/9781118704417.ch7. [Google Scholar]
  • Segarra A, Nosé M, Curto JJ, Araki T. 2015. Multipoint observation of the response of the magnetosphere and ionosphere related to the sudden impulse event on 19 November 2007. Space Weather Space Climate 5: A13. DOI: 10.1051/swsc/2015016. [CrossRef] [EDP Sciences] [Google Scholar]
  • Shprits Y, Thorne R, Friedel R, Reeves G, Fennell J, Baker D, Kanekal S. 2006. Outward radial diffusion driven by losses at magnetopause. J Geophys Res: Space Phys 111 (A11). [Google Scholar]
  • Shprits Y, Thorne R, Reeves G, Friedel R. 2005. Radial diffusion modeling with empirical lifetimes: comparison with CRRES observations. Ann Geophys 23: 1467–1471. [CrossRef] [Google Scholar]
  • Smith CW, L'Heureux J, Ness NF, Acũna MH, Burlaga LF, Scheifele J. 1998. The ACE magnetic fields experiment. Space Sci Rev 86: 611. [Google Scholar]
  • Stone E, Frandsen A, Mewaldt R, Christian E, Margolies D, Ormes J, Snow F. 1998. The advanced composition explorer. Space Sci Rev 86: 1–22. DOI: 10.1023/A:1005082526237. [NASA ADS] [CrossRef] [Google Scholar]
  • Thomsen MF. 2004. Why Kp is such a good measure of magnetospheric convection. Space Weather 2: S11004. DOI: 10.1029/2004SW000089. [CrossRef] [Google Scholar]
  • Tsyganenko N. 1989. A magnetospheric magnetic field model with a warped tail current sheet. Planet Space Sci 37 (1): 5–20. [NASA ADS] [CrossRef] [Google Scholar]
  • Tsyganenko N, Sitnov M. 2005. Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms. J Geophys Res: Space Phys 110(A3): A03208. [CrossRef] [Google Scholar]
  • Tsyganenko N, Sitnov M. 2007. Magnetospheric configurations from a high-resolution data-based magnetic field model. J Geophys Res: Space Phys 112(A6): A06225. [CrossRef] [Google Scholar]
  • Tu W, Cunningham G, Chen Y, Henderson M, Camporeale E, Reeves G. 2013. Modeling radiation belt electron dynamics during GEM challenge intervals with the DREAM3D diffusion model. J Geophys Res: Space Phys 118 (10): 6197–6211. [CrossRef] [Google Scholar]
  • van der Holst B, Sokolov IV, Meng X, Jin M, Manchester WB, Toth G, Gombosi TI. 2014. Alfven Wave Solar Model (AWSoM): coronal heating. Astrophys J 728(2): 81. DOI:10.1088/0004-637X/782/2/81. [CrossRef] [Google Scholar]
  • Viñas AF, Scudder JD. 1986. Fast and optimal solution to the “Rankine-Hugoniot Problem”. J Geophys Res 91(A1): 39–58. [NASA ADS] [CrossRef] [Google Scholar]
  • Wang J, Zhong Q, Liu S, Miao J, Liu F, Li Z, Tang W. 2015. Statistical analysis and verification of 3-hourly geomagnetic activity probability predictions. Space Weather 13: 831–852. DOI: 10.1002/2015SW001251. [CrossRef] [Google Scholar]
  • Wing S, Johnson JR, Jen J, Meng CI, Sibeck DG, Bechtold K, Freeman J, Costello K, Balikhin M, Takashi K. 2005. Kp forecast models. J Geophys Res 110: A04203. DOI: 10.1029/2004JA010500. [CrossRef] [Google Scholar]
  • Wintoft P, Wik M, Viljanen A. 2015. Solar wind driven empirical forecast models of the time derivative of the ground magnetic field. J Space Weather Space Clim 5: A7. DOI: 10.1051/swsc/2015008. [CrossRef] [EDP Sciences] [Google Scholar]
  • Zhang Y, Paxton LJ. 2008. An empirical Kp-dependent global auroral model based on TIMED/GUVI FUV data. J Atmos Solar-Terr Phys 70 (8–9): 1231–1242. DOI: 10.1016/j.jastp.2008.03.008. [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.