Open Access
Issue |
J. Space Weather Space Clim.
Volume 11, 2021
Topical Issue - Space Weather research in the Digital Age and across the full data lifecycle
|
|
---|---|---|
Article Number | 38 | |
Number of page(s) | 16 | |
DOI | https://doi.org/10.1051/swsc/2021021 | |
Published online | 30 June 2021 |
- Al-Shakarchi DA, Morgan H. 2018. Properties of the HPS-ICME-CIR interaction event of 9–10 September 2011. J Geophys Res (Space Phys) 123: 2535–2556. https://doi.org/10.1002/2017JA024849. [Google Scholar]
- Bhaskar A, Vichare G. 2019. Forecasting of SYMH and ASYH indices for geomagnetic storms of solar cycle 24 including St. Patrick’s day, 2015 storm using NARX neural network. J Space Weather Space Clim 9: A12. https://doi.org/10.1051/swsc/2019007. [Google Scholar]
- Bingham ST, Mouikis CG, Kistler LM, Boyd AJ, Paulson K, Farrugia CJ, Huang CL, Spence HE, Claudepierre SG, Kletzing C. 2018. The outer radiation belt response to the storm time development of seed electrons and chorus wave activity during CME and CIR driven storms. J Geophys Res 123: 10139–10157. https://doi.org/10.1029/2018JA025963. [Google Scholar]
- Birn J, Drake JF, Shay MA, Rogers BN, Denton RE, Hesse M, Kuznetsova M, Ma ZW, Bhattacharjee A, Otto A, Pritchett PL. 2001. Geospace environmental modeling (GEM) magnetic reconnection challenge. J Geophys Res 106(A3): 3715–3719. https://doi.org/10.1029/1999JA900449. [Google Scholar]
- Boyle CB, Reiff PH, Hairston MR. 1997. Empirical polar cap potentials. J Geophys Res 102(A1): 111–125. https://doi.org/10.1029/96JA01742. [Google Scholar]
- Burton RK, McPherron RL, Russell CT. 1975. An empirical relationship between interplanetary conditions and Dst. J Geophys Res 80: 4204–4214. https://doi.org/10.1029/JA080i031p04204. [Google Scholar]
- Daglis IA, Thorne RM, Baumjohann W, Orsini S. 1999. The terrestrial ring current: Origin, formation, and decay. Rev Geophys 37(4): 407–438. https://doi.org/10.1029/1999RG900009. [Google Scholar]
- Eastwood J, Nakamura R, Turc L, Mejnertsen L, Hesse M. 2017. The scientific foundations of forecasting magnetospheric space weather. Space Sci Rev 212(3–4): 1221–1252. https://doi.org/10.1007/s11214-017-0399-8. [CrossRef] [Google Scholar]
- Echer E, Gonzalez WD, Guarnieri FL, Dal Lago A, Vieira LEA. 2005. Introduction to space weather. Adv Space Res 35: 855–865. https://doi.org/10.1016/j.asr.2005.02.098. [CrossRef] [Google Scholar]
- Freeman J, Nagai A. 1993. The Magnetospheric Specification and Forecast Model: Moving from real time to prediction. In: Solar-Terrestrial Predictions IV, Proceeding of a Workshop at Ottawa, Canada, May 18–22, 1992, Vol 2, Hruska J, Shea MA, Smart DF, Heckman G, (Eds.), NOAA, Boulder. pp. 524–539. [Google Scholar]
- Feldstein YI. 1992. Modelling of the magnetic field of magnetospheric ring current as a function of interplanetary medium parameters. Space Sci Rev 59(1–2): 83–165. https://doi.org/10.1007/BF01262538. [Google Scholar]
- Gonzalez WD, Joselyn JA, Kamide Y, Kroehl HW, Rostoker G, Tsurutani BT, Vasyliunas VM. 1994. What is a geomagnetic storm? J Geophys Res 99(A4): 5771–5792. https://doi.org/10.1029/93JA02867. [NASA ADS] [CrossRef] [Google Scholar]
- Gardner M, Dorling S. 1998. Artificial neural networks (the multilayer perceptron) a review of applications in the atmospheric sciences. Atmos Environ 32(14): 2627–2636. https://doi.org/10.1016/S1352-2310(97)00447-0. [CrossRef] [Google Scholar]
- Gruet MA, Chandorkar M, Sicard A, Camporeale E. 2018. Multiple-hour-ahead forecast of the Dst index using a combination of long short-term memory neural network and gaussian process. Space Weather 16: 1882–1896. https://doi.org/10.1029/2018SW001898. [Google Scholar]
- Haykin S. 1998. Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ. ISBN 0132733501. [Google Scholar]
- Kugblenu S, Taguchi S, Okuzawa T. 1999. Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm. Earth Planet Space 51: 307–313. https://doi.org/10.1186/BF03352234. [CrossRef] [Google Scholar]
- Lazzús JA, Vega P, Rojas P, Salfate I. 2017. Forecasting the Dst index using a swarm-optimized neural network. Space Weather 15: 1068–1089. https://doi.org/10.1002/2017SW001608. [CrossRef] [Google Scholar]
- Lyons L. 1998. The geospace modeling program grand challenge. J Geophys Res 103(A7): 14781–14785. https://doi.org/10.1029/98JA00015. [Google Scholar]
- Lundstedt H, Wintoft P. 1994. Prediction of geomagnetic storms from solar wind data with the use of a neural network. Ann Geophys 12: 19. https://doi.org/10.1007/s00585-994-0019-2. [CrossRef] [Google Scholar]
- Lundstedt H, Gleisner H, Wintoft P. 2002. Operational forecasts of the geomagnetic Dst index. Geophys Res Lett 29: 2181. https://doi.org/10.1029/2002GL016151. [Google Scholar]
- Liemohn MW, Jazowski M, Kozyra JU, Ganushkina N, Thomsen MF, Borovsky JE. 2010. CIR versus CME drivers of the ring current during intense magnetic storms. Proc R Soc London A 466(2123): 3305–3328. https://doi.org/10.1098/rspa.2010.0075. [Google Scholar]
- Matamba TM, Habarulema JB. 2018. Ionospheric responses to CME- and CIR-driven geomagnetic storms along 30 E–40 E over the African sector from 2001 to 2015. Space Weather 16: 538–556. https://doi.org/10.1029/2017SW001754. [Google Scholar]
- Makarov GA. 2018. Heliolatitude regularities of magnetically disturbed days with daily average geomagnetic index Dst < −100 nT. Sol Terr Phys 4(3): 20–23. https://doi.org/10.12737/stp-43201803. [Google Scholar]
- McPherron RL, O’Brien TP. 2001. Predicting geomagnetic activity: The Dst index. In: Space Weather Geophys Monogr Ser, vol. 125. Song P, Singer HJ, Siscoe GL (Eds.), AGU, Washington, DC. pp. 339–345. https://doi.org/10.1029/GM125p0339. [Google Scholar]
- Murayama T. 1982. Coupling function between solar wind parameters and geomagnetic indices. Reviews of Geophysics 20(3): 623–629. https://doi.org/10.1029/RG020i003p00623. [Google Scholar]
- O’Brien TP, McPherron RL. 2000. Forecasting the ring current index Dst in real time. J Atmos Sol Terr Phys 62: 1295–1299. https://doi.org/10.1016/S1364-6826(00)00072-9. [Google Scholar]
- Ohtani S-I, Fujii R, Hesse M, Lysak RL. 2000. Magnetospheric current systems, vol 118. American Geophysical Union, Washington, DC. ISBN 9781118669006. [CrossRef] [Google Scholar]
- Raeder J, Maynard N. 2001. Foreword (to Special Section on Proton Precipitation Into the Atmosphere). J Geophys Res 106(A1): 345–348. https://doi.org/10.1029/2000JA000600. [Google Scholar]
- Rangarajan G. 1989. Indices of geomagnetic activity. Geomagnetism 3: 323–384. [Google Scholar]
- Rastätter L, Kuznetsova MM, Glocer A, Welling D, Meng X, Raeder J, et al. 2013. Geospace environment modeling 2008–2009 challenge: Dst index. Space Weather 11(4): 187–205. https://doi.org/10.1002/swe.20036. [CrossRef] [Google Scholar]
- Revallo M, Valach F, Hejda P, Bochníček J. 2014. A neural network Dst index model driven by input time histories of the solar wind-magnetosphere interaction. J Atmos Sol Terr Phys 110–111: 9–14. https://doi.org/10.1016/j.jastp.2014.01.011. [Google Scholar]
- Revallo M, Valach F, Hejda P, Bochnicek J. 2015. Modeling of CME and CIR driven geomagnetic storms by means of artificial neural networks. Contrib Geophys Geodesy 45(1): 53–65. https://doi.org/10.1515/congeo-2015-0013. [Google Scholar]
- Romashets EP, Poedts S, Vandas M. 2008. Modeling of the magnetic field in the magnetosheath region. J Geophys Res (Space Phys) 113: A02203. https://doi.org/10.1029/2006JA012072. [Google Scholar]
- Richardson IG, Cane HV. 2010. Near-earth interplanetary coronal mass ejections during solar cycle 23 (1996–2009): Catalog and summary of properties. Solar Phys 264: 189–237. https://doi.org/10.1007/s11207-010-9568. [Google Scholar]
- Shen XC, Hudson MK, Jaynes A, Shi Q, Tian A, Claudepierre S, et al. 2017. Statistical study of the storm time radiation belt evolution during Van Allen Probes era: CME- versus CIR-driven storms. J Geophys Res (Space Phys) 122: 8327–8339. https://doi.org/10.1002/2017JA024100. [Google Scholar]
- Stepanova M, Antonova E, Troshichev O. 2005. Prediction of Dst variations from polar cap indices using time-delay neural network. J Atmos Sol Terr Phys 67: 1658–1664. https://doi.org/10.1016/j.jastp.2005.02.027. [Google Scholar]
- Sugiura M. 1964. Hourly values of equatorial Dst for the IGY. Ann Int Geophys Year 35: 7–45. [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. https://doi.org/10.1029/2001JA007532. [CrossRef] [Google Scholar]
- Temerin M, Li X. 2006. Dst model for 1995–2002. J Geophys Res 111: A04221. https://doi.org/10.1029/2005JA011257. [CrossRef] [Google Scholar]
- Wang CB, Chao JK, Lin C-H. 2003. Influence of the solar wind dynamic pressure on the decay and injection of the ring current. J Geophys Res 108: 1341. https://doi.org/10.1029/2003JA009851. [Google Scholar]
- Wanliss JA, Showalter KM. 2006. High-resolution global storm index: Dst versus SYM-H. J Geophys Res 111(A2). https://doi.org/10.1029/2005JA011034. [Google Scholar]
- Watanabe S, Sagawa E, Ohtaka K, Shimazu H. 2002. Prediction of the Dst index from solar wind parameters by a neural network method. Earth Planet Space 54: 1263–1275. https://doi.org/10.1186/BF03352454. [Google Scholar]
- Watari S. 2017. Geomagnetic storms of cycle 24 and their solar sources. Earth Planet Space 69(1): 70. https://doi.org/10.1186/s40623-017-0653-z. [Google Scholar]
- Weigel RS. 2010. Solar wind density influence on geomagnetic storm intensity. J Geophys Res 115(A9). https://doi.org/10.1029/2009JA015062. [Google Scholar]
- Wu J-G, Lundstedt H. 1996. Prediction of geomagnetic storms from solar wind data using Elman recurrent neural networks. Geophys Res Lett 23: 319–322. https://doi.org/10.1029/96GL00259. [CrossRef] [Google Scholar]
- Wu J-G, Lundstedt H. 1997. Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks. J Geophys Res 102: 14255–14268. https://doi.org/10.1029/97JA00975. [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.