Kp forecast models

Journal of Geophysical Research: Space Physics

Volumn 110, Issue A4, 2005, Pages

  Wing, S ^a,g   Johnson, J R ^b   Jen, J ^a,g   Meng, C I ^a,g   Sibeck, D G ^c   Bechtold, K ^a,g   Freeman, J ^e   Costello, K ^f   Balikhin, M ^d   Takahashi, K ^a,g  

^a JOHNS HOPKINS UNIVERSITY   (United States)

^b PRINCETON PLASMA PHYSICS LABORATORY   (United States)

^c NASA GODDARD SPACE FLIGHT CENTER   (United States)

^d UNIVERSITY OF SHEFFIELD   (United Kingdom)

^e RICE UNIVERSITY   (United States)

^f NASA JOHNSON SPACE CENTER   (United States)

^g JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33747181292     PISSN: None     EISSN: 21699402     Source Type: Journal    
DOI: 10.1029/2004JA010500     Document Type: Article

References (39)

1. Arge, C. N., and V. J. Pizzo (2000), Improvement in the prediction of solar wind conditions using near-real time solar magnetic field updates, J. Geophys. Res., 105, 10,465-10,479.
2. Balikhin, M. A., O. M. Boaghe, S. A. Billings, and H. St C. K. Alleyne (2001), Terrestrial magnetosphere as a nonlinear resonator, Geophys. Res. Lett., 28, 1123-1126.
3. Bartels, J. (1949), The standardized index, Ks, and the planetary index, Kp, IATME Bull., 12b, 91.
4. Boaghe, O. M., M. A. Balikhin, S. A. Billings, and H. Alleyne (2001), Identification of nonlinear processes in the magnetospheric dynamics and forecasting of Dst index, J. Geophys. Lett., 106, 30,047-30,066.
5. Boberg, F., P. Wintoft, and H. Lundstedt (2000), Real time Kp prediction from solar wind data using neural networks, Phys. Chem. Earth, 25, 275-280.
6. Costello, K. A. (1997), Moving the Rice MSFM into a real-time forecast mode using solar wind driven forecast models, Ph.D. dissertation, Rice Univ., Houston, Texas.
7. Crooker, N. U., and K. I. Gringauz (1993), On the low correlation between long-term averages of solar wind speed and geomagnetic activity after 1976, J. Geophys. Res., 98, 59.
8. Detman, T., and J. A. Joselyn (1999), Real-time Kp predictions from ACE real time solar wind, in Solar Wind Nine, edited by S. R. Habbal et al., AIP Conf. Proc., 471, 729-732.
9. Fernandez, B., A. G. Parlos, and W. K. Tsai (1990), Nonlinear dynamic system identification using artificial neural networks (ANNs), in Proceedings of International Joint Conference on Neural Networks 1990, edited by M. Caudill, pp. 133-141, IEEE Press, Piscataway, N. J.
10. Fok, M.-C., T. E. Moore, and W. N. Spjeldvik (2001), Rapid enhancement of radiation belt electron fluxes due to substorm dipolarization of the geomagnetic field, J. Geophys. Res., 106, 3873-3881.
11. Fry, C. D., W. Sun, C. S. Deehr, M. Dryer, Z. Smith, S.-I. Akasofu, M. Tokumaru, and M. Kojima (2001), Improvements to the HAF solar wind model for space weather predictions, J. Geophys. Res., 106, 20,985-21,001.
12. Garrett, H. B., A. J. Dessler, and T. W. Hill (1974), Influence of solar wind variability on geomagnetic activity, J. Geophys. Res., 79, 4603-4610.
13. Gehred, P. A., W. Cliffswallow, and J. D. Schroeder III (1995), A comparison of USAF Ap and Kp indices to Gottingen indices, Tech. Memo. ERL SEL-88, NOAA, Silver Spring, Md.
14. Gershenfeld, N. (1999), The Nature of Mathematical Modeling, pp. 150-153, Cambridge Univ. Press, New York.
15. Hakamada, K., and S.-I. Akasofu (1982), Simulation of three-dimensional solar wind disturbances and resulting geomagnetic storms, Space Sci. Rev., 31, 3-70.
16. Hardy, D. A., M. S. Gussenhoven, R. Raistrick, and W. J. McNeil (1987), Statistical and functional representations of the pattern of auroral energy flux, number flux, and conductivity, J. Geophys. Res., 92, 12,275-12,294.
17. Hedin, A. E. (1987), MSIS-86 thermospheric model, J. Geophys. Res., 92, 4649.
18. Johnson, J. R., and S. Wing (2004), A cumulant-based analysis of nonlinear magnetospheric dynamics, Publ. PPPL-3919, Princeton Plasma Physics Laboratory, Princeton, N. J. (Available at http://www.pppl.gov/pub_report//2004/ PPPL-3919.pdf)
19. Johnson, J. R., and S. Wing (2005), A solar cycle dependence of nonlinearity in magnetospheric activity, J. Geophys. Res., doi:10.1029/ 2004JA010638, in press.
20. Joselyn, J. (1995), Geomagnetic activity forecasting: The state of the art, Rev. Geophys., 33, 383-401.
21. Koons, H. C., and D. J. Gomey (1991), A neural network model of the relativistic electron flux at geosynchronous orbit, J. Geophys. Res., 96, 5549-5556.
22. Lo, J., and D. Bassu (1999), Mathematical justification of recurrent multilayer perceptions with long- and short-term memories, in Proceedings of International Joint Conference on Neural Networks 1999, vol. 1, pp. 364-369, IEEE Press, Piscataway, N. J.
23. Mauk, B. H., and C. E. McIlwain (1974), Correlation of Kp with the sub-storm-injected plasma boundary, J. Geophys. Res., 79, 3193-3196.
24. Newell, P. T., S. Wing, C. I. Meng, and V. Sigillito (1990), A neural network based system for monitoring the aurora, Johns Hopkins APL Tech. Dig., 11(3, 4), 291-299.
25. Newell, P. T., S. Wing, C. I. Meng, and V. Sigillito (1991), The auroral oval position, structure and intensity of precipitation from 1984 onwards: An automated on-line data base, J. Geophys. Res., 96, 5877-5882.
26. Newell, P. T., V. A. Sergeev, G. R. Bikkuzina, and S. Wing (1998), Characterizing the state of the magnetosphere: Testing the ion precipitation maxima latitude (b2i) and the ion isotropy boundary, J. Geophys. Res., 103, 4739-4745.
27. Newell, P. T., T. Sotirelis, J. F. Carbary, K. Liou, J. P. Skura, C.-I. Meng, C. Deehr, D. Wilkinson, and F. J. Rich (2002), OVATION: Oval Variation, Assessment, Tracking, Intensity, and Online Nowcasting, Ann. Geophys., 20, 1039-1047.
28. Papitashvili, V. O., N. E. Papitashvili, and J. H. King (2000), Solar cycle effects in planetary geomagnetic activity: Analysis of 36-year long OMNI dataset, Geophys. Res. Lett., 27, 2797-2800.
29. Rostoker, G. (1972), Geomagnetic indices, Rev. Geophys., 10, 935-950.
30. Rumelhart, D. E., and J. L. McClelland (Eds.) (1987), Parallel Distributed Processing, vol. I, MIT Press, Cambridge, Mass.
31. Sergeev, V. A., M. Malkov, and K. Mursula (1993), Testing the Isotropic boundary algorithm method to evaluate the magnetic field configuration in the tail, J. Geophys. Res., 98, 7609-7620.
32. Takahashi, K., B. A. Toth, and J. V. Olson (2001), An automated procedure for near-real-time Kp estimates, J. Geophys. Res., 106, 21,017-21,032.
33. Tsyganenko, N. A. (1989), A magnetospheric magnetic field model with a warped tail current sheet, Planet. Space Sci., 37, 5-20.
34. Vandegriff, J., K. Wagstaff, G. Ho, and J. Plauger (2005), Forecasting space weather: Predicting energetic storm particle events using neural networks, Adv. Space Sci., in press.
35. Wang, Y.-M., and N. R. Sheely (1995), Solar implications of ULYSSES interplanetary field measurements, Astrophys. J., 447, L143-L146.
36. Wing, S., and P. Newell (2003), LLBL contribution to the plasma sheet ions, in Earth's Low-Latitude Boundary Layer, Geophys. Monogr. Ser., vol. 133, edited by P. T. Newell and T. Onsager, pp. 273-282, AGU, Washington D. C.
37. Wing, S., P. T. Newell, D. G. Sibeck, and K. B. Baker (1995), Large statistical study of the entry of interplanetary magnetic field Y-component into the magnetosphere, Geophys. Res. Lett., 22, 2083-2086.
38. Wing, S., R. A. Greenwald, C.-I. Meng, V. G. Sigillito, and L. V. Hutton (2003), Neural Networks for automated classification of ionospheric irregularities from HF radar backscattered signals, Radio Sci., 38(4), 1063, doi:10.1029/2003RS002869.
39. Wu, J., and H. Lundstedt (1997), Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks, J. Geophys. Res., 102, 14,255-14,268.