Real‐time prediction of magnetospheric activity using the Boyle Index

[1] We present a new algorithm with an improvement in the accuracy and lead time in short-term space weather predictions by coupling the Boyle Index, Φ = 10−4ν2 + 11.7Bsin3(θ/2) kV, to artificial neural networks. The algorithm takes inputs from ACE and a handful of ground-based magnetometers to predict the next upcoming Kp in real time. The model yields a correlation coefficient of over 86% when predicting Kp with a lead time of 1 hour and over 85% for a 2 hour ahead prediction, significantly larger than the Kp persistence of 0.80. The Boyle Index, available in near-real time from http://space.rice.edu/ISTP/wind.html, has been in use for over 5 years now to predict geomagnetic activity. The logarithm of both 3-hour and 1-hour averages of the Boyle Index correlates well with the following Kp: Kp = 8.93 log10 –12.55. Using the Boyle Index alone, the algorithm yields a correlation coefficient of 85% when predicting Kp with a lead time of 1 hour and over 84% for a 3 hour ahead prediction, nearly as good as when using Kp in the history but without any possibility of “persistence contamination.” Although the Boyle Index generally overestimates the polar cap potential for severe events, it does predict that severe activity will occur. Also, 1-hour value less than 100 kV is a good indicator that the magnetosphere will be quiet. However, some storm events with Kp > 6 occur when the Boyle Index is relatively low; the new algorithm is successful in predicting those events by capturing the influence of preconditioning.

[1]  J. Raeder,et al.  Cold dense magnetopause boundary layer under northward IMF: Results from THEMIS and MHD simulations , 2009 .

[2]  Kirt Allen Costello,et al.  Moving the Rice MSFM into a real-time forecast mode using solar wind driven forecast modules , 1998 .

[3]  Real-time Kp predictions from ACE real time solar wind , 2008 .

[4]  David G. Sibeck,et al.  Kp forecast models , 2005 .

[5]  Peter Wintoft,et al.  Real time Kp predictions from solar wind data using neural networks , 2000 .

[6]  Emilia Kilpua,et al.  Interplanetary coronal mass ejections in the near-Earth solar wind during the minimum periods following solar cycles 22 and 23 , 2011 .

[7]  Frederick J. Rich,et al.  A nearly universal solar wind-magnetosphere coupling function inferred from 10 magnetospheric state variables , 2007 .

[8]  T. Hill,et al.  Observed saturation of the ionospheric polar cap potential during the 31 March 2001 storm , 2003 .

[9]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[10]  J. Borovsky,et al.  Differences between CME‐driven storms and CIR‐driven storms , 2006 .

[11]  J. King,et al.  Solar cycle effects in planetary geomagnetic activity: Analysis of 36‐year long OMNI dataset , 2000 .

[12]  Simon Wing,et al.  A solar cycle dependence of nonlinearity in magnetospheric activity , 2004 .

[13]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[14]  R. Lepping,et al.  Effect of solar wind velocity on magnetic cloud‐associated magnetic storm intensity , 2002 .

[15]  John V. Olson,et al.  An automated procedure for near-real-time Kp estimates , 2001 .

[16]  Gopal Kanji,et al.  100 Statistical Tests , 1994 .

[17]  T. Hill,et al.  Mercury and Mars: The role of ionospheric conductivity in the acceleration of magnetospheric particles , 1976 .

[18]  T. Pulkkinen,et al.  Magnetosphere preconditioning under northward IMF: Evidence from the study of coronal mass ejection and corotating interaction region geoeffectiveness , 2006 .

[19]  Akira Nagai.  Prediction of magnetospheric parameters using artificial neural networks , 1995 .

[20]  P. Jayachandran,et al.  Polar cap voltage saturation , 2006 .

[21]  G. Siscoe,et al.  Hill model of transpolar potential saturation: Comparisons with MHD simulations , 2002 .

[22]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[23]  M. Menvielle,et al.  The K-derived planetary indices - Description and availability , 1991 .

[24]  V. Angelopoulos,et al.  THEMIS multi‐spacecraft observations of magnetosheath plasma penetration deep into the dayside low‐latitude magnetosphere for northward and strong By IMF , 2008 .

[25]  Timothy Masters,et al.  Neural, Novel & Hybrid Algorithms for Time Series Prediction , 1995 .

[26]  M. Vogt,et al.  Space weather drivers in the ACE era , 2004 .

[27]  Henrik Lundstedt,et al.  Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks , 1997 .

[28]  Patricia H. Reiff,et al.  Empirical polar cap potentials , 1997 .