Probability distribution invariance of 1‐minute auroral‐zone geomagnetic field fluctuations

[1] A statistical model of short time-scale geomagnetic fluctuations is developed and used to evaluate how geomagnetic dynamics are influenced by different solar wind controlling parameters. The functional form of the probability distribution function (PDF) that describes extreme-value (greater than 4a) minute-to-minute changes in the ground magnetic field (Δx) at magnetometer station Sodankyla (geomagnetic latitude and longitude of [63.87,107.61]) is shown to be nearly independent of the variables solar wind (SW) forcing, local time (LT), and day of year (DOY). Instead of modifying the intrinsic dynamics, as characterized by the functional form of the PDF of Δx, these variables are shown either to amplify or reduce the absolute level of variability of the fluctuations: The primary difference in the PDF tail of Δx during weak and strong solar wind forcing is the standard deviation, a; the functional form of the PDF = f[Δx/σ(DOY,LT,SW)] is nearly invariant. In a statistical interpretation, we conclude that differences in solar-generated conductivity, seasonal effects, strength of solar wind forcing and variability, and position of the magnetometer ground station in local time do not change the structure of the extreme-value dynamics, as characterized by the probability distribution of Ax, but they serve to amplify the intrinsic variability.

[1]  M. Freeman,et al.  Evidence for a solar wind origin of the power law burst lifetime distribution of the AE indices , 2000 .

[2]  Daniel N. Baker,et al.  The evolution from weak to strong geomagnetic activity: an interpretation in terms of deterministic chaos , 1990 .

[3]  H. Gleisner,et al.  Response of the auroral electrojets to the solar wind modeled with neural networks , 1997 .

[4]  Antti Pulkkinen,et al.  Time derivative of the horizontal geomagnetic field as an activity indicator , 2001 .

[5]  Edward J. Smith,et al.  The nonlinear response of AE to the IMF BS driver: A spectral break at 5 hours , 1990 .

[6]  Toshiki Tajima,et al.  Forecasting auroral electrojet activity from solar wind input with neural networks , 1999 .

[7]  David Boteler,et al.  The effects of geomagnetic disturbances on electrical systems at the earth's surface , 1998 .

[8]  M. Kivelson,et al.  Solar wind control of auroral zone geomagnetic activity , 1981 .

[9]  British Antarctic Survey,et al.  Scaling of solar wind ϵ and the AU, AL and AE indices as seen by WIND , 2002, physics/0208021.

[10]  K. Sobczyk.  Stochastic Differential Equations: With Applications to Physics and Engineering , 1991 .

[11]  D. Vassiliadis,et al.  On the uniqueness of linear moving-average filters for the solar wind-auroral geomagnetic activity coupling , 1995 .

[12]  D. D. Zeeuw,et al.  Using steady state MHD results to predict the global state of the magnetosphere‐ionosphere system , 2001 .

[13]  Sreenivasa Rao Jammalamadaka,et al.  Statistical Distributions in Engineering , 1999 .

[14]  D. Newman,et al.  Using the R/S statistic to analyze AE data , 2001 .

[15]  Karl V. Bury,et al.  Statistical Distributions in Engineering: Statistics , 1999 .

[16]  Wendell Horton,et al.  A Low-Dimensional Dynamical Model for the Solar Wind Driven Geotail-Ionosphere System , 1998 .

[17]  Daniel N. Baker,et al.  Substorm recurrence during steady and variable solar wind driving: Evidence for a normal mode in the unloading dynamics of the magnetosphere , 1994 .

[18]  D. Vassiliadis,et al.  Coupling of the solar wind to temporal fluctuations in ground magnetic fields , 2002 .

[19]  Robert Scott Weigel,et al.  Precursor analysis and prediction of large‐amplitude relativistic electron fluxes , 2003 .

[20]  G. Crowley,et al.  Quantification of high latitude electric field variability , 2001 .

[21]  R. Pirjola,et al.  Geomagnetically Induced Currents During Magnetic , 2000 .

[22]  Testing the SOC hypothesis for the magnetosphere , 2000, astro-ph/0002359.