Identification of nonlinear processes in the magnetospheric dynamics and forecasting of Dst index

An approach based on the Nonlinear Autoregressive Moving Average Model With Exogenous Inputs (NARMAX) is used to analyze simultaneous measurements of the geomagnetic Dst index and VBs, the merging rate of the interplanetary magnetic field and the geomagnetic field. A nonlinear model which describes the dynamics of the Dst index driven by VBs is identified directly from experimental measurements. The identified model is then used to forecast the evolution of the Dst index and to compute generalized frequency response functions (GFRFs). Models were also identified on different intervals of data to check the stability of the main properties of the GFRFs. Analysis of these functions provides information about the types of nonlinearities involved in the energy storage process in the magnetosphere; in particular, the importance of coupling processes between various spectral components in the input which transfer energy to the summation frequency in the Dst index is illustrated. It is shown that the GFRFs derived from the data can be used to validate theoretical models which have been proposed to describe the evolution of the magnetosphere. For example, a coupling between various spectral components in the input indicates that linear models which relate VBs to Dst are inadequate to represent real physical processes involved in the Dst evolution.

[1]  Michael A. Balikhin,et al.  Experimental method for identification of dispersive three-wave coupling in space plasma , 2000 .

[2]  D. Baker,et al.  The Dst geomagnetic response as a function of storm phase and amplitude and the solar wind electric field , 1999 .

[3]  T. D. Wit,et al.  Identifying nonlinear wave interactions in plasmas using two-point measurements: A case study of Short Large Amplitude Magnetic Structures (SLAMS) , 1999, physics/9906065.

[4]  D. Baker,et al.  Dst index prediction using data‐derived analogues of the magnetospheric dynamics , 1998 .

[5]  D. Baker,et al.  Data‐derived analogues of the magnetospheric dynamics , 1997 .

[6]  Henrik Lundstedt,et al.  Neural network modeling of solar wind‐magnetosphere interaction , 1997 .

[7]  Daniel N. Baker,et al.  The organized nonlinear dynamics of the magnetosphere , 1996 .

[8]  Stephen A. Billings,et al.  Model validation tests for multivariable nonlinear models including neural networks , 1995 .

[9]  Stephen A. Billings,et al.  Nonlinear model validation using correlation tests , 1994 .

[10]  H. W. Kroehl,et al.  What is a geomagnetic storm , 1994 .

[11]  R. A. Smith,et al.  Prediction of geomagnetic activity , 1993 .

[12]  T. Bell,et al.  Nonlinear wave‐wave interactions in the subauroral ionosphere on the basis of ISIS‐2 satellite observations of Siple Station VLF signals , 1993 .

[13]  D. Fairfield Advances in magnetospheric storm and substorm research: 1989–1991 , 1992 .

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

[15]  S. Billings,et al.  Recursive algorithm for computing the frequency response of a class of non-linear difference equation models , 1989 .

[16]  Sheng Chen,et al.  Representations of non-linear systems: the NARMAX model , 1989 .

[17]  I. J. Leontaritis,et al.  Model selection and validation methods for non-linear systems , 1987 .

[18]  S. Billings,et al.  Correlation based model validity tests for non-linear models , 1986 .

[19]  Leon O. Chua,et al.  Fading memory and the problem of approximating nonlinear operators with volterra series , 1985 .

[20]  I. J. Leontaritis,et al.  Input-output parametric models for non-linear systems Part II: stochastic non-linear systems , 1985 .

[21]  S. A. Billings,et al.  Structure Detection and Model Validity Tests in the Identification of Nonlinear Systems , 1983 .

[22]  C. Russell,et al.  An empirical relationship between interplanetary conditions and Dst , 1975 .

[23]  V. Zakharov Collapse of Langmuir Waves , 1972 .