Evaluation of solar wind‐magnetosphere coupling functions during geomagnetic storms with the WINDMI model

Abstract

[1] We evaluate the performance of three solar wind-magnetosphere coupling functions in training the physics-based WINDMI model on the 3-7 October 2000 geomagnetic storm and predicting the geomagnetic Dst and AL indices during the 15-24 April 2002 geomagnetic storm. The rectified solar wind electric field, a coupling function by Siscoe, and a recent formula proposed by Newell are evaluated. The Newell coupling function performed best in both the training and prediction phases for Dst prediction. The Siscoe formula performed best during the training phase in reproducing the AL faithfully and capturing storm time events. The rectified driver was discovered to be the best in overall performance during both training as well as prediction phases, even though the other two coupling functions outperform it in the training phase. The results indicate that multiple drivers need to be concurrently employed in space weather models to yield different possible levels of geomagnetic activity.

An optimum solar wind coupling function for the AL index

“…Initially, this model consisted of five equations with parameters derived from MHD and plasma physics considerations of the geomagnetic tail and its linkage to the ionosphere. It has evolved to eight equations [ Spencer et al ., ] with 21 free parameters [see Mays et al ., , Table 1] predicting both the AL and Dst indices. When driven by solar wind coupling functions, the model is a generally good predictor of the activity indices.…”

“…The model performs better when empirical parameters calculated by genetic algorithms are used in the equations [ Spencer et al ., ]. It has been used to evaluate the efficacy of several coupling functions with mixed results concerning which is best [ Spencer et al ., ].…”

Emotional Brain-Inspired Adaptive Fuzzy Decayed Learning for online prediction problems

“…The result is presented in Appendix A written and evaluated in Matlab R2010b. The source code is accessible from http://www.bitools.ir/projects.html and is used to predict Kp (Kennziffer planetarisch), AE (Auroral electrojet) and Ds storm time index which are used to characterize the geomagnetic activity of the earth's magnetosphere [27][28][29][30][31]. These time series have chaotic behavior [36][37][38] with low dimensional chaos [39][40] and many researchers used them as case study to assess the new methods [41][42].…”

Ground-Based Monitoring of the Solar Wind Geoefficiency

“…It is well known (Kamide, 1974;Russel et al, 1974;Burton et al, 1975;Akasofu, 1981) that magnetic storms intensity is dominantly controlled by southward IMF component (B ZS ), whereas the solar wind velocity (v) and density (n) are of minor importance. While investigating solar wind-magnetosphere coupling functions, the best result was obtained for functions including the geoeffective interplanetary electric field E KL (Newell et al, 2008;Spencer et al, 2009).…”