Real Time Kp Predictions from Solar Wind Data using Neural Networks

摘要

Multilayer feed-forward neural network models are developed to make three-hour predictions of the planetary magnetospheric K_p index. The input parameters for the networks are the B_z-component of the interplanetary magnetic field, the solar wind density n, and the solar wind velocity V, given as three-hour averages. The networks are trained with the error back-propagation algorithm on data sequences extracted from the 21~(st) solar cycle. The result is a hybrid model consisting of two expert networks providing Kp predictions with an RMS error of 0.96 and a correlation of 0.76 in reference to the measured Kp values. This result can be compared with the linear correlation between V(t) and Kp(t + 3 hours) which is 0.47. The hybrid model is tested on geomagnetic storm events extracted from the 22~(nd) solar cycle. The hybrid model is implemented and real time predictions of the planetary magnetospheric Kp index are available at http://www.astro.lu.se/～fredrikb.