• 2022-06-10
• Bakare N.O
• Volume 4, Issue 1

Abstract

Timely knowledge of disturbance storm time (Dst) index gives us an idea of the severity with which geomagnetic storms may occur, so that possible preventive or remedial measures could be taken. The various techniques for predicting geomagnetic storms are not adequately available for use globally, either since the data required are not available in real time or computing facilities are inadequate. Therefore, the study motivated the current research to improve on the existing models, which is a linear predictive model using the solar wind parameters: magnetic field, flow speed and electric field. A total of 74 intense storms during the solar cycle 23 were used. The geomagnetic storms are primarily associated with two classes of drivers, which are the magnetic cloud and the complex ejecta. The correlation coefficients for individual parameters were calculated. The correlation coefficients were highly significant for all the parameters, which afforded a predictive model based on multivariance regression analysis. The modeled result shows that the initial and recovery phases of the geomagnetic storms were well predicted, while during the main phase, in some cases, predicted Dst is smaller than the measured Dst. The difference between the measured and predicted Dst suggests that the geomagnetic storm phenomena like pre-storm phenomena could result from some underlying mechanism that is working together with varying degrees of importance and is probably not adequately captured by available solar wind parameters.

Download PDF