The Dst (Disturbance storm time) index is a measurement of earth geomagnetic activity and is widely used to characterize the geomagnetic storm. It is calculated on the basis of the average value of the horizontal component of the earth's magnetic field at four observatories, namely, Hermanus (33.3°south, 80.3°in magnetic dipole latitude and longitude), Kakioka (26.0°north, 206.0°), Honolulu (21.0°n orth, 266.4°), and San Juan (29.9°north, 3.2°) and is expressed in nano-Teslas. The strength of the low-latitude surface magnetic field is inversely proportional to the energy content of the ring current around earth caused by solar protons and electrons, which increases during geomagnetic storms. Thus a negative Dst index value indicates that the earth's magnetic field is weakened which is specifically the case during solar storms. Predicting Dst index is a difficult task due to its structural complexity involving a variety of underlying plasma mechanism. For characterizing and forecasting this complex time series, a formal model must be established to identify the specific pattern of the series. Persistent demand for a fool proof model of Geomagnetic Dst index prompted us to investigate the Dst Time Series mechanism with a very recent technique called Visibility Algorithm and it is observed that the Dst time series follows the same model that of a Stochastic Fractional Brownian motion having long range correlation.

The disturbance storm time (Dst) index, a measure of the strength of a geomagnetic storm, is difficult to predict by some conventional methods due to its abstract structural complexity and stochastic nature though a timely geomagnetic storm warning could save society from huge economic losses and hours of related hazards. Self-organized criticality and the concept of many-body interactive nonlinear system can be considered an explanation for the fundamental mechanism of the nonstationary geomagnetic disturbances controlled by the perturbed interplanetary conditions. The present paper approaches this natural phenomena by a sandpile-like cellular automata-based model of magnetosphere, taking the real-time solar wind and both the direction and magnitude of the B Z component of the real-time interplanetary magnetic field as the system-controlling input parameters. Moreover, three new parameters had been introduced in the model which modify the functional relationships between the variables and regulate the dynamical behavior of the model to closely approximate the actual geomagnetic fluctuations. The statistical similarities between the dynamics of the model and that of the actual Dst index series during the entire 22nd solar cycle signifies the acceptability of the model.

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