Several previous studies, including one using early Sanae radar data, have found examples of high speed ionospheric plasma flows on the nightside, mapping to the magnetospheric tail, during periods which were magnetically quiet. These high speed flows were interpreted to be associated with the release of energy from a rapid reconfiguration of tail magnetic field lines due to reconnection. Such events are now known as ‘TRINNIs’ or ‘tail reconnection during IMF northward, non-substorm intervals’. The purpose of this study was to identify further TRINNI events, using SuperDARN data from both hemispheres. In situations where the y-component of the Interplanetary Magnetic Field dominates over the z-component, the directions of both the high speed flows and the underlying convection pattern depend on the direction of the y-component. Some examples of likely TRINNI events for cases where the y-component was positive and negative are presented and discussed. The assumption of a non-substorm interval is justified by magnetometer and GOES satellite data, and the observations are discussed in relation to magnetic reconnection in the magnetotail. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2010.

Geomagnetic pulsations are ultra-low frequency (ULF) oscillations of the geomagnetic field that have been observed in the magnetosphere and on the Earth since the 1800’s. In the 1960’s in situ observations of the solar wind suggested that the source of pulsation activity must lie beyond the magnetosphere. In this work the influence of several solar wind plasma and interplanetary magnetic field (IMF) parameters on Pc3 pulsations are studied. Pc3 pulsations are a class of geomagnetic pulsations with frequency ranging between 22 and 100 mHz. A large dataset of solar wind and pulsation measurements is employed to develop two empirical models capable of predicting the Pc3 index (an indication of Pc3 intensity) at one hour and five minute time resolution, respectively. The models are based on artificial neural networks, due to their ability to model highly non-linear interactions between dependent and independent variables. A robust, iterative process is followed to find and rank the set of solar wind input parameters that optimally predict Pc3 activity. According to the parameter selection process the input parameters to the low resolution model (1 hour data) are, in order of importance, solar wind speed, a pair of time-based parameters, dynamic solar wind pressure, and the IMF orientation with respect to the Sun-Earth line (i.e. the cone angle). Input parameters to the high resolution model (5 minute data) are solar wind speed, cone angle, solar wind density and a pair of time-based parameters. Both models accurately predict Pc3 intensity from unseen solar wind data. It is observed that Pc3 activity ceases when the density in the solar wind is very low, even while other conditions are favourable for the generation and propagation of ULF waves. The influence that solar wind density has on Pc3 activity is studied by analysing six years of solar wind and Pc3 measurements at one minute resolution. It is suggested that the pause in Pc3 activity occurs due to two reasons: Firstly, the ULF waves that are generated in the region upstream of the bow shock does not grow efficiently if the solar wind density is very low; and secondly, waves that are generated cannot be convected into the magnetosphere because of the low Mach number of the solar wind plasma due to the decreased density.

Solar transient eruptions are the main cause of interplanetary-magnetospheric disturbances leading to the phenomena known as geomagnetic storms. Eruptive solar events such as coronal mass ejections (CMEs) are currently considered the main cause of geomagnetic storms (GMS). GMS are strong perturbations of the Earth’s magnetic field that can affect space-borne and ground-based technological systems. The solar-terrestrial impact on modern technological systems is commonly known as Space Weather. Part of the research study described in this thesis was to investigate and establish a relationship between GMS (periods with Dst ≤ −50 nT) and their associated solar and interplanetary (IP) properties during solar cycle (SC) 23. Solar and IP geoeffective properties associated with or without CMEs were investigated and used to qualitatively characterise both intense and moderate storms. The results of this analysis specifically provide an estimate of the main sources of GMS during an average 11-year solar activity period. This study indicates that during SC 23, the majority of intense GMS (83%) were associated with CMEs, while the non-associated CME storms were dominant among moderate storms. GMS phenomena are the result of a complex and non-linear chaotic system involving the Sun, the IP medium, the magnetosphere and ionosphere, which make the prediction of these phenomena challenging. This thesis also explored the predictability of both the occurrence and strength of GMS. Due to their nonlinear driving mechanisms, the prediction of GMS was attempted by the use of neural network (NN) techniques, known for their non-linear modelling capabilities. To predict the occurrence of storms, a combination of solar and IP parameters were used as inputs in the NN model that proved to predict the occurrence of GMS with a probability of 87%. Using the solar wind (SW) and IP magnetic field (IMF) parameters, a separate NN-based model was developed to predict the storm-time strength as measured by the global Dst and ap geomagnetic indices, as well as by the locally measured K-index. The performance of the models was tested on data sets which were not part of the NN training process. The results obtained indicate that NN models provide a reliable alternative method for empirically predicting the occurrence and strength of GMS on the basis of solar and IP parameters. The demonstrated ability to predict the geoeffectiveness of solar and IP transient events is a key step in the goal towards improving space weather modelling and prediction.