Empirical modelling of the solar wind influence on Pc3 pulsation activity
- Authors: Lotz, Stefanus Ignatius
- Date: 2012
- Subjects: Solar wind -- Research Solar activity -- Research Stellar oscillations -- Research , Magnetospheric radio wave propagation , Interplanetary magnetic fields
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:5464 , http://hdl.handle.net/10962/d1005249
- Description: 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.
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- Date Issued: 2012
Predictability of Geomagnetically Induced Currents using neural networks
- Authors: Lotz, Stefanus Ignatius
- Date: 2009
- Subjects: Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5483 , http://hdl.handle.net/10962/d1005269 , Advanced Composition Explorer (Artificial satellite) , Geomagnetism , Electromagnetic induction , Neural networks (Computer science) , Artificial intelligence
- Description: It is a well documented fact that Geomagnetically Induced Currents (GIC’s) poses a significant threat to ground-based electric conductor networks like oil pipelines, railways and powerline networks. A study is undertaken to determine the feasibility of using artificial neural network models to predict GIC occurrence in the Southern African power grid. The magnitude of an induced current at a specific location on the Earth’s surface is directly related to the temporal derivative of the geomagnetic field (specifically its horizontal components) at that point. Hence, the focus of the problem is on the prediction of the temporal variations in the horizontal geomagnetic field (@Bx/@t and @By/@t). Artificial neural networks are used to predict @Bx/@t and @By/@t measured at Hermanus, South Africa (34.27◦ S, 19.12◦ E) with a 30 minute prediction lead time. As input parameters to the neural networks, insitu solar wind measurements made by the Advanced Composition Explorer (ACE) satellite are used. The results presented here compare well with similar models developed at high-latitude locations (e.g. Sweden, Finland, Canada) where extensive GIC research has been undertaken. It is concluded that it would indeed be feasible to use a neural network model to predict GIC occurrence in the Southern African power grid, provided that GIC measurements, powerline configuration and network parameters are made available.
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- Date Issued: 2009