An analysis of sources and predictability of geomagnetic storms
- Authors: Uwamahoro, Jean
- Date: 2011
- Subjects: Ionospheric storms Solar flares Interplanetary magnetic fields Magnetospheric substorms Coronal mass ejections Space environment Neural networks (Computer science)
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:5451 , http://hdl.handle.net/10962/d1005236
- Description: 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.
- Full Text:
- Authors: Uwamahoro, Jean
- Date: 2011
- Subjects: Ionospheric storms Solar flares Interplanetary magnetic fields Magnetospheric substorms Coronal mass ejections Space environment Neural networks (Computer science)
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:5451 , http://hdl.handle.net/10962/d1005236
- Description: 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.
- Full Text:
Forecasting solar cycle 24 using neural networks
- Authors: Uwamahoro, Jean
- Date: 2009
- Subjects: Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5468 , http://hdl.handle.net/10962/d1005253 , Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Description: The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
- Full Text:
- Authors: Uwamahoro, Jean
- Date: 2009
- Subjects: Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:5468 , http://hdl.handle.net/10962/d1005253 , Solar cycle , Neural networks (Computer science) , Ionosphere , Ionospheric electron density , Ionospheric forecasting , Solar thermal energy
- Description: The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near-Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. Several prediction techniques have been applied and have achieved varying degrees of success in the domain of solar activity prediction. These techniques include, for example, neural networks and geomagnetic precursor methods. In this thesis, various neural network based models were developed and the model considered to be optimum was used to estimate the shape and timing of solar cycle 24. Given the recent success of the geomagnetic precusrsor methods, geomagnetic activity as measured by the aa index is considered among the main inputs to the neural network model. The neural network model developed is also provided with the time input parameters defining the year and the month of a particular solar cycle, in order to characterise the temporal behaviour of sunspot number as observed during the last 10 solar cycles. The structure of input-output patterns to the neural network is constructed in such a way that the network learns the relationship between the aa index values of a particular cycle, and the sunspot number values of the following cycle. Assuming January 2008 as the minimum preceding solar cycle 24, the shape and amplitude of solar cycle 24 is estimated in terms of monthly mean and smoothed monthly sunspot number. This new prediction model estimates an average solar cycle 24, with the maximum occurring around June 2012 [± 11 months], with a smoothed monthly maximum sunspot number of 121 ± 9.
- Full Text:
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