The work described in this thesis is part of the initial development of the open-source visualization software OpenSpace, a collaborative project between Linköping University (LiU), the National Aeronautics and Space Administration (NASA) and the American Museum of Natural History (AMNH). The report covers the background and implementation of a rendering system that enables OpenSpace to interactively visualize multiple overlapping space weather events. The system works much like a Deferred Renderer by rendering all objects once and then resolves the final image in a second rendering step. To render a mix of opaque and translucent objects and volumes simultaneously, order-independent transparency solutions are implemented. Performance is compared against traditional methods and possible improvements are discussed. The implemented rendering system is currently powering the OpenSpace visualizations, this gives scientists an interactive tool for studying multiple space weather events, education and public outreach.

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART¿s active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

Yes / In this paper, a machine-learning-based system that could provide automated short-term solar flare prediction is presented. This system accepts two sets of inputs: McIntosh classification of sunspot groups and solar cycle data. In order to establish a correlation between solar flares and sunspot groups, the system explores the publicly available solar catalogues from the National Geophysical Data Center to associate sunspots with their corresponding flares based on their timing and NOAA numbers. The McIntosh classification for every relevant sunspot is extracted and converted to a numerical format that is suitable for machine learning algorithms. Using this system we aim to predict whether a certain sunspot class at a certain time is likely to produce a significant flare within six hours time and if so whether this flare is going to be an X or M flare. Machine learning algorithms such as Cascade-Correlation Neural Networks (CCNNs), Support Vector Machines (SVMs) and Radial Basis Function Networks (RBFN) are optimised and then compared to determine the learning algorithm that would provide the best prediction performance. It is concluded that SVMs provide the best performance for predicting whether a McIntosh classified sunspot group is going to flare or not but CCNNs are more capable of predicting the class of the flare to erupt. A hybrid system that combines a SVM and a CCNN is suggested for future use. / EPSRC

yes / This paper presents a hybrid system for automatic detection and McIntosh-based classification of sunspot groups on SOHO/MDI white-light images using active-region data extracted from SOHO/MDI magnetogram images. After sunspots are detected from MDI white-light images they are grouped/clustered using MDI magnetogram images. By integrating image-processing and neural network techniques, detected sunspot regions are classified automatically according to the McIntosh classification system. Our results show that the automated grouping and classification of sunspots is possible with a high success rate when compared to the existing manually created catalogues. In addition, our system can detect and classify sunspot groups in their early stages, which are usually missed by human observers. / EPSRC

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART's active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

Coronal Mass Ejections (CMEs) and solar flares are energetic events taking place at the Sun that can affect the space weather or the near-Earth environment by the release of vast quantities of electromagnetic radiation and charged particles. Solar active regions are the areas where most flares and CMEs originate. Studying the associations among sunspot groups, flares, filaments, and CMEs is helpful in understanding the possible cause and effect relationships between these events and features. Forecasting space weather in a timely manner is important for protecting technological systems and human life on earth and in space. The research presented in this thesis introduces novel, fully computerised, machine learning-based decision rules and models that can be used within a system design for automated space weather forecasting. The system design in this work consists of three stages: (1) designing computer tools to find the associations among sunspot groups, flares, filaments, and CMEs (2) applying machine learning algorithms to the associations' datasets and (3) studying the evolution patterns of sunspot groups using time-series methods. Machine learning algorithms are used to provide computerised learning rules and models that enable the system to provide automated prediction of CMEs, flares, and evolution patterns of sunspot groups. These numerical rules are extracted from the characteristics, associations, and time-series analysis of the available historical solar data. The training of machine learning algorithms is based on data sets created by investigating the associations among sunspots, filaments, flares, and CMEs. Evolution patterns of sunspot areas and McIntosh classifications are analysed using a statistical machine learning method, namely the Hidden Markov Model (HMM).

<p>Magnetized planets, such as Earth, are strongly influenced by the solar wind. The Sun is very dynamic, releasing varying amounts of energy, resulting in a fluctuating energy and momentum exchange between the solar wind and planetary magnetospheres. The efficiency of this coupling is thought to be controlled by magnetic reconnection occurring at the boundary between solar wind and planetary magnetic fields. One of the main tasks in space physics research is to increase the understanding of this coupling between the Sun and other solar system bodies. Perhaps the most important aspect regards the transfer of energy from the solar wind to the terrestrial magnetosphere as this is the main source for driving plasma processes in the magnetosphere-ionosphere system. This may also have a direct practical influence on our life here on Earth as it is responsible for Space Weather effects. In this thesis I investigate both the global scale of the varying solar-terrestrial coupling and local phenomena in more detail. I use mainly the European Space Agency Cluster mission which provide unprecedented three-dimensional observations via its formation of four identical spacecraft. The Cluster data are complimented with observations from a broad range of instruments both onboard spacecraft and from groundbased magnetometers and radars.</p><p>A period of very strong solar driving in late October 2003 is investigated. We show that some of the strongest substorms in the history of magnetic recordings were triggered by pressure pulses impacting a quasi-stable magnetosphere. We make for the first time direct estimates of the local energy flow into the magnetotail using Cluster measurements. Observational estimates suggest a good energy balance between the magnetosphere-ionosphere system while empirical proxies seem to suffer from over/under estimations during such extreme conditions.</p><p>Another period of extreme interplanetary conditions give rise to accelerated flows along the magnetopause which could account for an enhanced energy coupling between the solar wind and the magnetosphere. We discuss whether such conditions could explain the simultaneous observation of a large auroral spiral across the polar cap.</p><p>Contrary to extreme conditions the energy conversion across the dayside magnetopause has been estimated during an extended period of steady interplanetary conditions. A new method to determine the rate at which reconnection occurs is described that utilizes the magnitude of the local energy conversion from Cluster. The observations show a varying reconnection rate which support the previous interpretation that reconnection is continuous but its rate is modulated.</p><p>Finally, we compare local energy estimates from Cluster with a global magnetohydrodynamic simulation. The results show that the observations are reliably reproduced by the model and may be used to validate and scale global magnetohydrodynamic models.</p>

The non-hydrostatic dynamical core ENDGame (Even Newer Dynamics for the General Atmospheric Modelling of the Environment) is extended into the thermosphere to test its feasability as a whole-atmosphere dynamical core that can simulate the large scale fluid dynamics of the whole atmosphere from the surface to the top of the thermosphere at 600km. This research may have applications in the development of a Sun-to-Earth modelling system involving the Met Office Unified Model, which will be useful for space weather forecasting and chemical climate modelling. Initial attempts to raise the top boundary of ENDGame above ∼100km give rise to instabilities. To explore the potential causes of these instabilities, a one dimensional column version of ENDGame: ENDGame1D, is developed to study the effects of vertically propagating acoustic waves in the dynamical core. A 2D ray-tracing scheme is also developed, which accounts for the numerical effects on wave propagation. It is found that ENDGame’s numerics have a tendency towards the excessive focussing of wave energy towards vertical propagation, and have poor handling of large amplitude waves, also being unable to handle shocks. A key finding is that the physical processes of vertical molecular viscosity and diffusion prevent the excessive growth of wave amplitudes in the thermosphere in ENDGame, which may be crucial to improving ENDGame’s stability as it is extended upwards. Therefore, a fully implicit-in-time implementation of vertical molecular viscosity and diffusion is developed in both ENDGame1D and the full three-dimensional version of ENDGame: ENDGame3D. A new scheme is developed to deal with the viscous and diffusive terms with the dynamics terms in a fully coupled way to avoid time-splitting errors that may arise. The combination of a small amount of off-centring of ENDGame’s semi-implicit formulation and the inclusion of vertical molecular viscosity and diffusion act to make ENDGame significantly more stable, as long as the simulation is able to remain stable up to the molecularly diffused region above an altitude of ∼130km.

Magnetized planets, such as Earth, are strongly influenced by the solar wind. The Sun is very dynamic, releasing varying amounts of energy, resulting in a fluctuating energy and momentum exchange between the solar wind and planetary magnetospheres. The efficiency of this coupling is thought to be controlled by magnetic reconnection occurring at the boundary between solar wind and planetary magnetic fields. One of the main tasks in space physics research is to increase the understanding of this coupling between the Sun and other solar system bodies. Perhaps the most important aspect regards the transfer of energy from the solar wind to the terrestrial magnetosphere as this is the main source for driving plasma processes in the magnetosphere-ionosphere system. This may also have a direct practical influence on our life here on Earth as it is responsible for Space Weather effects. In this thesis I investigate both the global scale of the varying solar-terrestrial coupling and local phenomena in more detail. I use mainly the European Space Agency Cluster mission which provide unprecedented three-dimensional observations via its formation of four identical spacecraft. The Cluster data are complimented with observations from a broad range of instruments both onboard spacecraft and from groundbased magnetometers and radars. A period of very strong solar driving in late October 2003 is investigated. We show that some of the strongest substorms in the history of magnetic recordings were triggered by pressure pulses impacting a quasi-stable magnetosphere. We make for the first time direct estimates of the local energy flow into the magnetotail using Cluster measurements. Observational estimates suggest a good energy balance between the magnetosphere-ionosphere system while empirical proxies seem to suffer from over/under estimations during such extreme conditions. Another period of extreme interplanetary conditions give rise to accelerated flows along the magnetopause which could account for an enhanced energy coupling between the solar wind and the magnetosphere. We discuss whether such conditions could explain the simultaneous observation of a large auroral spiral across the polar cap. Contrary to extreme conditions the energy conversion across the dayside magnetopause has been estimated during an extended period of steady interplanetary conditions. A new method to determine the rate at which reconnection occurs is described that utilizes the magnitude of the local energy conversion from Cluster. The observations show a varying reconnection rate which support the previous interpretation that reconnection is continuous but its rate is modulated. Finally, we compare local energy estimates from Cluster with a global magnetohydrodynamic simulation. The results show that the observations are reliably reproduced by the model and may be used to validate and scale global magnetohydrodynamic models.

Thesis (MScEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: Geomagnetic storms are primarily driven by the rapid transfer of energy from the solar wind to the magnetosphere. The mechanism of energy transfer involves the merging of the interplanetary magnetic field to the geomagnetic field in a process known as magnetic reconnection. This leads to an influx of energetic, charged particles into the magnetosphere so that current systems are enhanced. Specifically, an increase in the equatorial ring current leads to a decrease in the surface field. Geomagnetic storms are thus characterized by a strong decline in the horizontal components of the geomagnetic field, lasting from several hours to days. The intensity of a storm is described by the disturbed storm-time index, which is essentially a measure of the deviation from the typical quiet day variation along the equator. Severe storms can lead to the disruption of high frequency (HF) communications as a consequence of a strongly perturbed ionosphere. By the same token, the global positioning system (GPS) can become highly unreliable during magnetically disturbed conditions, yielding distance errors as large as 50 meters. The impact of geomagnetic activity and other solar-driven processes on technology systems are collectively known as space weather. Magnetic field sensing thus forms an important part of space weather forecasting and is vital to space science research as a means of improving our understanding of solar wind-magnetosphere interactions. This study examines the use of magnetometers built as SQUIDs (Superconducting Quantum Interference Devices) for monitoring the geomagnetic field for space weather forecasting purposes. A basic theory of superconductivity is presented and subsequently the key aspects governing the operation of SQUIDs are discussed. Space weather is also introduced with respect to the various processes on the sun that perturb the magnetosphere and hence the geomagnetic field. The method of analysis was basically to Fourier-transform the data using the Wiener-Khintchine theorem. A systematic approach to Fourier analysis is thus presented, demonstrating the superiority of the Wiener-Khintchine theorem in noise reduction. The suitability of SQUID magnetometers for space science research is demonstrated by a comparative study between SQUID and fluxgate datasets for magnetic storms during 2011. Strong correlation was observed between the frequency content of the SQUID and fluxgate signals. This result supports South Africa’s SQUID project, currently undertaken as a collaborative effort between SANSA Space Science and the Department of Electrical and Electronic Engineering at Stellenbosch University. This thesis thus lays a foundation for future research involving advanced magnetometry using SQUIDs. / AFRIKAANSE OPSOMMING: Geomagnetiese storms word hoofsaaklik gedryf deur die vinnige oordrag van energie van die sonwind na die magnetosfeer. Die meganisme van energie oordrag behels die samesmelting van die interplanetêre magneetveld met die geomagneetveld, in 'n proses wat bekend staan as magnetiese heraansluiting. Dit lei tot 'n instroming van energieke elektries-gelaaide deeltjies, tot in die magnetosfeer, met die gevolg dat magnetosferiese elektriese stroomstelsels versterk word. 'n Toename in die ekwatoriale ringstrome lei spesifiek tot 'n afname in die horisontale komponent van die geomagnetiese veld. Geomagnetiese storms word dus gekenmerk deur 'n sterk afname in die horisontale komponent van die geomagnetiese veld, ‘n afname wat etlike ure tot dae kan duur. Die intensiteit van 'n storm word beskryf deur die storm-tyd versteurings indeks , 'n maatstaf van die afwyking van die tipiese stil dag magnetiese variasie langs die ewenaar. Ernstige storms kan lei tot die ontwrigting van hoë frekwensie (HF) kommunikasie as 'n gevolg van 'n erg versteurde ionosfeer. Soortgelyk kan die Globale Posisionering Stelsel (GPS) hoogs onbetroubaar word tydens magneties versteurde toestande, en posisiefoute so groot as 50 meter veroorsaak. Die impak van geomagnetiese aktiwiteit en ander sonkrag gedrewe prosesse op tegnologie is gesamentlik bekend as ruimteweer. Magneetveldmetinge vorm dus 'n belangrike deel van ruimteweervoorspelling en is noodsaaklik vir ruimtewetenskaplike navorsing as 'n middel om die sonwind-magnetosfeer interaksies beter te verstaan. Hierdie studie ondersoek die gebruik van SQUID (Engels: Superconducting Quantum Interference Device) magnetometers vir die monitering van die geomagnetiese veld vir ruimteweervoorspellingsdoeleindes. ’n Basiese teorie van supergeleiding word aangebied, waarvolgens die sleutelaspekte van SQUIDs bespreek word. Ruimteweer word ook voorgestel in terme van die verskillende prosesse op die son wat die aarde se magnetosfeer en dus die geomagnetiese veld versteur. Die analisemetode wat hier gebruik word, is om die Fourier-transform van data met die Wiener-Khintchine theorema te bereken. A sistematiese metode vir Fourier-analise word aangebied, wat die superiorireit van die Wiener-Khintchine teorema vir ruisvermindering demonstreer. Die geskiktheid van SQUID magnetometers vir ruimtewetenskaplike navorsing word gedemonstreer deur ’n vergelykende studie tussen SQUID- en vloedhek-datastelle vir magnetiese storms gedurende 2011. Sterk korrelasie is waargeneem tussen die frekwensie-inhoud van die SQUID- en vloedhekseine. Hierdie resultate ondersteun Suid-Afrika se SQUID-projek, wat tans as ’n samewerkingspoging tussen SANSA Space Science en die Departement Elektriese en Elektroniese Ingenieurswese aan die Universiteit van Stellenbosch bedryf word. Hierdie tesis lê ’n fondasie vir toekomstige navorsing oor gevorderde magnetometrie met SQUIDs.