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Yes / Solar images, along with other observational data, are very important for solar physicists and space weather researchers aiming to understand the way the Sun works and affects Earth. In this study a 3D modelling technique for visualizing solar magnetic field lines using solar images is presented. Photospheric magnetic field footpoints are detected from magnetogram images and using negative and positive magnetic footpoints, dipole pairs are associated according to their proximity. Then, 3D field line models are built using the calculated dipole coordinates, and mapped to detected pairs after coordinate transformations. Final 3D models are compared to extreme ultraviolet images and existing models and the results of visual comparisons are presented.

La connaissance du flux UltraViolet (UV) solaire et de sa variabilité dans le temps est un problème clé aussi bien dans le domaine de l’aéronomie qu’en physique solaire. Alors que l’extrême UV, entre 10 et 121 nm, est important pour la caractérisation de l’ionosphère, l’UV entre 121 et 300 nm l’est tout autant pour les modélisations climatiques. La mesure continue de l’irradiance dans l’UV est cependant une tâche ardue. En effet, les instruments spatiaux étant dans un environnement hostile se dégradent rapidement. De nombreux modèles basés sur des indices solaires sont alors utilisées lorsque peu de données sont disponibles. Pourtant, l’utilisation de ces indices ne permet pas d’atteindre aujourd’hui une précision suffisante pour les différentes applications en météorologie de l’espace. Comme alternative, ce travail de thèse met en avant l’utilisation de bandes passantes pour reconstruire l’irradiance solaire dans l’UV. En utilisant des méthodes d’analyse statistique multivariée, ce travail met tout d’abord en évidence la forte cohérence de la variabilité spectrale de l’irradiance dans l’UV, ainsi que ses principales caractéristiques. Une première étape consiste à utiliser des bandes passantes existantes afin de tester la faisabilité de notre approche : le flux UV peut ainsi être reconstruit avec une erreur relative d’environ 20%, une bien meilleure performance qu’avec l’utilisation d’indices solaires. Afin de limiter les problèmes de dégradation liés à l’utilisation des filtres, nous proposons un instrument d’un genre nouveau basé uniquement sur des détecteurs à larges bande interdite permettant de sélectionner une bande spectrale (notamment pour l’UV à partir de 120 nm). Un tel radiomètre permettrait de reconstruire les raies spectrales importantes pour la spécification de la thermosphère terrestre avec une bonne précision. Enfin, une modélisation de l’impact du flux UV solaire sur l’atmosphère de Ganymède est exposée. Les émissions atmosphériques pour quelques espèces sont alors calculées, afin de proposer quelques recommandations pour les futures missions pour Jupiter. / The knowledge of the solar spectral irradiance in the UV and its variation in time is a key problem in aeronomy but also in climatology and in solar physics. While the Extreme UV (10-121 nm) range is important for thermosphere/ionosphere specification, the Far UV and Middle UV ranges are essential for climate modelling. However, the continuous monitoring of the UV irradiance is a difficult task. Space instruments are indeed suffering from ageing but also signal contamination of many kinds. Because of the lack of long-term measurements of the whole UV range, most thermosphere/ionosphere and climate models rely today on proxies for the solar irradiance, which may however not reflect very well the variability. As an alternative, we proposed in this work to use a few radiometers with properly chosen passbands in order to reconstruct the solar UV irradiance. Using a multivariate statistical approach, we first characterize the high redundancy as well as the different features of the solar UV irradiance. With four passbands from already existing instrument, we test our concept : the solar UV flux is reconstructed with a relative error of about 20%. This work proposes then to define a new kind of instrument, which may use wide bandgap materials as detectors selecting moreover the spectral range without using filters. Filters are indeed very sensitive to the degradation. This new instrument could reconstruct very well some spectral lines important to the Earth thermosphere specification. This thesis finally proposes to model the impact of the solar UV flux on the atmosphere of Ganymede. We predict some atmospheric emissions in the framework of future space mission to Jupiter.

This thesis aims to provide public science dissemination of space weather data by integrating a space weather analysis system used by experts in the field into an interactive visualization software called OpenSpace; designed to visualize the entire known Universe. Data and images from complex space weather models were processed and used as textures on different surface geometries, which are then positioned, oriented and scaled correctly relative other planets in the solar system. The obtained results were within the goals of the thesis and has successfully incorporated several features that will help understanding of space weather phenomena.

Energetic electrons undergo significant flux variations in the Earth’s outer radiation belt, where magnetospheric waves play an important role in changing the energetic electron dynamics. In particular, electromagnetic ion cyclotron (EMIC) waves are suggested to drive efficient pitch angle scattering of relativistic electrons, which results in relativistic electron precipitation into the upper atmosphere. Such precipitation provides an important source of energy input into the upper atmosphere, where precipitating electrons can affect atmospheric chemistry and ionization. However, the quantitative role of EMIC waves in energetic electron precipitation in various regions of the magnetosphere is not fully understood. This dissertation aims to answer outstanding open questions on the characteristics and quantification of EMIC-driven precipitation, such as the spatial extent and the energy range of electron precipitation. The relationship between EMIC waves and electron precipitation is evaluated by analyzing magnetic conjunction events when EMIC waves are detected in the magnetosphere by near-equatorial satellites (Van Allen Probes, GOES) and precipitating electrons are measured by Low-Earth-Orbiting satellites (POES, FIREBIRD). Quasi-linear theory is used to quantify the role of various observed magnetospheric waves (e.g., EMIC waves, plasmaspheric hiss, magnetosonic waves) in the electron precipitation. Several in-depth case analyses show that EMIC waves are the main driver of the observed relativistic electron precipitation, while other waves play a minor role. The precipitation events were clearly identified within L shell of ~7.5, favorably near the dusk and night sectors. The analysis shows that each precipitation event was localized on average spatial scales of ~0.3 L, suggesting that the resonance conditions are satisfied in a very localized region of the magnetosphere. The electron precipitation was observed at the expected relativistic (> ~MeV) energies; however, the minimum energy of efficient electron precipitation was newly found to extend down to at least ~200–300 keV. The quantitative analysis using multi-point measurements combined with theoretical calculations in this dissertation provides a more comprehensive understanding of EMIC-driven precipitation, which is a critical electron loss process in the magnetosphere. Moreover, the results are helpful to improve currently existing models of radiation belt, ring current and atmosphere dynamics, as well as theories of wave-particle interactions.

Yes / In this study a method for 3D representation of active regions and sunspots that are detected from Solar and Heliospheric Observatory/Michelson Doppler Imager magnetogram and continuum images is provided. This is our first attempt to create a visual solar catalogue. Because of the difficulty of providing a full description of data in text based catalogues, it can be more accurate and effective for scientist to search 3D solar feature models and descriptions at the same time in such a visual solar catalogue. This catalogue would improve interpretation of solar images, since it would allow us to extract data embedded in various solar images and visualize it at the same time. In this work, active regions that are detected from magnetogram images and sunspots that are detected from continuum images are represented in 3D coordinates. Also their properties extracted from text based catalogues are represented at the same time in 3D environment. This is the first step for creating a 3D solar feature catalogue where automatically detected solar features will be presented visually together with their properties.

Yes / Solar imaging is currently an active area of research. A fast hybrid system for the automated detection of filaments in solar images is presented in this paper. The system includes three major stages. The central solar region is detected in the first stage using integral projections. Intensity filtering and image enhancement techniques are implemented in the second stage to enhance the quality of detection in the central region. Local detection windows are implemented in the third stage to detect the positions of filaments and to define various sized arrays to contain them. The extracted arrays are fed later to a neural network for verification purposes.

Yes / In this work we study the association between eruptive filaments/prominences and coronal mass ejections (CMEs) using machine learning-based algorithms that analyse the solar data available between January 1996 and December 2001. The Support Vector Machine (SVM) learning algorithm is used for the purpose of knowledge extraction from the association results. The aim is to identify patterns of associations that can be represented using SVM learning rules for the subsequent use in near real-time and reliable CME prediction systems. Timing and location data in the NGDC filament catalogue and the SOHO/LASCO CME catalogue are processed to associate filaments with CMEs. In the previous studies which classified CMEs into gradual and impulsive CMEs, the associations were refined based on CME speed and acceleration. Then the associated pairs were refined manually to increase the accuracy of the training dataset. In the current study, a data- mining system has been created to process and associate filament and CME data, which are arranged in numerical training vectors. Then the data are fed to SVMs to extract the embedded knowledge and provide the learning rules that could have the potential, in the future, to provide automated predictions of CMEs. The features representing the event time (average of the start and end times), duration, type and extent of the filaments are extracted from all the associated and not-associated filaments and converted to a numerical format that is suitable for SVM use. Several validation and verification methods are used on the extracted dataset to determine if CMEs can be predicted solely and efficiently based on the associated filaments. More than 14000 experiments are carried out to optimise the SVM and determine the input features that provide the best performance.

Yes / A web-based prototype system for predicting solar energetic particle (SEP) events and solar flares for use by space launch operators is presented. The system has been developed as a result of the European Space Agency (ESA) project SEPsFLAREs (Solar Events Prediction system For space LAunch Risk Estimation). The system consists of several modules covering the prediction of solar flares and early SEP Warnings (labeled Warning tool), the prediction of SEP event occurrence and onset, and the prediction of SEP event peak and duration. In addition, the system acquires data for solar flare nowcasting from Global Navigation Satellite Systems (GNSS)-based techniques (GNSS Solar Flare Detector, GSFLAD and the Sunlit Ionosphere Sudden Total Electron Content Enhancement Detector, SISTED) as additional independent products that may also prove useful for space launch operators. / This work has been developed in the frame of 34 SEPsFLAREs project (ESA Contract Number 4000109626/13/NL/ 35 AK), which is an activity funded by ESA/ESTEC Space Environ- 36 ment (TEC-EES) section. The authors of this work are grateful to 37 ESA’s MONITOR project (Contract Number 4000100988/2010/F/ 38 WE) for allowing the use of GSFLAD and SISTED products. 39 We also thank AGAUR (Generalitat de Catalunya) for the financial 40 support from Grant PDJ 2014 00074.

A study of large solar storms in the equinox periods of solar cycles 23 and 24 is presented to quantify their effects on the total electron content (TEC) in the ionosphere. We study the dependence of TEC over the contiguous US on various storm parameters, including the onset time of the storm, the duration of the storm, its intensity, and the rate of change of the ring current response. These parameters are inferred autonomously and compared to TEC values obtained from the CORS network of GPS stations. To quantify the effects we examine the difference between the storm-time TEC value and an average from 5 quiet days during the same month. These values are studied over a grid with 1 deg x 1 deg spatial resolution in latitude and longitude over the US sector. Correlations between storm parameters and the quantified delta TEC values are studied using machine learning techniques to identify the most important controlling variables. The weights inferred by the algorithm for each input variable show their importance to the resultant TEC change. The results of this work are compared to recent TEC studies to investigate the effects of large storms on the distribution of ionospheric density over large spatial and temporal scales. / MS / This study analyzes the impact of geomagnetic storms on the electrical properties of the upper atmosphere at altitudes where satellites routinely fly. The storms are caused by bursts of charged particles from the sun entering the Earth’s atmosphere at high latitudes, leading to phenomena like the aurora. These fluctuations in the atmospheric electrical properties can potentially have serious consequences for the electrical power grid, the communications infrastructure, and various technological systems. Given the risks solar storms can pose, it is important to predict how strong the impact of a given storm is likely to be. The current study applies machine learning techniques to model one particular parameter that relates to the electrified atmosphere over the contiguous US sector. We quantify the strength of the fluctuations as a function of various storm parameters, including onset time and duration. This enables us to autonomously infer which storm parameters have the most significant influence on the resultant atmospheric changes, and compare our results to other recent studies.