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. Thus, research is required to yield a better understanding of flare occurrence and enable an accurate flare forecasting. This work 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 work is to achieve the followings: 1) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions and an investigation of this measurement in relation to flares. 2) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART to enable the design of a new flare prediction system. 3) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding flare occurrence.

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

This study looked at observational and theoretical studies of flare physics, at quests for flare precursors, and at mathematical models for combining masses of predictive information. We also looked at the worldwide effort to gather and share timely data and combine it with knowledge and experience to forecast solar flares and their effects. Topics include: Long-lived, large-scale magnetic and velocity fields; Magnetic-energy buildup in an active region; Flare initiation; Flare precursors -- Filament activation, Preflare brightening, Magnetic shear, and Emerging and cancelling magnetic flux; Quantitative prediction; Operational solar flare prediction; Forecast evaluation.

The report describes research on ways to improve the performance of solar patrol observers, personnel who monitor the condition and activity of the sun through optical telescopes. Section 1 explains the need for visual monitoring to detect, evaluate, and predict solar flares. Section 2 discusses the need for increased objectivity and precision in sensory and perceptual evaluations for solar flare prediction. Section 3 is a report of research based on the foregoing rationale. (Modified author abstract).

This volume is dedicated to the Solar Dynamics Observatory (SDO), which was launched 11 February 2010. The articles focus on the spacecraft and its instruments: the Atmospheric Imaging Assembly (AIA), the Extreme Ultraviolet Variability Experiment (EVE), and the Helioseismic and Magnetic Imager (HMI). Articles within also describe calibration results and data processing pipelines that are critical to understanding the data and products, concluding with a description of the successful Education and Public Outreach activities. This book is geared towards anyone interested in using the unprecedented data from SDO, whether for fundamental heliophysics research, space weather modeling and forecasting, or educational purposes. Previously published in Solar Physics journal, Vol. 275/1-2, 2012. Selected articles in this book are published open access under a CC BY-NC 2.5 license at link.springer.com. For further details, please see the license information in the chapters.