Solar flare prediction is a valuable and sought after commodity for the safety of astronauts and satellites. Our work takes an image processing approach to solving this problem using Improved Solar Observing Optical Network (ISOON) images to produce a robust flare prediction algorithm. Our algorithm operates on images of the chromosphere which is associated with the H[alpha] wavelength. We detect solar structures of importance to flare prediction namely filaments and active regions, then extract their features. Using these features, we use a classification method known as anomaly detection which is not affected by an imbalanced dataset. Our results are highly successful when filament features are paired with features of active regions. We speculate the combination of active regions and filament features produce a catalyst to successfully predict solar flares. We also have data indicating that feature selection within anomaly detection may produce higher accuracies.

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.

As we rely more on satellites, communication systems and space research, the importance of space weather is increasing continuously. There are many space missions and ground based observatories providing continuous observation of the Sun at many different wavelengths to supply the demand for space weather forecast and research. All the forecasting strategies highly depend on experience of solar physicists and done manually. The results differ from observatories to observatories and subjective. There is a need for automated analysis of Sun and space weather forecasting. The solar activity is the driver of space weather. Thus it is important to be able to predict the violent eruptions such as coronal mass ejections and solar flares. In this book a hybrid system combining image processing and machine learning techniques for the automated short-term prediction of solar flares is presented. The system can also detect, group, and classify sunspots using solar images. The algorithms, implementation, and results are explained in this work.

The relationship between various measurable solar parameters and solar-flare occurrence is examined utilizing a comprehensive solar-geophysical data base containing a variety of objectively-correlated solar measurements. The sample covers the period from January 1955 through February 1968 and includes such parameters as solar flares, sunspots, magnetic fields of sunspots, calcium plages and 9.1 cm radio brightness temperatures. A statistical analysis was performed to determine the parameters most useful for the prediction of solar flares 24 hours in advance. Persistence was identified as the single most important flare predictor, with sunspot magnetic classification, 9.1 cm radio brightness temperature, plage brightness and sunspot area also selected as useful predictors. Objective flare probability prediction equations were developed that incorporate all useful predictors simultaneously. (Author).

Over the last decade we entered a new exploration phase of solar flare physics, equipped with powerful spacecraft such as Yohkoh, SoHO, and TRACE that pro vide us detail-rich and high-resolution images of solar flares in soft X-rays, hard X -rays, and extreme-ultraviolet wavelengths. Moreover, the large-area and high sensitivity detectors on the Compton GRO spacecraft recorded an unprecedented number of high-energy photons from solar flares that surpasses all detected high energy sources taken together from the rest of the universe, for which CGRO was mainly designed to explore. However, morphological descriptions of these beau tiful pictures and statistical catalogs of these huge archives of solar data would not convey us much understanding of the underlying physics, if we would not set out to quantify physical parameters from these data and would not subject these measurements to theoretical models. Historically, there has always been an unsatisfactory gap between traditional astronomy that dutifully describes the mor phology of observations, and the newer approach of astrophysics, which starts with physical concepts from first principles and analyzes astronomical data with the goal to confirm or disprove theoretical models. In this review we attempt to bridge this yawning gap and aim to present the recent developments in solar flare high-energy physics from a physical point of view, structuring the observations and analysis results according to physical processes, such as particle acceleration, propagation, energy loss, kinematics, and radiation signatures.

This is a summary of the ten years (1955-1964) of observations of solar flares reported by 61 observatories. The flare reports have been published predominantly in the IAU Bulletin and CRPL-F Series Part B. This report summarizes the types of information contained in the flare reports, and the frequency of some parts of these reports as functions of many parameters. The results reflect the need for closer cooperation and coordination of participating observatories in recording and reporting solar flare data.

An extensive data base consisting of VLF records for 130 different propagation paths over a 2-year period was analyzed statistically to study the correlation of VLF propagation changes with solar X-ray events recorded by satellites. Results of the study indicate that with reasonable restrictions on path illumination conditions, it is possible to detect and classify solar X-ray flares, in near realtime, by proper interpretation of VLF phase and amplitude variations. A method of calibrating VLF paths for this purpose is discussed, and a possible global network of receiving stations capable of 24-hr per day monitoring of solar X-rays is described.