Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields.

Magnetospheric Imaging: Understanding the Space Environment through Global Measurements is a state-of-the-art resource on new and advanced techniques and technologies used in measuring and examining the space environment on a global scale. Chapters detail this emergent field by exploring optical imaging, ultraviolet imaging, energetic neutral atom imaging, X-ray imaging, radio frequency imaging, and magnetic field imaging. Each technique is clearly described, with details about the technologies involved, how they work, and both their opportunities and limitations. Magnetospheric imaging is still a relatively young capability in magnetospheric research, hence this book is an ideal resource on this burgeoning field of study. This book is a comprehensive resource for understanding where the field stands, as well as providing a stepping stone for continued advancement of the field, from developing new techniques, to applying techniques on other planetary bodies.