This volume gives a comprehensive and integrated overview of current knowledge and understanding of corotating interaction regions (CIRs) in the solar wind. It is the result of a workshop at ISSI, where space scientists involved in the Ulysses, Pioneer, Voyager, IMP-8, Wind, and SOHO missions exchanged their data and interpretations with theorists in the fields of solar and heliospheric physics. The book provides a broad synthesis of current understanding of CIRs, which form at the interface between the fast solar wind originating in the northern and southern coronal holes and the slow solar wind that originates near and within coronal streamers surrounding the heliomagnetic equator. CIRs are the dominant structure in the heliosphere near and beyond Earth on the declining phase and near the minimum of the 11-year solar activity cycle. Particles energized at the shocks that bound CIRs at heliospheric distances beyond the orbit of Earth are the dominant energetic particle population observed in the outer heliosphere at these times. Papers included in this volume cover the subject of CIRs from their dissipation in the outer hemisphere, and include discussions of complexities associated with their evolution with distance from the Sun, their three-dimensional structure, and the myriad effects that CIRs have on energetic particles throughout the heliosphere. The book is intended to provide scientists active in space physics research with an up-to-date status report on current understanding of CIRs and their effects in the heliosphere, and also to serve the advanced graduate student with introductory material on this active field of research.

Past attempts to explain the large solar wind velocities in high speed streams by theoretical models of the expansion have invoked either extended nonthermal heating of the corona, heat flux inhibition, or direct addition of momentum to the expanding coronal plasma. Several workers have shown that inhibiting the heat flux at low coronal densities is probably not adequate to explain quantitatively the observed plasma velocities in high speed streams. It stressed that, in order to account for both these large plasma velocities and the low densities found in coronal holes (from which most high speed streams are believed to emanate), extended heating by itself will not suffice. One needs a nonthermal mechanism to provide the bulk acceleration of the high wind plasma close to the sun, and the most likely candidate at present is direct addition of the momentum carried by outward-propagating waves to the expanding corona. Some form of momentum addition appears to be absolutely necessary if one hopes to build quantitatively self-consistent models of coronal holes and high speed solar wind streams.

This report has correlated the positions of high latitude coronal holes as determined from the Helium 10830 A spectroheliograms, with the velocities of solar wind streams high above or below the ecliptic, which could have originated from the same positions as the coronal holes. The solar wind speeds were determined from interplanetary scintillation (IPS) measurements made at the University of California at San Diego and at the Toyokawa Observatory in Japan. The correlation covered the two and a half year period, January 1, 1977 to June 30, 1979, during which there were no large equatorial coronal holes present, since we were approaching solar maximum. We have found that these high latitude coronal holes are often, but not always, correlated to high speed solar wind streams. The lack of a clearer correlation can be attributed to uncertainties in the solar wind velocities obtained from IPS measurements, to uncertainties in the exact boundaries of the coronal holes, and to the deflection or attenuation of relatively weak solar wind streams in interplanetary space.

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. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms