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

Earth-affecting solar transients encompass a broad range of phenomena, including major solar flares, CMEs, ICMEs, solar energetic particle events, and corotating interaction regions.​ In the past decade, nearly continuous observations of the Sun and the inner heliosphere with an unprecedented wide spatial coverage from a fleet of spacecraft, including STEREO Ahead/Behind, SDO, SOHO, Messenger, Venus Express, ACE and WIND, in combination with a significant advancement of global MHD numerical simulation and theoretical analysis, have greatly improved our understanding of solar transients and the prediction of their potential impact on Earth. This Topical Collection is based on the International Study of Earth-affecting Solar Transients (ISEST) project, initially launched in 2013 to bring together scientists from many countries to join efforts on studying solar transients. ISEST became one of the four research projects of the Variability of the Sun and Its Terrestrial Impact (VarSITI) program, sponsored by the Scientific Committee on Solar-Terrestrial Physics (SCOSTEP) for the period of 2014 – 2018. Originally published in the journal Solar Physics, volumes 292 (2017) and 293 (2018).

This is a completely updated and revised version of a monograph published in 2002 by the NASA History Office under the original title Deep Space Chronicle: A Chronology of Deep Space and Planetary Probes, 1958-2000. This new edition not only adds all events in robotic deep space exploration after 2000 and up to the end of 2016, but it also completely corrects and updates all accounts of missions from 1958 to 2000--Provided by publisher.

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

Examines how solar and terrestrial space phenomena affect sophisticated technological systems Contemporary society relies on sophisticated technologies to manage electricity distribution, communication networks, transportation safety, and myriad other systems. The successful design and operation of both ground-based and space-based systems must consider solar and terrestrial space phenomena and processes. Space Weather Effects and Applications describes the effects of space weather on various present-day technologies and explores how improved instrumentation to measure Earth's space environment can be used to more accurately forecast changes and disruptions. Volume highlights include: Damage and disruption to orbiting satellite equipment by solar particles and cosmic rays Effects of space radiation on aircraft at high altitudes and latitudes Response of radio and radar-based systems to solar bursts Disturbances to the propagation of radio waves caused by space weather How geomagnetic field changes impact ground-based systems such as pipelines Impacts of human exposure to the space radiation environment The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals. Find out more about the Space Physics and Aeronomy collection in this Q&A with the Editors in Chief