The solar wind is a continuous outward expansion of charged particles generated from the Sun’s corona. It fills the entire interplanetary space and plays a major role in space weather. The interplanetary space plasma environment is governed by nonlinear dynamical processes over a wide range of spatiotemporal scales. Previous studies have pointed out the important role of the Alfvén waves in the acceleration of the solar wind from the solar corona but the physical mechanisms accelerating the solar wind have not been understood yet. In this work, we focus on investigating the nonlinear behavior of Alfvén waves in the solar wind as well as their role in accelerating and heating the plasma. Based on this work, we discuss the possible generation mechanism of Alfvénic solar wind and present analysis on solar wind Alfvénicity over several years encompassing solar cycles 23 and 24 in order to foresee what should be expected for solar cycle 25. We also introduce a machine learning approach that implements a new prediction interval forecast method to give advanced notice of the arrival of the interaction regions. The interaction region is formed due to the interaction between the fast and preceding slow solar winds. This approach is important to protect the satellites, communication systems, and infrastructures on which our society depends. The adapted machine learning model is tailored for the Space Weather community, but applicable to many other communities. On the Sun’s surface, the fine structure of the stripes-pattern radio burst sources is not observable by satellites, and discriminating between their sources is not easy. In this work, a new analytical study is carried out to identify the plasma characteristics of fiber- and zebra-pattern emission sources without an underlying density or magnetic model. The study suggests the conditions for generating different patterns and provides a numerical technique for estimating their plasma parameters. Finally, based on our knowledge of an existing tool for studying the behavior of the active regions on the Sun’s surface, we created ARTop open-source framework that provides new tools for analyzing the active region topology on the Sun’s surface. The outcomes are useful for robust prediction of flares and CMEs. This code offers researchers a powerful tool for investigating the behavior of active regions and the origins of space weather.
Analysis of solar transients for space weather forecasting
Darwish, K. A. (Author). 2023
Student thesis: Doctoral Thesis › Doctor of Philosophy