Investigating Solar Drivers of Space Weather

Sinha, Suvadip (2023) Investigating Solar Drivers of Space Weather. PhD thesis, Indian Institute of Science Education and Research Kolkata.

Text (PhD thesis of Suvadip Sinha (16RS006)) 16RS006.pdf - Submitted Version Restricted to Repository staff only Download (17MB)

Abstract

Solar activity modulates the near-Earth space environment – a phenomena known as space weather. Among the different types of solar activity, solar flares, coronal mass ejections, and filament eruptions are significant drivers of space weather. Flares release an immense amount of energy in terms of electromagnetic radiation and are frequently associated with solar energetic particles (SEPs), which travel at relativistic speeds through the heliosphere. They take only a few minutes to reach Earth. Flares can cause adverse space weather impacts on space and ground based technologies. The increased levels of radiation due to SEP can severely impact astronauts’ health and satellite’s sensors if they are not shielded adequately. The other drivers, CMEs, carry magnetized plasma from the Sun to the interplanetary space. While passing through the Earth, CMEs can interact with the Earth’s magnetic field and inject energy and charged particles into Earth’s atmosphere causing adverse space weather condition – known as geomagnetic storms. The ability to produce a geomagnetic storm is referred to geoeffectiveness of a CME. This geoeffectiveness mainly depends on the CME speed, mass, magnetic field strength and orientation of the magnetic field (Bz). Filament eruptions also often lead to flare- CME events and impact space weather. A geomagnetic storm has various consequences on Earth. It induces large currents in the ionosphere and conductors on Earth, e.g., underground pipelines, power transmission cables, etc. These large induced currents can damage transformers which lead to power grid failures, and increase in atmospheric temperature causing an expansion of the upper atmosphere. The atmospheric expansion imposes a higher drag on satellites, which can deorbit satellites in extreme cases. With the advancement of space reliant technologies, we are becoming more and more exposed to space weather consequences. Therefore predicting space weather is crucial for our preparedness for geomagnetic storms and other hazardous effects of extreme space weather. The main challenge in predicting space weather is our lack of understanding of the triggering mechanism for major space weather drivers, i.e., the flare-CME events. Hence prior knowledge of an imminent flare/CME event is considered to be critical in safeguarding our technological assets to mitigate significant economic With the aim of addressing the aforementioned concerns, we present this thesis, which involves the forecasting of space weather events via analysing its drivers. By studying filaments during their eruptive phase and estimating magnetic field properties of solar active regions (ARs), we search for indications of any upcoming flare and CME events. We begin with a discussion on space weather impacts and the sun’s activities that drive space weather in Chapter 1. We also cover a few historical geomagnetic events that highlight the importance of space weather. In Chapter 2, we describe an automated detection algorithm for solar filaments. This algorithm identifies solar filaments present in full-disk Hα images based on intensity thresholding. Tracking a filament throughout its eruption process can give us the exact time of eruption. As solar filaments are often associated with flares and CMEs, their automated detection in real-time gives us an advantage in assessing space weather. We use the filament detection algorithm to explore the connection between filament eruption and flare-CME events. We study 33 filament eruption events. Monitoring how the filament area changes during an eruption, we constrain the initiation time of the filament eruption process and show a statistical connection with the onset time of associated flare and CME events. These connections are reported in Chapter 3. The result indicates that filament eruption generally precedes the flare peak time. Also, an eruptive filament can hint at a CME before its appearance in the coronagraph field of view. Therefore filament eruptions can be treated as precursors for the two significant drivers of space weather. For quiescent filaments, the decay rate of the filament area gives us an idea about the strength of the driving mechanism behind the eruption. We obtain a linear correlation of 0.75 between filament decay rate (for quiescent filaments) and associated CME speed. Although filament eruption can provide useful information on associated flares, flares are not always observed in conjunction with filament eruptions. Flares most often occur in the intense magnetic field regions on the solar surface called ARs. The magnetic properties of solar ARs can be obtained from vector magnetogram data with SDO/HMI observations. Characterizing each AR with a set of magnetic parameters, we try to identify the flare-prone regions using various machine learning (ML) algorithms. The usefulness of the ML algorithm is that it can easily handle multidimensional data. The implication of ML suits our purpose as ARs are characterized by various magnetic parameters, and the triggering processes is not fully understood. Our study constrains the best ML algorithm for identifying ARs that are most likely to produce intense flares within the next 24 hours. Our result shows that logistic regression and support vector machine perform quite well for this AR classification task with a high true skill score above 0.95. Our study sheds light on the essential magnetic parameters which distinguish flare-prone ARs. This research is presented in Chapter 4. Unlike flares, CMEs take a longer time to reach Earth, typically ∼ 2 − 4 days. The travel time depends on the CME speed and other heliospheric conditions (like, solar wind speed). An accurate prediction of CME arrival time and its geoeffectiveness can provide us valuable time to take preventive actions to minimize losses. Kinematic analysis with multi-spacecraft observations provides 3D speed and propagation direction of CMEs, which can be used with drag based ensemble model (DBEM) to estimate CME arrival times. The lower coronal source signatures often tell us crucial information about the geoeffectiveness of the CME. Recent studies show that some CMEs do not have a clear source signature on the solar disk, and can sometimes remain invisible in coronagraph images but impact space weather conditions at Earth. These CMEs are termed as stealth CMEs. The geomagnetic storms driven by these stealth CMEs are hard to predict and often called “problem geomagnetic storms”. Advanced image processing techniques, along with high-resolution EUV observations from SDO/AIA, STEREO/EUVI enable us to find faint source signatures for stealth events. In Chapter 5, we report a few stealth CME events and explore their source regions with the above mentioned methods. The main objective of this thesis is to predict extreme space weather conditions. We believe the topics covered in this thesis – identifying the precursors of these major space weather drivers, magnetic characteristics of flare-prone active regions, revealing source signatures, and estimating CME arrival time contribute significantly to space weather forecasting. Chapter 6 concludes the thesis with brief discussions and describes future pursuit based upon our work. The research work of this thesis has been reported in the four publications listed below. Chapters 3 and 4 mainly reproduce publications 1 and 2, respectively, with minor changes wherever appropriate. In items 3 and 4 in the publication list, the author collaborated with an ISSI team and contributed to the geometrical fitting of CME flux rope and identified possible source signatures. The author’s contributions are summarized in Chapter 5 with additional details.

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