Student: Sheng Li
Supervisor: Gilbert Pi, Ph.D.
ConsultantProf. RNDr. František Němec, Ph.D.
Status: Available
Abstract:
The principal task of the present effort in solar-terrestrial physics is the transport of solar wind particles and energy into the magnetosphere with a motivation to contribute to the reliability of space weather predictions. However, these processes are highly dynamic due to varying upstream conditions. For accurate and timely space weather forecasting, advanced knowledge of the ambient solar wind is required, both for its direct impact on the magnetosphere and for accurately forecasting the propagation of large solar wind structures to Earth (e.g., ICMEs, CIRs). With a space weather monitor in the L5 Lagrange point (e.g., using the STEREO spacecraft or possible new planned probes to L5) and with the remote observations of the Sun (applying the Solar Orbiter surface image), we can quantify the solar wind forecast outputs and compare them with existing observations at the L1 point (ACE, Wind, DISCOVR, THEMIS, MMS).
In the proposed (Ph.D.) topic, the student will analyze all the above-mentioned data sources and focus on building a model using different techniques (including machine learning). The aim is to directly predict the arrival of space weather agents, thereby improving our knowledge of the evolution of the solar wind in the heliosphere.
………………………………………………………………..Literature:
[1] Turner, H., Lang, M., Owens, M., Smith, A., Riley, P., Marsh, M., & Gonzi, S. (2023). Solar wind data assimilation in an operational context: Use of near-real-time data and the forecast value of an L5 monitor. Space Weather, 21, e2023SW003457. https://doi.org/10.1029/2023SW003457
[2] Mishra, W. and Teriaca L. (2023), Propagation of coronal mass ejections from the Sun to the Earth, Journal of Astrophysics and Astronomy, 44 (1), DOI: 10.1007/s12036-023-09910-6
[3] A Statistical Study of coronal mass ejections for solar cycle 23 (Chinese), Master thesis, NCU, 2005
[4] Lin, R.-P., Y. Yang, F. Shen, G. Pi, and Y. Li (2024), An Algorithm For Determination of CME Kinematic
Parameters Based On Machine Learning, ApJS, accepted.
[5] Nafchi, M.A., F. Němec , G. Pi, Z. Němeček, J. Šafranková, K. Grygorov, J. Šimůnek, T.-C. Tsai (2024), Magnetopause Location Modeling Using Machine Learning: Inaccuracy Due to Solar Wind Parameter Propagation, Frontiers in Astronomy and Space Sciences, submitted.