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Joint Study Group

Space Weather Monitoring and Prediction

Led by GGOS; joint with IAG Commission 4, Sub-Commission 4.3

Chair: Haixia Lyu (China)
Vice-Chair: Benedikt Soja (Switzerland)

Summary

Space weather refers to the dynamic conditions in the Earth’s outer space environment as influenced by the sun and the solar wind. It can affect different systems with increasing human activity reliance, such as satellite navigation systems, communication systems, power grids, etc. In the 25th solar cycle, characterized by heightened solar activity, the occurrence of intense coronal mass ejections (CMEs) and solar flares in 2023 underscores the critical importance of space weather monitoring and prediction. It is still challenging to monitor space weather in real time due to the inherent complexity and unpredictability of solar-terrestrial interactions, limitations in observational capabilities, and the computational demands of processing vast amounts of data in a timely manner. The JSG3 will focus on combining spaceborne observation systems and ground-based observations to better understand the process of space weather events and their effect on the near-Earth environment, developing space weather forecasting methods or models, trying to give more accurate prediction for ionospheric variations. The synergy with JSG1, JSG4 and JSG AI for GNSS Remote Sensing of the new Focus Area – AI for Geodesy is promoted by identifying connected key questions to achieve a comprehensive understanding of the impact of space weather events.

Objectives

The main objectives are:

  • Comparison and inter-validation among different space-based and ground-based observations of space weather events
  • Investigate in detail the impact of different space weather information on TEC predictions
  • Study the impact of selected geomagnetic storms on ionospheric modeling and GNSS positioning performance
  • Development of AI and Data Fusion techniques for improving real-time space weather prediction capabilities

Activities

Planned activities:

  • Select space weather events with available dedicated space-based and ground-based observations; create data archives for comparison
  • Select representative geomagnetic storms
  • Analyse TEC prediction results with different space weather information as input
  • Synergy with JSG1 in the impact study of geomagnetic storms on ionospheric modelling and GNSS positioning
  • Synergy with the Focus Area AI for Geodesy in real-time space weather prediction studies.

List of publications

Schunk, Robert Walter, Ludger Scherliess, Vince Eccles, Larry C. Gardner, Jan Josef Sojka, Lie Zhu, Xiaoqing Pi et al. (2021), Challenges in Specifying and Predicting Space Weather, Space Weather 19, no. 2 (2021): e2019SW002404.

Verkhoglyadova, O., X. Meng, A. J. Mannucci, J-S. Shim, R. McGranaghan (2020), Evaluation of Total Electron Content Prediction Using Three Ionosphere‐Thermosphere Models, Space Weather 18, no. 9 (2020): e2020SW002452.

Aroca-Farrerons, J., Hernández-Pajares, M., Lyu, H. (2022), Can the GEC be used as Space Weather index? Oral presentation in the 21st International Beacon Satellite Symposium, Boston, USA: 1-5 August 2022

Yan, F., Wu, Z., Shang, Z., Wang, B., Zhang, L., Chen, Y. (2022). The First Flare Observation with a New Solar Microwave Spectrometer Working in 35–40 GHz. The Astrophysical Journal Letters, 942(1), L11.

Tsagouri, I., Belehaki, A. (2022). Assessment of solar wind driven ionospheric storm forecasts: the case of the Solar Wind driven autoregression model for Ionospheric Forecast (SWIF). Advances in Space Research.

Vaishnav, R., Jacobi, C., Berdermann, J., Schmölter, E., Codrescu, M. (2022). Delayed ionospheric response to solar extreme ultraviolet radiation variations: A modeling approach. Advances in Space Research, 69(6), 2460-2476.

Abed, A. K., Qahwaji, R., Abed, A. (2021). The automated prediction of solar flares from SDO images using deep learning. Advances in Space Research, 67(8), 2544-2557.

  1. Natras, B. Soja, M. Schmidt (2022): “Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting”; Remote Sensing 14(15):3547 https://doi.org/10.3390/rs14153547