Improving Solar Corona Simulations with Advanced Boundary Conditions

The primary objective of this project is to advance the accuracy of solar corona simulations by developing a predictive model for short-term (ideally hourly) solar farside magnetograms. By improving the quality of magnetograms —key input data representing the magnetic field distribution on the solar surface— through the use of modern data processing, the goal is to enhance resolution and reduce noise, thereby significantly improving space weather forecasting. This work will enhance our ability to predict solar events such as flares and coronal mass ejections (CMEs), which is crucial for mitigating their impact on Earth’s technology. The project will also involve the development of high-order numerical solvers for solar corona modeling, aimed at enhancing predictive accuracy and contributing to both foundational research and practical applications in space weather prediction.

Methodology: Model Development: Explore and evaluate different models to identify the most effective approach for short-term magnetogram prediction, focusing on improving data quality. Train and validate the selected model using observational data to generate accurate AI-based magnetograms, ensuring that temporal dynamics are effectively captured. Magnetogram Prediction and Forecast Integration: Use the developed model to predict future magnetograms and include them as input boundary conditions in a solar corona solver. Integrate these predictions into a comprehensive space weather forecasting pipeline, providing continuous and updated magnetogram inputs to enhance heliospheric simulations.