Space weather is a term used to describe hazardous events in the near-Earth space environment that can have adverse effects. Power grids, telecommunication infrastructure and space assets show significant vulnerability to space weather events originating from the Sun. While these effects are largely invisible to the naked eye, in the 21st century operations in space and on the ground significantly depend on the accurate knowledge and forecast of the conditions in the space environment.

Using ground observations of the Sun and physics-based models of the Solar System, it is possible to quantify space weather hazards (e.g., solar wind speed and density) and risks on Earth’s technology. The output of this method provides an estimate of the desired variables. These methods, however do not utilize the vast amount of observations that are available from space, and they are computationally demanding and dominantly physics-based, making them difficult to run in real-time and use all available measurements.

We propose to leverage the growing number of highly detailed multi-spectral images of the Sun to improve predictions of the solar wind streams arriving at the Earth. We propose to exploit the capabilities of modern computer vision and machine learning (ML) techniques to register solar images, analyze them and assimilate them in an empirical (data-driven) model. Through our approach we will develop a novel, data-driven framework directly connecting solar disturbances to its consequences for space weather.