To build their model, researchers at Predictive Science use measurements of the Sun’s changing magnetic field at the solar surface to drive their model in near real-time. A key to this innovation was creating an automated process that converts raw data from SDO to show how magnetic flux and energy are injected into the corona over time. Adding this dynamic into the model allows the corona to evolve over time, leading to solar eruptions. “We developed a software pipeline that took in the magnetic field maps, picked out all of the areas that should be energized, and then fine-tuned the amount of energy to add to those areas,” Mason said. Building this automatic pipeline was a huge step forward for the team. In past predictions, the model used a static snapshot of the surface magnetic field – not ideal for keeping up with the ever-changing Sun, especially during our current heightened period of solar activity. Similarly, in iterations from 2017 and 2021, Mason explained that a teammate used to “literally hand-draw which areas on the Sun needed to be energized” by analyzing extreme ultraviolet activity in certain regions. Continuously updating the magnetic field is central to all of the changes with this year’s model, and the team has high hopes for the results.