Aeronautics, Space and Transport, Key Technologies

Solar Image-based Modelling

“A few years ago, when anyone would ask me why research into space weather was so important, I would answer because there are nearly 1,000 satellites up there in space,” says Prof. Dr. Yuri Shprits of the GFZ German Research Centre for Geosciences. “Now, there are already 3,000 and, by the end of the decade, there are expected to be 10,000.”

They are all potentially threatened by hazardous events in near-earth space, such as solar wind or geomagnetic storms. These can cause disruptions or even complete failures of satellite navigation systems, which are becoming increasingly important in connection with autonomous driving, and the same goes for telecommunication technologies and even power grids on earth.

“Currently, solar wind conditions are predicted using physics-based models that are too massive and computationally expensive to be run in real-time,” Shprits explains. The project he and his colleagues are working on aims to change all this. Working from telescopic images of the sun, an artificial intelligence will predict how the space weather will develop after solar flares, for example, have been observed.

The ALT Tag

The novelty is that this system will take detailed data captured from space and analyse it with machine learning algorithms. The aim is to deliver automated forecasts with ample warning, before the effects of processes observed on the sun eventually reach near-earth space. “This way, we avoid having to use the extremely complicated models, with which it can take months to model a solar storm. With machine learning, we want to make it possible for a computer to make its own forecasts automatically from solar images,” Yuri Shprits says. Some satellites could then be switched into “safe mode” and planned manoeuvers or software updates rescheduled as a precaution.

Project Partners


Prof. Dr. Yuri Shprits,
Dr. Marcus Paradies,
Dr. Jenia Jitsev,
Melanie Burns

Duration: 24 months

Primary Contact

Prof. Dr. Yuri Shprits