Real-time Space Weather forecasting using satellite data and machine learning

With society's increasing reliance on ground- and space-based technology such as electricity networks, communications and GPS, we are ever more exposed to the risk of disruption from space weather events in the form of solar flares, coronal mass ejections and geomagnetic storms. Although there are a number of real-time monitoring systems in space measuring the solar wind and its magnetic field, forecasting the space weather impacts on both ground-based technology and the satellites essential for observing the Earth, remains problematic.

Aims

There are many potential sources of information which could be used to predict a space weather event including human-made forecasts (e.g. reports issued by the Space Weather Prediction Centre, and the Met Office Space Weather Operations Centre), magnetic field measurements (from the worldwide network of geomagnetic observatories), solar disc imagery and data from geostationary satellites (e.g. the Solar Dynamic Observatory, and the Solar and Heliospheric Observatory). We seek to combine such information to improve the real-time automation of forecasts of space weather between 1 to 72 hours ahead of time, aiding efforts to reduce the space weather hazard to ground- and space-based technological infrastructure. This will be achieved using advanced machine learning techniques including nonlinear time series forecasting, Bayesian modelling, text mining, and image processing. Models of the Earth's magnetic field, built using satellite data (e.g. ESA's Swarm mission) will also be used to better understand the wider context for space weather impacts, for example, the increased risk of electronic damage to satellites in the region of the South Atlantic Anomaly.