Space weather prediction is moving from an era of pure curiosity-driven research into an era of 24/7 operations.
The interest in space weather forecasts has never been greater, with society becoming ever more reliant upon technology and infrastructure which are potentially at risk. Amongst space weather hazards, geomagnetic storms are potential threats to power-grid infrastructure, communication systems and oil and gas operations.
Geomagnetic indices capture the severity of magnetic storms by summarising magnetic activity at spatially disparate locations. They have become almost ubiquitous as parameterisations of storm-time magnetic conditions and are required inputs for radiation belt,ionospheric and neutral atmosphere models.
We present the first results from a study aiming to provide operational geomagnetic index prediction that is: robust and reliable, has high cadence, runs fast enough for real-time operations , and is accurate forecasting up to three days ahead. The predictive power of autoregressive and machine-learning techniques applied to combinations of solar, solar wind and geomagnetic data is investigated.
The predictions presented will ultimately form part of the British Geological Survey’s space weather forecast operations
