Research Article

Probabilistic prediction of geomagnetic storms and the K_p index

¹ Bradley Department of Electrical & Computer Engineering, Virginia Tech, Blacksburg, 24061 VA, USA
² Space Science and Applications (ISR-1), Los Alamos National Laboratory, Los Alamos, 87545 NM, USA

^* Corresponding author: shibaji7@vt.edu

Received: 23 December 2019
Accepted: 4 July 2020

Abstract

Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index K _p in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic K _p predictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-ahead K _p prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (K _p  ≥ 5⁻) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.