Probabilistic Prediction of Geomagnetic Storms and the K$_{textrm{p}}$ Index

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 $text{K}_text{p}$ in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic $text{K}_text{p}$ 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 $text{K}_text{p}$ 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 ($text{K}_text{p}geq 5^-$) 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.