  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.
