Probabilistic prediction of geomagnetic storms and the Kp index

• Main Authors: Chakraborty Shibaji, Morley Steven Karl
• Format: Article
• Language: English
• Published: EDP Sciences 2020-01-01
• Series: Journal of Space Weather and Space Climate
• Subjects: geomagnetic storms; k p forecasting; deep learning; lstm; gaussian process
• Online Access: https://www.swsc-journal.org/articles/swsc/full_html/2020/01/swsc190086/swsc190086.html

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.