J. Space Weather Space Clim.
Volume 10, 2020
Topical Issue - Space Weather research in the Digital Age and across the full data lifecycle
|Number of page(s)
|30 July 2020
Probabilistic prediction of geomagnetic storms and the Kp index
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: email@example.com
Accepted: 4 July 2020
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.
Key words: geomagnetic storms / K p forecasting / deep learning / LSTM / Gaussian process
© S. Chakraborty & S.K. Morley, Published by EDP Sciences 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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