Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Smith, A. W., Rae, Jonathan, Forsyth, C., Oliveira, D. M., Freeman, M. P. and Jackson, D. R. (2020) Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning. Space Weather, 18 (11). ISSN 1542-7390

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Official URL: https://doi.org/10.1029/2020SW002603

Abstract

In this study we investigate the ability of several different machine learning models to provide probabilistic predictions as to whether interplanetary shocks observed upstream of the Earth at L1 will lead to immediate (Sudden Commencements, SCs) or longer lasting magnetospheric activity (Storm Sudden Commencements, SSCs). Four models are tested including linear (Logistic Regression), nonlinear (Naive Bayes and Gaussian Process), and ensemble (Random Forest) models and are shown to provide skillful and reliable forecasts of SCs with Brier Skill Scores (BSSs) of ∼0.3 and ROC scores >0.8. The most powerful predictive parameter is found to be the range in the interplanetary magnetic field. The models also produce skillful forecasts of SSCs, though with less reliability than was found for SCs. The BSSs and ROC scores returned are ∼0.21 and 0.82, respectively. The most important parameter for these predictions was found to be the minimum observed BZ. The simple parameterization of the shock was tested by including additional features related to magnetospheric indices and their changes during shock impact, resulting in moderate increases in reliability. Several parameters, such as velocity and density, may be able to be more accurately predicted at a longer lead time, for example, from heliospheric imagery. When the input was limited to the velocity and density the models were found to perform well at forecasting SSCs, though with lower reliability than previously (BSSs ∼ 0.16, ROC Scores ∼ 0.8), Finally, the models were tested with hypothetical extreme data beyond current observations, showing dramatically different extrapolations.

Item Type: Article
Subjects: F300 Physics
F800 Physical and Terrestrial Geographical and Environmental Sciences
F900 Others in Physical Sciences
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 05 Jan 2021 15:16
Last Modified: 05 Jan 2021 15:30
URI: http://nrl.northumbria.ac.uk/id/eprint/45112

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