Probabilistic Forecasts of Storm Sudden Commencements from Interplanetary Shocks using Machine Learning

We acknowledge and thank the Wind and ACE teams for the solar wind data and NASA GSFC's Space Physics Data Facility's CDAWeb service for data availability (https://cdaweb.gsfc.nasa.gov/index.html/). The results presented in the paper also rely on the SC list made available by the International Service on Rapid Magnetic Variations (https://www.obsebre.es/en/rapid) and published by the Observatorio de l'Ebre in association with the International Association of Geomagnetism and Aeronomy (IAGA) and the International Service of Geomagnetic Indices (ISGI). We thank the involved national institutes, the INTERMAGNET network and the ISGI. The authors would like to thank A. A. Samsonov for helpful discussions. This work has also used the interplanetary shock catalog compiled by Oliveira, Arel, et al. (2018), including those intervals identified Wang et al. (2010), and Dr. J. C. Kasper for the Wind (https://www.cfa.harvard.edu/shocks/wi_data/) and ACE data (https://www.cfa.harvard.edu/shocks/ac_master_data/), and also by the ACE team (https://www‐ssg.sr.unh.edu/mag/ace/ACElists/obs_list.html#shocks). It may be found in the supporting information of Oliveira, Arel, et al. (2018). A. W. S. and I. J. R. were supported by STFC Consolidated Grant ST/S000240/1 and NERC grants NE/P017150/1 and NE/V002724/1. C. F. was supported by the NERC Independent Research Fellowship NE/N014480/1 and STFC Consolidated Grant ST/S000240/1. D. M. O. was supported by NASA through grant HISFM18‐HIF (Heliophysics Innovation Fund). The analysis in this paper was performed using python, including the pandas (McKinney, 2010), numpy (van der Walt et al., 2011), scikit‐learn (Pedregosa et al., 2011), scipy (Virtanen et al., 2020) and matplotlib (Hunter, 2007) libraries. Detailed documentation for the models can be found at https://scikit‐learn.org/, while the specific implementations of the models used in this work are: sklearn.linear_model.LogisticRegression, sklearn.naive_bayes.GaussianNB, sklearn.gaussian_process.GaussianProcessClassifier, sklearn.ensemble.RandomForestClassifier. Funding Information: National Aeronautics and Space Administration (NASA). Grant Number: HISFM18‐HIF Natural Environment Research Council (NERC). Grant Numbers: NE/P017150/1, NE/V002724/1, NE/N014480/1 RCUK | Science and Technology Facilities Council (STFC). Grant Number: ST/S000240/1