The data were used to study the high-frequency geomagnetic disturbances within the magnetic field data. Included in this repository are the python scripts that perform an identification and classification of high-frequency signals within the magnetometer data that is downloaded from the databases listed in the Methodology section. All analysis and plots were created using subsequent Python libraries. The machine learning study implemented libraries from the sci-kit learn software. All of the specific methodology can be accessed in the readme.txt script.
These data are TLA events identified in MACCS magnetometer data throughout 2015. These events are short-timescale (< 60 s), large -amplitude (> 6 nT/s) magnetic disturbances measured at Earth's surface that are analyzed for space weather research purposes. and The events were identified in a year's worth of magnetic field data using an algorithm developed in the MATLAB platform. The algorithm dBdt_main.m can be run using the associated scripts (clean_maccs.m, simple_dbdt.m, extremes1.m, newdbdt.m) to return the events in the 2015_AllEvents.csv file. The substorm onset delays of each event are determined with the onset_delays.m script and the substorm event list 20191127-15-56-substorms.csv (both included).
Engebretson, M. J., Pilipenko, V. A., Ahmed, L. Y., Posch, J. L., Steinmetz, E. S., Moldwin, M. B., … Vorobev, A. V. (2019). Nighttime Magnetic Perturbation Events Observed in Arctic Canada: 1. Survey and Statistical Analysis. Journal of Geophysical Research: Space Physics, 124(9), 7442–7458. https://doi.org/10.1029/2019JA026794
