A supervised convolutional neural network (CNN) was developed to automatically identify electromagnetic ion cyclotron (EMIC) wave events from spectrograms. These events have usually been identified manually, which can be a time-consuming process. Statistical analyses of larger datasets would be facilitated if this process were simplified. The neural network model was trained on spectrogram images from the Halley magnetometer station that had been manually identified as either containing or not containing an EMIC wave event anywhere in the spectrogram. This model was tested on an unseen set of spectrograms, achieving a perfect true positive rate of 1. Size, time, frequency, and pixel color information was extracted from each identified event and exported into a spreadsheet for easier analysis. This method has the capability of reducing time and effort required to identify important spectrogram features by hand. Such an automated method could be applied to other space weather data stored in spectrograms.

We investigate the drivers of 40-150 keV hourly electron flux at geostationary orbit (GOES 13) using ARMAX (autoregressive moving average transfer function) models which remove the confounding effect of diurnal cyclicity and allow assessment of each parameter independently of others. By taking logs of flux and predictor variables, we create nonlinear models. While many factors show high correlation with flux (substorms, ULF waves, solar wind velocity (V), pressure (P), number density (N) and electric field (Ey), IMF Bz, Kp, and SymH), the ARMAX model identifies substorms as the dominant influence at 40-75 keV and over 20-12 MLT, with little difference seen between disturbed and quiet periods. Also over 40-75 keV, Ey has a modest effect: positive over 20-12 MLT but negative over 13-19 MLT. Pressure shows some negative influence at 150 keV. Hourly ULF waves, Kp, and SymH show little influence when other variables are included. Using path analysis, we calculate the total sum of influence, both directly and indirectly through the driving of intermediate parameters. Pressure shows a summed direct and indirect influence nearly half that of the direct substorm effect, peaking at 40 keV. N, V, and Bz, as indirect drivers, are equally influential. Neither simple correlation nor neural networks can effectively identify drivers. Instead, consideration of actual physical influences, removing cycles that artificially inflate correlations, and controlling the effects of other parameters using multiple regression (specifically, ARMAX) gives a clearer picture of which parameters are most influential in this system.

Although lagged correlations have suggested influences of solar wind velocity (V) and number density (N), IMF Bz, ULF wave power, and substorms (as measured by AE) on MeV electron flux at geosynchronous orbit over an impressive number of hours and days, a satellite’s diurnal cycle can inflate correlations, associations between drivers may produce spurious effects, and correlations between all previous time steps may create an appearance of additive influence over many hours. Autoregressive-moving average transfer function (ARMAX) multiple regressions incorporating previous hours simultaneously can eliminate cycles and assess the impact of parameters, at each hour, while others are controlled. ARMAX influences are an order of magnitude lower than correlations. Most influence occurs within a few hours, not the many hours suggested by correlation. Over all hours, V and N show an initial negative impact, with longer term positive influences over the 9 (V) or 27 (N) h. Bz is initially a positive influence, longer term (6 h) negative effect. ULF waves impact flux in the first (positive) and second (negative) hour before the flux measurement, with further negative influences in the 12- 24 h before. AE (representing electron injection by substorms) shows only a short term (1 h) positive influence. However, when only recovery and after-recovery storm periods are considered (using stepwise regression), there are positive influences of ULF waves and V, negative influences of N and Bz, while AE shows no influence.

We investigate the timing and relative influence of VLF in the chorus frequency range observed by the DEMETER spacecraft and ULF wave activity from ground stations on daily changes in electron flux (0.23 to over 2.9 MeV) observed by the HEO-3 spacecraft. At each L shell, we use multiple regression to investigate the effects of each wave type and each daily lag independent of the others. We find that reduction and enhancement of electrons occur at different time scales. Chorus power spectral density and ULF wave power are associated with immediate electron decreases on the same day but with flux enhancement 1-2 days later. ULF is nearly always more influential than chorus on both increases and decreases of flux, although chorus is often a significant factor. There was virtually no difference in correlations of ULF Pc3, Pc4, or Pc5 with electron flux. A synergistic interaction between chorus and ULF waves means that enhancement is most effective when both waves are present, pointing to a two-step process where local acceleration by chorus waves first energizes electrons which are then brought to even higher energies by inward radial diffusion due to ULF waves. However, decreases in flux due to these waves act additively. Chorus and ULF waves combined are most effective at describing changes in electron flux at >1.5 MeV. At lower L (2-3), correlations between ULF and VLF (likely hiss) with electron flux were low. The most successful models, over L=4-6, explained up to 47.1% of the variation in the data.

Nearly all studies of impulsive magnetic perturbation events (MPEs) that can produce dangerous geomagnetically induced currents (GICs) have used data from the northern hemisphere. In this study we investigated MPE occurrences during the first 6 months of 2016 at four magnetically conjugate high latitude station pairs using data from the Greenland West Coast magnetometer chain and from Antarctic stations in the conjugate AAL-PIP magnetometer chain. Events for statistical analysis and four case studies were selected from Greenland/AAL-PIP data by detecting the presence of >6 nT/s derivatives of any component of the magnetic field at any of the station pairs. For case studies, these chains were supplemented by data from the BAS-LPM chain in Antarctica as well as Pangnirtung and South Pole in order to extend longitudinal coverage to the west. Amplitude comparisons between hemispheres showed a) a seasonal dependence (larger in the winter hemisphere), and b) a dependence on the sign of the By component of the interplanetary magnetic field (IMF): MPEs were larger in the north (south) when IMF By was > 0 (< 0). A majority of events occurred nearly simultaneously (to within ± 3 min) independent of the sign of By as long as |By| ≤ 2 |Bz|. As has been found in earlier studies, IMF Bz was < 0 prior to most events. When IMF data from Geotail, Themis-B, and/or Themis C in the near-Earth solar wind were used to supplement the time-shifted OMNI IMF data, the consistency of these IMF orientations was improved.