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We present a comprehensive statistical analysis of high‐frequency transient‐large‐amplitude (TLA) magnetic perturbation events that occurred at 12 high‐latitude ground magnetometer stations throughout Solar Cycle 24 from 2009 to 2019. TLA signatures are defined as one or more second‐timescale dB/dtinterval with magnitude ≥6 nT/s within an hour event window. This study characterizes high‐frequency TLA events based on their spatial and temporal behavior, relation to ring current activity, auroral substorms, and nighttime geomagnetic disturbance (GMD) events. We show that TLA events occur primarily at night, solely in the high‐latitude region above 60° geomagnetic latitude, and commonly within 30 min of substorm onsets. The largest TLA events occurred more often in the declining phase of the solar cycle when ring current activity was lower and solar wind velocity was higher, suggesting association to high‐speed streams caused by coronal holes and subsequent corotating interaction regions reaching Earth. TLA perturbations often occurred preceding or within the most extreme nighttime GMD events that have 5–10 min timescales, but the TLA intervals were often even more localized than the ∼300 km effective scale size of GMDs. We provide evidence that shows TLA‐related GMD events are associated with dipolarization fronts in the magnetotail and fast flows toward Earth and are closely temporally associated with poleward boundary intensifications (PBIs) and auroral streamers. The highly localized behavior and connection to the most extreme GMD events suggests that TLA intervals are a ground manifestation of features within rapid and complex ionospheric structures that can drive geomagnetically induced currents.

Dipolarizing flux bundles (DFBs) have been suggested to transport energy and momentum from regions of reconnection in the magnetotail to the high latitude ionosphere, where they can generate localized ionospheric currents that can produce large nighttime geomagnetic disturbances (GMDs). In this study we identified DFBs observed in the midnight sector from ∼7 to ∼10 R_Eby THEMIS A, D, and E during days in 2015–2017 whose northern hemisphere magnetic footpoints mapped to regions near Hudson Bay, Canada, and have compared them to isolated GMDs observed by ground magnetometers. We found 6 days during which one or more of these DFBs coincided to within ±3 min with ≥6 nT/s GMDs observed by latitudinally closely spaced ground‐based magnetometers located near those footpoints. Spherical elementary current systems (SECS) maps and all‐sky imager data provided further characterization of two events, showing short‐lived localized intense upward currents, auroral intensifications and/or streamers, and vortical perturbations of a westward electrojet. On all but one of these days the coincident DFB—GMD pairs occurred during intervals of high‐speed solar wind streams but low values of SYM/H. The observations reported here indicate that isolated DFBs generated under these conditions influence only limited spatial regions nearer Earth. In some events, in which the DFBs were observed closer to Earth and with lower Earthward velocities, the GMDs occurred slightly earlier than the DFBs, suggesting that braking had begun before the time of the DFB observation.

We present an automated method to identify high‐frequency geomagnetic disturbances in ground magnetometer data and classify the events by the source of the perturbations. We developed an algorithm to search for and identify changes in the surface magnetic field, dB/dt, with user‐specified amplitude and timescale. We used this algorithm to identify transient‐large‐amplitude (TLA) dB/dtevents that have timescale less than 60 s and amplitude >6 nT/s. Because these magnetic variations have similar amplitude and time characteristics to instrumental or man‐made noise, the algorithm identified a large number of noise‐type signatures as well as geophysical signatures. We manually classified these events by their sources (noise‐type or geophysical) and statistically characterized each type of event; the insights gained were used to more specifically define a TLA geophysical event and greatly reduce the number of noise‐type dB/dtidentified. Next, we implemented a support vector machine classification algorithm to classify the remaining events in order to further reduce the number of noise‐type dB/dtin the final data set. We examine the performance of our complete dB/dtsearch algorithm in widely used magnetometer databases and the effect of a common data processing technique on the results. The automated algorithm is a new technique to identify geomagnetic disturbances and instrumental or man‐made noise, enabling systematic identification and analysis of space weather related dB/dtevents and automated detection of magnetometer noise intervals in magnetic field databases.

Simultaneously cycling space weather parameters may show high correlations even if there is no immediate relationship between them. We successfully remove diurnal cycles using spectral subtraction, and remove both diurnal and longer cycles (e.g., the 27 days solar cycle) with a difference transformation. Other methods of diurnal cycle removal (daily averaging, moving averages [MAs], and simpler spectral subtraction using regression) are less successful at removing cycles. We apply spectral subtraction (a finite impulse response equiripple bandstop filter) to hourly electron flux (Los Alamos National Laboratory satellite data) and a ground‐based ULF index to remove a 24 hr noise signal. This results in smoother time series appropriate for short‐term (approximately < 1 week) correlation and observational studies. However, spectral subtraction may not remove longer cycles such as the 27 days and 11 yr solar cycles. A differencing transformation (y_t–y_t−24) removes not only the 24 hr noise signal but also the 27 days solar cycle, autocorrelation, and longer trends. This results in a low correlation between electron flux and the ULF index over long periods of time (maximum of 0.1). Correlations of electron flux and the ULF index with solar wind velocity (differenced aty_t–y_t−1) are also lower than previously reported (≤0.1). An autoregressive, MA transfer function model (ARIMAX) shows that there are significant cumulative effects of solar wind velocity on ULF activity over long periods, but correlations of velocity and ULF waves with flux are only seen over shorter time spans of more homogeneous geomagnetic activity levels.

To find the best method of predicting when daily relativistic electron flux (>2 MeV) will rise at geosynchronous orbit, we compare model predictive success rates (true positive rate or TPR) for multiple regression, ARMAX, logistic regression, a feed‐forward multilayer perceptron (MLP), and a recurrent neural network (RNN) model. We use only those days on which flux could rise, removing days when flux is already high from the data set. We explore three input variable sets: (1) ground‐based data (Kp,Dst, and sunspot number), (2) a full set of easily available solar wind and interplanetary magnetic field parameters (|B|,Bz,V,N,P,Ey,Kp,Dst, and sunspot number, and (3) this full set with the addition of previous day's flux. Despite high validation correlations in the multiple regression and ARMAX predictions, these regression models had low predictive ability (TPR < 45%) and are not recommended for use. The three classifier model types (logistic regression, MLP, and RNN) performed better (TPR: 50.8–74.6%). These rates were increased further if the cost of missing an event was set at 4 times that of predicting an event that did not happen (TPR: 73.1–89.6%). The area under the receiver operating characteristic curves did not, for the most part, differ between the classifier models (logistic, MLP, and RNN), indicating that any of the three could be used to discriminate between events and nonevents, but validation suggests a full RNN model performs best. The addition of previous day's flux as a predictor provided only a slight advantage.

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 eachL‐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 timescales. 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 lowerL(2–3), correlations between ULF and VLF (likely hiss) with electron flux were low. The most successful models, overL = 4–6, explained up to 47.1% of the variation in the data.