On the magnetospheric ULF wave counterpart of substorm onset

One near‐ubiquitous signature of substorms observed on the ground is the azimuthal structuring of the onset auroral arc in the minutes prior to onset. Termed auroral beads, these optical signatures correspond to concurrent exponential increases in ground ultralow frequency (ULF) wave power and are likely the result of a plasma instability in the magnetosphere. Here, we present a case study showing the development of auroral beads from a Time History of Events and Macroscale Interactions during Substorms (THEMIS) all‐sky camera with near simultaneous exponential increases in auroral brightness, ionospheric and conjugate magnetotail ULF wave power, evidencing their intrinsic link. We further present a survey of magnetic field fluctuations in the magnetotail around substorm onset. We find remarkably similar superposed epoch analyses of ULF power around substorm onset from space and conjugate ionospheric observations. Examining periods of exponential wave growth, we find the ground‐ and space‐based observations to be consistent, with average growth rates of ∼0.01 s−1, lasting for ∼4 min. Cross‐correlation suggests that the space‐based observations lead those on the ground by approximately 1–1.5 min. Meanwhile, spacecraft located premidnight and ∼10 RE downtail are more likely to observe enhanced wave power. These combined observations lead us to conclude that there is a magnetospheric counterpart of auroral beads and exponentially increasing ground ULF wave power. This is likely the result of the linear phase of a magnetospheric instability, active in the magnetotail for several minutes prior to auroral breakup

The influence of sudden commencements on the rate of change of the surface horizontal magnetic field in the United Kingdom

Sudden commencements (SCs) are rapid increases in the northward component of the surface geomagnetic field, related to sharp increases in the dynamic pressure of the solar wind. Large rates of change of the geomagnetic field can induce damaging currents in ground power networks. In this work, the effect of SCs on the (one minute) rate of change of the surface magnetic field (R) at three UK stations is investigated. The distributions of R during SCs are shifted to higher values than the data set as a whole. Rates of change greater than 10 nTmin‐1 are 30‐100 times more likely during SCs, though less than 8% of the most extreme R (≥ 99.99th percentile) are observed during SCs. SCs may also precede geomagnetic storms, another potential source of large R. We find that the probability of observing large R is greatly enhanced for three days following an SC. In the 24 hours following an SC it is 10 times more likely than at any given time to observe rates of change between 10 and several hundred nTmin‐1. Additionally, between 90 and 94% of data (depending on station) above the 99.97th percentile is recorded within three days of an SC. All values of R ≥ 200 nTmin‐1 in the UK have been observed within three days of an SC. These results suggest that accurately predicting sudden commencements is critically important to identify intervals during which power networks at similar geomagnetic latitudes to the UK are at risk from large GICs

The Changing Eigenfrequency Continuum during Geomagnetic Storms:Implications for Plasma Mass Dynamics and ULF Wave Coupling

Geomagnetic storms are one of the most energetic space weather phenomena. Previous studies have shown that the eigenfrequencies of ultralow frequency (ULF) waves on closed magnetic field lines in the inner magnetosphere decrease during storm times. This change suggests either a reduction in the magnetic field strength and/or an increase in its plasma mass density distribution. We investigate the changes in local eigenfrequencies by applying a superposed multiple‐epoch analysis to cross‐phase spectra from 132 geomagnetic storms. Six ground magnetometer pairs are used to investigate variations from approximately 3 4, the eigenfrequencies decrease by as much as 50% relative to their quiet time values. Both a decrease in magnetic field strength and an increase in plasma mass density, in some locations by more than a factor of 2, are responsible for this reduction. The enhancement of the ring current and an increase in oxygen ion density could explain these observations. At L < 4, the eigenfrequencies increase due to the decrease in plasma mass density caused by plasmaspheric erosion

The impact of sudden commencements on ground magnetic field variability: Immediate and delayed consequences

We examine how Sudden Commencements (SCs) and Storm Sudden Commencements (SSCs) influence the occurrence of high rates of change of the magnetic field (R) as a function of geomagnetic latitude. These rapid, high amplitude variations in the ground-level geomagnetic field pose a significant risk to ground infrastructure, such as power networks, as the drivers of geomagnetically induced currents. We find that rates of change of ∼ 30 nT min−1 at near-equatorial stations are up to 700 times more likely in an SC than in any random interval. This factor decreases with geomagnetic latitude such that rates of change around 30 nT min−1 are only up to 10 times more likely by 65°. At equatorial latitudes we find that 25% of all R in excess of 50 nT min−1 occurs during SCs. This percentage also decreases with geomagnetic latitude, reaching ≤ 1% by 55°. However, the time period from the SC to three days afterwards accounts for ≥ 90% of geomagnetic field fluctuations over 50 nT min−1, up to ∼ 60° latitude. Above 60°, other phenomena such as isolated substorms account for the majority of large R. Furthermore, the elevated rates of change observed during and after SCs are solely due to those classified as SSCs. These results show that SSCs are the predominant risk events for large R at mid and low latitudes, but that the risk from the SC itself decreases with latitude

Statistical investigation on equatorial pitch angle distribution of energetic electrons in Earth's outer radiation belt during CME- and CIR-driven storms

We present a statistical investigation (September 2012 - September 2017) of pitch angle distribution (PAD) of energetic electrons (&sim;30&nbsp;keV - 1&nbsp;MeV) in the outer radiation belt (L &ge; 3) during CME- and CIR-driven geomagnetic storms using Van Allen Probe measurements. We selected geomagnetic storms based on minimum of SYM-H being less than -50&nbsp;nT and classified the storms according to their drivers. Thus, we obtained 23 CME- and 24 CIR-driven storms. During the storm intervals, pitch angle resolved electron flux measurements are obtained from the MagEIS instrument on-board Van Allen Probe-A spacecraft. We assume symmetric pitch angle distributions around 90&deg; pitch angle and fit the observed PADs with Legendre polynomials after propagating them to the magnetic equator. Legendre coefficients c2 and c4, and the ratio R = |c2/c4| are used to categorize the different PAD types. To resolve the spatio-temporal distribution of PADs, these coefficients are binned in 5 L-shell bins, 12 MLT bins for seven energy channels and four storm phases. We found that several hundreds of keV electrons exhibit clear dependence on local time, storm phases and storm drivers, with increased anisotropy for CME-driven storms during main and early recovery phases. On the contrary, we found that tens of keV electrons do not exhibit significant dependence on these parameters. We have discussed the different physical mechanisms responsible for the observed MLT dependent PADs and found drift-shell splitting to be the major contributor. &nbsp;</p

Probabilistic forecasts of storm sudden commencements from interplanetary shocks using machine learning

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), non‐linear (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, e.g. 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

Forecasting GOES 15 >2 MeV electron fluxes from solar wind data and geomagnetic indices

The flux of > 2 MeV electrons at geosynchronous orbit is used by space weather forecasters as a key indicator of enhanced risk of damage to spacecraft in low, medium or geosynchronous Earth orbits. We present a methodology that uses the amount of time a single input dataset (solar wind data or geomagnetic indices) exceeds a given threshold to produce deterministic and probabilistic forecasts of the > 2 MeV flux at GEO exceeding 1000 or 10000 cm‐2 s‐1 sr‐1 within up to 10 days. By comparing our forecasts with measured fluxes from GOES 15 between 2014 and 2016, we determine the optimum forecast thresholds for deterministic and probabilistic forecasts by maximising the ROC and Brier Skill Scores respectively. The training dataset gives peak ROC scores of 0.71 to 0.87 and peak Brier Skill Scores of ‐0.03 to 0.32. Forecasts from AL give the highest skill scores for forecasts of up to 6‐days. AL, solar wind pressure or SYM‐H give the highest skill scores over 7‐10 days. Hit rates range over 56‐89% with false alarm rates of 11‐53%. Applied to 2012, 2013 and 2017, our best forecasts have hit rates of 56‐83% and false alarm rates of 10‐20%. Further tuning of the forecasts may improve these. Our hit rates are comparable to those from operational fluence forecasts, that incorporate fluence measurements, but our false alarm rates are higher. This proof‐of‐concept shows that the geosynchronous electron flux can be forecast with a degree of success without incorporating a persistence element into the forecasts

Do statistical models capture the dynamics of the magnetopause during sudden magnetospheric compressions?

Under periods of strong solar wind driving, the magnetopause can become compressed, playing a significant role in draining electrons from the outer radiation belt. Also termed “magnetopause shadowing,” this loss process has traditionally been attributed to a combination of magnetospheric compression and outward radial diffusion of electrons. However, the drift paths of relativistic electrons and the location of the magnetopause are usually calculated from statistical models and, as such, may not represent the time‐varying nature of this highly dynamic process. In this study, we construct a database ∼20,000 spacecraft crossings of the dayside magnetopause to quantify the accuracy of the commonly used Shue et al. (1998, https://doi.org/10.1029/98JA01103) model. We find that, for the majority of events (74%), the magnetopause model can be used to estimate magnetopause location to within ±1 RE. However, if the magnetopause is compressed below 8 RE, the observed magnetopause is greater than 1 RE inside of the model location on average. The observed magnetopause is also significantly displaced from the model location during storm sudden commencements, when measurements are on average 6% closer to the radiation belts, with a maximum of 42%. We find that the magnetopause is rarely close enough to the outer radiation belt to cause direct magnetopause shadowing, and hence rapid outward radial transport of electrons is also required. We conclude that statistical magnetopause parameterizations may not be appropriate during dynamic compressions. We suggest that statistical models should only be used during quiescent solar wind conditions and supplemented by magnetopause observations wherever possible

Statistical comparison of electron loss and enhancement in the outer radiation belt during storms

The near-relativistic electron population in the outer Van Allen radiation belt is highly dynamic and strongly coupled to geomagnetic activity such as storms and substorms, which are driven by the interaction of the magnetosphere with the solar wind. The energy, content and spatial extent of electrons in the outer radiation belt can vary on timescales of hours to days, dictated by the continuously evolving influence of acceleration and loss processes. While net changes in the electron population are directly observable, the relative influence of different processes is far from fully understood. Using a continuous 12-year dataset from the Proton Electron Telescope (PET) on board the Solar Anomalous Magnetospheric Particle Explorer (SAMPEX), we statistically compare the relative variations of trapped electrons to those in the bounce loss cone. Our results show that there is a proportional increase in flux entering the bounce loss cone outside the plasmapause during storm main phase and early recovery phase. Loss enhancement is sustained on the dawnside throughout the recovery phase while loss on the duskside is enhanced around minimum Sym-H and quickly diminishes. Spatial variations are also examined in relation to geomagnetic activity, making comparisons to possible causal wave modes such as whistler-mode chorus and plasmaspheric hiss

How well can we estimate Pedersen conductance from the THEMIS white-light all-sky cameras?

We show that a THEMIS (Time History of Events and Macroscale Interactions during Substorms) white‐light all‐sky imager (ASI) can estimate Pedersen conductance with an uncertainty of 3 mho or 40%. Using a series of case studies over a wide range of geomagnetic activity, we compare estimates of Pedersen conductance from the backscatter spectrum of the Poker Flat Advanced Modular Incoherent Scatter Radar (ISR) with auroral intensities. We limit this comparison to an area bounding the radar measurements and within a limited area close to, (but off) imager zenith. We confirm a linear relationship between conductance and the square root of auroral intensity predicted from a simple theoretical approximation. Hence we extend a previous empirical result found for green‐line emissions to the case of white‐light off‐zenith emissions. The difference between the radar conductance and the best‐fit relationship has a mean of ‐0.76 ± 4.8 mho, and a relative mean difference of 21% ± 78%. The uncertainties are reduced to ‐0.72 ± 3.3 mho and 0% ± 40% by averaging conductance over 10 minutes, which we attribute to the time that auroral features take to move across the imager field being greater than the 1 minute resolution of the radar data. Our results demonstrate and calibrate the use of THEMIS ASIs for estimating Pedersen conductance. This technique allows the extension of estimates of Pedersen conductance from ISRs to derive continental‐scale estimates on scales of ~1‐10 minutes and ~100 km2. It thus complements estimates from low‐altitude satellites, satellite auroral imagers, and ground‐based magnetometers