Search for: All records

Abstract 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. 

Abstract 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. 

Abstract Ground observations of VLF (very low frequency) waves have often been used to infer VLF activity in the magnetosphere; however, they are not an unbiased measure of activity at satellite altitudes due to transionospheric absorption and subionospheric attenuation. We propose several empirical models that control for these effects. VLF power spectral density (PSD) from the VLF/ELF Logger Experiment (VELOX, L=4.6, Halley, Antarctica) is used to predict DEMETER low Earth orbit VLF PSD. Validation correlations of these models are as high as 0.764; thus, ground VLF receivers spaced around the Earth could provide coverage of outer radiation belt lower band chorus over the latitudinal limits of this model (±45–75°). Correlations of four frequency bands (centered at 0.5, 1.0, 2.0, and 4.25 kHz) are compared. The simple linear correlation between ground and satellite VLF PSD in the 1.0‐kHz channel was 0.606 (at dawn). A cubic model resulted in higher correlation (0.638). VLF penetration to the ground is reduced by ionospheric absorption during solar illumination and by disruption of ducting field lines during disturbed conditions. Subionospheric attenuation also reduces VLF observations from distant field lines. Addition of these covariates improved predictions. Both solar illumination and disturbed conditions reduced ground observation of VLF PSD, with higher power waves penetrating to the ground proportionately less than lower power waves. The effect of illumination in reducing wave penetration was more pronounced at higher frequency (4.25 kHz), with the effect at a midrange frequency (2.0 kHz) falling between these two extremes. 

Abstract We model lower band chorus observations from the DEMETER satellite using daily and hourly autoregressive‐moving average transfer function (ARMAX) equations. ARMAX models can account for serial autocorrelation between observations that are measured close together in time and can be used to predict a response variable based on its past behavior without the need for recent data. Unstable distributions of radiation belt source electrons (tens of keV) and the substorm activity (SMEd from the SuperMAG array) that is thought to inject these electrons were both statistically significant explanatory variables in a daily ARMAX model describing chorus. Predictions from this model correlated well with observations in a hold‐out test data set (validation correlation of 0.675). Source electron flux was most influential when observations came from the same day or the day before the chorus measurement, with effects decaying rapidly over time. Substorms were more influential when they occurred on previous days, presumably due to their injecting source electrons from the plasma sheet. A daily ARMAX model with interplanetary magnetic field (IMF)|B|, IMFB_z, and solar wind pressure as inputs instead of those given above was somewhat less predictive of chorus (r=0.611). An hourly ARMAX model with only solar wind and IMF inputs was even less successful, with a validation correlation of 0.502. 

Abstract Nearly all studies of impulsive geomagnetic disturbances (GMDs, also known as magnetic perturbation events MPEs) that can produce dangerous geomagnetically induced currents (GICs) have used data from the northern hemisphere. In this study, we investigated GMD 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 theBycomponent of the interplanetary magnetic field (IMF): GMDs were larger in the north (south) when IMFBywas >0 (<0). A majority of events occurred nearly simultaneously (to within ±3 min) independent of the sign ofByas long as |By| ≤ 2 |Bz|. As has been found in earlier studies, IMFBzwas <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.