An official website of the United States government
Abstract Electron fluxes (20 eV–2 MeV, RBSP‐A satellite) show reasonable simple correlation with a variety of parameters (solar wind, IMF, substorms, ultralow frequency (ULF) waves, geomagnetic indices) over L‐shells 2–6. Removing correlation‐inflating common cycles and trends (using autoregressive and moving average terms in an ARMAX analysis) results in a 10 times reduction in apparent association between drivers and electron flux, although many are still statistically significant (p < 0.05). Corrected influences are highest in the 20 eV–1 keV and 1–2 MeV electrons, more modest in the midrange (2–40 keV). Solar wind velocity and pressure (but not number density), IMF magnitude (with lower influence ofBz), SME (a substorm measure), a ULF wave index, and geomagnetic indices Kp and SymH all show statistically significant associations with electron flux in the corrected individual ARMAX analyses. We postulate that only pressure, ULF waves, and substorms are direct drivers of electron flux and compare their influences in a combined analysis. SME is the strongest influence of these three, mainly in the eV and MeV electrons. ULF is most influential on the MeV electrons. Pressure shows a smaller positive influence and some indication of either magnetopause shadowing or simply compression on the eV electrons. While strictly predictive models may improve forecasting ability by including indirect driver and proxy parameters, and while these models may be made more parsimonious by choosing not to explicitly model time series behavior, our present analyses include time series variables in order to draw valid conclusions about the physical influences of exogenous parameters.
more »
« less
Determining the Timing of Driver Influences on 1.8–3.5 MeV Electron Flux at Geosynchronous Orbit Using ARMAX Methodology and Stepwise Regression
Abstract Although lagged correlations have suggested influences of solar wind velocity (V) and number density (N), Bz, ultralow frequency (ULF) wave power, and substorms (as measured by the auroral electrojet (AE) index) 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 uncorrected for time behavior. Most influence occurs within a few hours, not the many hours suggested by correlation. A log transformation accounts for nonlinearities. Over all hours, solar wind velocity (V) and number density (N) show an initial negative impact, with longer term positive influences over the 9 (V) or 27 (N) hr. Bz is initially a positive influence, with a longer term (6 hr) 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 hr before. AE (representing electron injection by substorms) shows only a short term (1 hr) positive influence. However, when only recovery and after‐recovery storm periods are considered (using stepwise regression), there are positive influences of ULF waves, AE, andV, with negative influences ofNand Bz.
more »
« less
- Award ID(s):
- 2013648
- PAR ID:
- 10392007
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 128
- Issue:
- 1
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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 (yt–yt−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 atyt–yt−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.more » « less
-
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.more » « less
-
Abstract The coupling response between solar wind structures and the magnetosphere is highly complex, leading to different effects in the outer radiation belt electron fluxes. Most Coronal Mass Ejections cause strong geomagnetic storms with short recovery phases, often 1–2 days. By contrast, High‐Speed Solar Wind Streams lead to moderate and weak storms often with much longer recovery phases, from several to ∼10 days. The magnetosphere receives energy for a long time under the influence of the HSSs, considerably changing its dynamics. This in turn has an effect on the charged particles trapped in the outer radiation belt. Although the high‐energy electron flux enhancements have received considerable attention, the high‐energy electron flux enhancement pattern (L > 4) has not. This paper identifies 37 events with this enhancement pattern in the high‐energy electron flux during the Van Allen Probes era. We find the enhancements coincident with HSS occurrence. The interplanetary magnetic field (IMF) exhibits north/south Bz fluctuations of Alfvénic nature with moderate amplitudes. The high‐energy electron flux enhancements also correspond to long periods of auroral activity indicating a relationship to magnetotail dynamics. However, the AE index only reaches moderate values. Ultra‐Low Frequency waves were present in all of the events and whistler‐mode chorus waves were present in 89.1% of the events, providing a convenient scenario for wave‐particle interactions. The radial gradient of the ULF wave power related to theL, under the influence of the HSSs, is necessary to trigger the physical processes responsible for this type of high‐energy electron flux enhancement pattern.more » « less
-
Predicting Geostationary (GOES) 4.1–30 keV Electron Flux Over All MLT Using LEEMYR Regression ModelsAbstract Regression models (LEEMYR: Low Energy Electron MLT geosYnchronous orbit Regression) predict hourly 4.1–30 keV electron flux at geostationary orbit (GOES‐16) using solar wind, IMF, and geomagnetic index parameters. Multiplicative interaction and polynomial terms describe synergistic and nonlinear effects. We reduce predictors to an optimal set using stepwise regression, resulting in models with validation comparable to a neural network. Models predict 1, 3, 6, 12, and 24 hr into the future. Validation correlations are as high as 0.78 (4.1 and 11 keV, 1 hr prediction) and Heidke Skill scores (HSS) up to 0.66. A 3 hr ahead prediction is more practical, with slightly lower validation correlation (0.75) and HSS (0.61). The addition of location (MLT: magnetic local time) as a covariate, including multiplicative interaction terms, accounts for location‐dependent flux differences and variation of parameter influence, and allows prediction over the full orbit. Adding a substorm index (SME) provides minimal increase in validation correlation (0.81) showing that other parameters are good proxies for an unavailable real time substorm index. Prediction intervals on individual values provide more accurate assessments of model quality than confidence intervals on the mean values. An inverse N‐weighted least squares approach is impractical as it increases false positive warnings. Physical interpretations are not possible as spurious correlations due to common cycles are not removed. However,SME, Bz, Kp, and Dst are the highest correlates of electron flux, with solar wind velocity, density, and pressure, and IMF magnitude being less well correlated.more » « less
