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
Predicting Lower Band Chorus With Autoregressive‐Moving Average Transfer Function (ARMAX) Models
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|, IMFBz, 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.
more »
« less
- Award ID(s):
- 1651263
- PAR ID:
- 10445706
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 124
- Issue:
- 7
- ISSN:
- 2169-9380
- Page Range / eLocation ID:
- p. 5692-5708
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract The auroral substorm has been extensively studied over the last six decades. However, our understanding of its driving mechanisms is still limited and so is our ability to accurately forecast its onset. In this study, we present the first deep learning‐based approach to predict the onset of a magnetic substorm, defined as the signature of the auroral electrojets in ground magnetometer measurements. Specifically, we use a time history of solar wind speed (Vx), proton number density, and interplanetary magnetic field (IMF) components as inputs to forecast the occurrence probability of an onset over the next 1 hr. The model has been trained and tested on a data set derived from the SuperMAG list of magnetic substorm onsets and can correctly identify substorms ∼75% of the time. In contrast, an earlier prediction algorithm correctly identifies ∼21% of the substorms in the same data set. Our model's ability to forecast substorm onsets based on solar wind and IMF inputs prior to the actual onset time, and the trend observed in IMFBzprior to onset together suggest that a majority of the substorms may not be externally triggered by northward turnings of IMF. Furthermore, we find that IMFBzandVxhave the most significant influence on model performance. Finally, principal component analysis shows a significant degree of overlap in the solar wind and IMF parameters prior to both substorm and nonsubstorm intervals, suggesting that solar wind and IMF alone may not be sufficient to forecast all substorms, and preconditioning of the magnetotail may be an important factor.more » « less
-
Abstract Solar wind directional discontinuities, such as rotational discontinuities (RDs), significantly influence energy and transport processes in the Earth's magnetosphere. A recent observational study identified a long‐lasting double cusp precipitation event associated with RD in solar wind on 10 April 2015. To understand the magnetosphere‐ionosphere response to the solar wind RD, a global hybrid simulation of the magnetosphere was conducted, with solar wind conditions based on the observation event. The simulation results show significant variations in the magnetopause and cusp regions caused by the passing RD. After the RD propagates to the magnetopause, ion precipitation intensifies, and a double cusp structure at varying latitudes and longitudes forms near noon in the northern hemisphere, which is consistent with the satellite observations by Wing et al. (2023,https://doi.org/10.1029/2023gl103194). Regarding dayside magnetopause reconnection, the simulation reveals that the high‐latitude reconnection process persists during the RD passing, regardless of whether the interplanetary magnetic field (IMF) with a highBy/Bzratio has a positive or negativeBzcomponent, and low‐latitude reconnection occurs after the RD reaches the magnetopause at noon when the IMF turns southward. By examining the ion sources along the magnetic field lines, a connection is found between the single‐ or double‐cusp ion precipitation and the solar wind ions entering from both high‐latitude and low‐latitude reconnection sites. This result suggests that the double‐cusp structure can be triggered by magnetic reconnection occurring at both low latitudes and high latitudes in the opposite hemispheres, associated with a largeBy/Bzratio of the IMF around the RD.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
-
Abstract Many factors influence relativistic outer radiation belt electron fluxes, such as waves in the ultralow frequency (ULF) Pc5, very low frequency (VLF), and electromagnetic ion cyclotron (EMIC) frequency bands, seed electron flux, Dst disturbance levels, substorm occurrence, and solar wind inputs. In this work we compared relativistic electron flux poststorm versus prestorm using three methods of analysis: (1) multiple regression to predict flux values following storms, (2) multiple regression to predict the size and direction of the change in electron flux, and (3) multiple logistic regression to predict only the probability of the flux rising or falling. We determined which is the most predictive model and which factors are most influential. We found that a linear regression predicting the difference in prestorm and poststorm flux (Model 2) results in the highest validation correlations. The logistic regression used in Model 3 had slightly weaker predictive abilities than the other two models but had the most value in providing a prediction of the probability of the electron flux increasing after a storm. Of the variables used (ULF Pc5 and VLF, seed electrons, substorm activity, and EMIC waves), the most influential in the final model were ULF Pc5 waves and the seed electrons. IMF Bz, Dst, and solar wind number density, velocity, and pressure did not improve any of the models, and were deemed unnecessary for effective predictions.more » « less
An official website of the United States government
