Substorm probabilities are best predicted from solar wind speed

Newell, P. T.; Liou, K.; Gjerloev, J. W.; Sotirelis, T.; Wing, Simon; Mitchell, E. J.

doi:10.1016/j.jastp.2016.04.019

Cited by 39 publications

(49 citation statements)

References 55 publications

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“…It is clearly shown that the expansion onset occurs earlier when the solar wind velocity increases and SBZ increases. This is consistent with the statistical study showing that the integral substorm probability increases with the solar wind velocity and SBZ (Newell et al, ). The rates of change in SML for the first 10 min from the expansion onset are indicated by the numerical figures in each panel.…”

Section: Resultssupporting

confidence: 92%

New Diagnosis for Energy Flow From Solar Wind to Ionosphere During Substorm: Global MHD Simulation

2019

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To investigate the energy involved by a substorm, we performed global magnetohydrodynamics simulation for different solar wind conditions. The intensity of the auroral electrojet increases with the southward component of interplanetary magnetic field (IMF) and the solar wind velocity, which is consistent with observations. To evaluate the energy that enters the magnetosphere, we first defined a solar wind effective cross‐sectional area in which all the Poynting fluxes entering the magnetosphere pass through. We found that the solar wind magnetic energy is insufficient to provide the magnetic energy entering the magnetosphere (intake magnetic energy) for southward IMF. About 33–88% of the intake magnetic energy is converted from the solar wind kinetic energy. About 2–7% of the solar wind kinetic energy (passing through the effective area) is converted to the magnetic energy that enters the magnetosphere. Significant contribution from the solar wind kinetic energy makes the energy coupling function complicated. The effective area also depends on the solar wind parameters, also making it complicated. An interesting point is that the rates of energy stored and released in the lobe also depend on the solar wind parameters. The ionospheric Joule heating rate is well correlated with the intake magnetic energy at onset, and during the substorm expansion. The correlation coefficients between them are better than that of the ε parameter. These results imply that both the directly driven process (manifested by intake magnetic energy) and loading‐unloading process (manifested by stored/released energy in the lobe) are largely regulated by the solar wind condition.

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Section: Resultssupporting

confidence: 92%

New Diagnosis for Energy Flow From Solar Wind to Ionosphere During Substorm: Global MHD Simulation

2019

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“…Our model ranks V x as the second most influential factor in forecasting substorms. This is in good agreement with (Li et al., ; Luo et al., ; Newell et al., ) who showed that while the B z component of IMF plays an important role in modulating magnetospheric dynamics, the role of solar wind velocity is more important than is usually assumed. Furthermore, we confirm the result reported by Li et al.…”

Section: Discussionsupporting

confidence: 92%

“…More recently, Newell et al. () used several years of substorm onsets compiled by SuperMAG (Newell & Gjerloev, ) and showed that solar wind velocity ( V x ) could be the most important factor in driving substorms. While the growth phase of substorms was typically associated with periods of elevated solar wind driving, a few studies reported observations of substorms occurring during relatively quiet conditions (Kullen & Karlsson, ; Lee et al., ; Miyashita et al., ).…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning‐Based Approach to Forecast the Onset of Magnetic Substorms

et al. 2019

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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 IMF Bz prior to onset together suggest that a majority of the substorms may not be externally triggered by northward turnings of IMF. Furthermore, we find that IMF Bz and Vx have 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.

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“…On the other hand, preferential occurrence of interplanetary shocks was found in July and November (Echer et al, ). So we examine solar wind and geomagnetic conditions during the great‐FEE days using the following parameters: the solar wind speed V as the best predictor of substorm probability (e.g., Newell et al, ) and as a factor influencing interplanetary shock parameters (e.g., Lugaz et al, ); a parameter of interplanetary electric field fluctuations VδB calculated by an expression VδB =

V \cdot \sqrt{(δ_{italicBz}^{2} + δ_{italicBy}^{2})}

, where

((), δ_{italicBz}^{2})

and

((), δ_{italicBy}^{2})

are variances of the IMF B z and B y components; and AL index of substorm activity.…”

Section: Discussionmentioning

confidence: 99%

Flux Enhancements of > 30 keV Electrons at Low Drift Shells L < 1.2 During Last Solar Cycles

Suvorova

2017

JGR Space Physics

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We present results of statistical analysis of enhancements of >30 keV electrons observed by the NOAA/POES satellites during solar cycles 23 and 24 (1998–2016) at low drift shells L < 1.2, so‐called forbidden zone. We collected 1,750 days (~25% of the total time) when fluxes of the forbidden energetic electrons (FEE) exceeded 103 (cm2 s sr)−1. We found 530 days, when FEE fluxes reached high intensity from 104 up to 107 (cm2 s sr)−1. It was found that the FEE enhancements were observed mostly often at the declining phases and solar minimum. More than 85% of the events occurred under fast solar wind (V > 450 km/s), high substorm activity (AL >150 nT), and enhanced interplanetary electric field perturbations (VδB > 1.5 mV/m). The FEE occurrence rate peaks around the local midnight. We have also found a quite unexpected annual variation of the FEE occurrence rate with a pronounced maximum from May to September, a minor peak in December–January, and minima at the equinoxes. The May–September peak, persisting at different solar cycle phases, was assumed to originate from high conductivity in the auroral ionosphere, which is controlled by the dipole tilt angle and provides better conditions for penetration of electric field perturbations into the inner magnetosphere. This allows explanation of the shape and amplitude of annual variation in the FEE occurrence rate from the convolution of the solar wind driver with the penetration conditions.

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Substorm probabilities are best predicted from solar wind speed

Cited by 39 publications

References 55 publications

New Diagnosis for Energy Flow From Solar Wind to Ionosphere During Substorm: Global MHD Simulation

New Diagnosis for Energy Flow From Solar Wind to Ionosphere During Substorm: Global MHD Simulation

A Deep Learning‐Based Approach to Forecast the Onset of Magnetic Substorms

Flux Enhancements of > 30 keV Electrons at Low Drift Shells L < 1.2 During Last Solar Cycles

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