Storm Sudden Commencements Without Interplanetary Shocks

Park, Wooyeon; Lee, Jeongwoo; Yi, Yu; Ssessanga, Nicholas; Oh, Suyeon

doi:10.5140/jass.2015.32.3.181

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Meanwhile from the perspective of SCs, 107 of the 414 (25 % ) SCs recorded during this interval do not correspond to an identified interplanetary shock at L1. This is very similar to the fraction found by Wang et al (2006) in their study between 1995 and 2004; such events have been attributed to other phenomena in the solar wind (e.g., Park et al, 2015; Tsurutani et al, 2011). Additionally, it is possible that there are data gaps due to extreme plasma conditions at L1 which prohibit the identification of a counterpart shock.…”

Section: Data and Catalogssupporting

confidence: 89%

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Smith¹,

Rae

Forsyth³

et al. 2020

Space Weather

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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), nonlinear (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 B Z. 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, for example, 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.

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Section: Data and Catalogssupporting

confidence: 89%

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Smith¹,

Rae

Forsyth³

et al. 2020

Space Weather

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“…Among them, events such as Fig. 1, which shows changes due to storm sudden commencements (Park et al 2015) and smooth recovery phase (Lee et al 2001), were selected. As a result, a total of 12 events were selected (Table 1).…”

Section: Data Preparationmentioning

confidence: 99%

Analysis of the Tsyganenko Magnetic Field Model Accuracy during Geomagnetic Storm Times Using the GOES Data

Song¹,

Min²

2022

Journal of Astronomy and Space Sciences

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Because of the small number of spacecraft available in the Earth’s magnetosphere at any given time, it is not possible to obtain direct measurements of the fundamental quantities, such as the magnetic field and plasma density, with a spatial coverage necessary for studying, global magnetospheric phenomena. In such cases, empirical as well as physics-based models are proven to be extremely valuable. This requires not only having high fidelity and high accuracy models, but also knowing the weakness and strength of such models. In this study, we assess the accuracy of the widely used Tsyganenko magnetic field models, T96, T01, and T04, by comparing the calculated magnetic field with the ones measured in-situ by the GOES satellites during geomagnetically disturbed times. We first set the baseline accuracy of the models from a data-model comparison during the intervals of geomagnetically quiet times. During quiet times, we find that all three models exhibit a systematic error of about 10% in the magnetic field magnitude, while the error in the field vector direction is on average less than 1%. We then assess the model accuracy by a data-model comparison during twelve geomagnetic storm events. We find that the errors in both the magnitude and the direction are well maintained at the quiet-time level throughout the storm phase, except during the main phase of the storms in which the largest error can reach 15% on average, and exceed well over 70% in the worst case. Interestingly, the largest error occurs not at the Dst minimum but 2–3 hours before the minimum. Finally, the T96 model has consistently underperformed compared to the other models, likely due to the lack of computation for the effects of ring current. However, the T96 and T01 models are accurate enough for most of the time except for highly disturbed periods.

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“…Sudden commencements (SC) are defined as abrupt increases of the horizontal component of the Earth magnetic field due to the compression of the magnetosphere, which may be followed or not by a magnetic storm (Park et al 2015). Following the initial onset, SC signals show an increase of the horizontal magnetic intensity (H) which can last from 1 to 10 min but is usually limited to 3-4 min (Maeda et al 1962).…”

Section: Ssc Recordings On Broad-band Seismometersmentioning

confidence: 99%

On the observation of magnetic events on broad-band seismometers

Díaz¹,

Ruiz²,

Curto³

et al. 2020

Earth Planets Space

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The objective of this contribution is to get new insights into the effects of magnetic field variations of natural and anthropogenic origin on broad-band seismic stations. Regarding natural sources of magnetic perturbations, we have investigated if the Sudden Storm Commencements (SSC) cataloged during the 24th solar cycle (2008-2019) can be systematically identified in broad-band seismic stations distributed worldwide. The results show that the 23 SSC events with a mean amplitude above 30 nT and most of those with lower energy but still clearly identified in the magnetometer detection network can be observed at broad-band stations' network using a simple low-pass filter. Although the preliminary impulse of those signals is usually stronger at stations located at high latitudes, major SSC are observed at seismic stations distributed worldwide. Regarding anthropogenic sources, we focus on the short period seismic signals recorded in urban environments which are correlated with the activity of the railway transportation system. We have analyzed collocated measurements of electric field and seismic signals within Barcelona, evidencing that significant changes in the electric field following the activity of the transportation systems can be attributed to leakage currents transmitted to the soil by trains. During space weather events, electric currents in the magnetosphere and ionosphere experience large variations inducing telluric currents near the Earth surface, which in turn generate a secondary magnetic field. In the case of underground trains, leakage currents are transmitted to the soil, which in turn can result in local variations in the magnetic field. The observed signals in modern seismometers can be related to the reaction of the suspension springs to these magnetic field variations or to the effect of the magnetic field variations on the force transducers used to keep the mass fixed.

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Storm Sudden Commencements Without Interplanetary Shocks

Cited by 7 publications

References 19 publications

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning

Analysis of the Tsyganenko Magnetic Field Model Accuracy during Geomagnetic Storm Times Using the GOES Data

On the observation of magnetic events on broad-band seismometers

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