A radiation belt disturbance study from the space weather point of view

Rochel, Sandrine; Böscher, D.; Benacquista, Remi; Roussel, Jean

doi:10.1016/j.actaastro.2016.07.012

Cited by 3 publications

(7 citation statements)

References 17 publications

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“…Defining such an index is also interesting because it can take into account both multiple close events and energy accumulation, which could explain some extreme events (Benacquista et al, 2017;Lugaz et al, 2015). Besides, it has been shown in Rochel et al (2016) and more detailed in the next paragraph that Ca is well correlated to energetic particle fluxes (the highest correlations being found at L* ≈ 4). Hence using Ca as the main index in our study is adequate in order to perform an EVA on the planetary geomagnetic activity and in order to establish a link between this analysis and the dynamics of relativistic electron fluxes in Earth's radiation belts.…”

Section: 1029/2020sw002450mentioning

confidence: 94%

Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets

Bernoux

Maget

2020

Space Weather

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One of the main goals when studying Space Weather is to characterize extreme events occurrences and related characteristics. To do so, dedicated statistical methods from the so-called extreme value analysis (EVA) field have been developed. In this study we used Ca index, derived from aa, in order to characterize geoeffectiveness from the radiation belts point of view with a 150-year-long data set. The analysis performed in this study thus focuses on this newsworthy index to provide clues on the reliability of EVA methods. The first main result we present here is that the 1-in-10-, 1-in-50-, and 1-in-100-year events, respectively, match Ca values of 100.39, 131.39, and 142.84 nT. Consequently, the only 1-in-100 event observed during the Space Era would be the "Halloween Storm" in 2003 that reached a Ca value of 147.6 nT. The second main result highlighted in this work is that performing the same analysis with shorter subsets (20 years long) can give significantly different results for two reasons. The first reason is that some short time periods do not display the same distribution of events as the full period. The second reason is that the choice of the correct threshold (when using a Peaks Over Threshold approach) is made difficult with a short data set and leads to inaccurate results. This is a strong result as for accurate estimation of the induced effects of extreme events in radiation belts, we may only rely on short flux data sets from one or another mission (mostly shorter than 20 years).

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Section: 1029/2020sw002450mentioning

confidence: 94%

Characterizing Extreme Geomagnetic Storms Using Extreme Value Analysis: A Discussion on the Representativeness of Short Data Sets

Bernoux

Maget

2020

Space Weather

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“…The Ca index was created to account for geomagnetic storms during which intensification of relativistic electrons trapped in the radiation belts is observed. It was shown in Rochel et al (2016) and Bernoux & Maget (2020) that this index correlates well with electron fluxes (E > 30 keV) in the radiation belts and can take into account phenomena such as energy accumulation due to long-duration Stream Interaction Region (SIR)-driven storms, but also due to multiple successive Interplanetary Coronal Mass Ejection (ICME)-driven events. Figure 1 displays examples of the typical behaviour of the Ca index during ICME-and SIR-driven storms.…”

Section: Why Study and Forecast The Index?mentioning

confidence: 97%

“…The geomagnetic index studied here is the Ca index, which was first introduced by Bernoux & Maget (2020) based on a previous study by Rochel et al (2016). Therefore the following paragraphs rephrase some information on the purpose and relevance of this index that was contained in these papers.…”

Section: Data Setsmentioning

confidence: 99%

“…As stated in Bernoux & Maget (2020), the Ca index has been designed to quantify the geoeffectiveness of solar wind structures impacting the magnetosphere from the radiation belts perspective. The relaxation characteristic time in the radiation belt's for high-energy electrons after a strong magnetospheric disturbance is of the order of 4 days (Meredith et al, 2006;Rochel et al, 2016). Therefore, the Ca index is defined as follows:…”

Section: Data Setsmentioning

confidence: 99%

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An operational approach to forecast the Earth’s radiation belts dynamics

Bernoux

Brunet

Buchlin

et al. 2021

J. Space Weather Space Clim.

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The Ca index is a time-integrated geomagnetic index that correlates well with the dynamics of high-energy electron fluxes in the outer radiation belts. Therefore Ca can be used as an indicator for the state of filling of the radiation belts for those electrons. Ca also has the advantage of being a ground-based measurement with extensive historical records. In this work, we propose a data-driven model to forecast Ca up to 24 hours in advance from near-Earth solar wind parameters. Our model relies mainly on a recurrent neural network architecture called Long Short Term Memory that has shown good performances in forecasting other geomagnetic indices in previous papers. Most implementation choices in this study were arbitrated from the point of view of a space system operator, including the data selection and split, the definition of a binary classification threshold, and the evaluation methodology. We evaluate our model (against a linear baseline) using both classical and novel (in the space weather field) measures. In particular, we use the Temporal Distortion Mix (TDM) to assess the propensity of two time series to exhibit time lags. We also evaluate the ability of our model to detect storm onsets during quiet periods. It is shown that our model has high overall accuracy, with evaluation measures deteriorating in a smooth and slow trend over time. However, using the TDM and binary classification forecast evaluation metrics, we show that the forecasts lose some of their usefulness in an operational context even for time horizons shorter than 6 hours. This behaviour was not observable when evaluating the model only with metrics such as the root-mean-square error or the Pearson linear correlation. Considering the physics of the problem, this result is not surprising and suggests that the use of more spatially remote data (such as solar imaging) could improve space weather forecasts.

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“…Focusing on the ring current and the associated low‐latitude magnetic disturbances, “geomagnetic storms” as characterized by Dst are important. Focusing on intensifications of relativistic electrons, what might be called “trapped radiation storms” could be characterized by radiation‐belt fluxes or by the time integral of a Kp ‐like index (e.g., Baker, Kanekal, & Blake, ; Borovsky & Yakymenko, ; Rochel et al, ). Focusing on high‐latitude currents, “high‐latitude electrical storms” could be characterized by d B /d t or the temporal Fourier transforms from ground‐based magnetometers (e.g., Marshall et al, ; Wintoft, Wik, & Viljanen, ).…”

mentioning

confidence: 99%