Estimation of the Occurrence Probability of Extreme Geomagnetic Storms by Applying Extreme Value Theory to Aa Index

Chen, Si; Chai, Lihui; Xu, Kaihua; Wei, Yong; Rong, Zhaojin; Wan, Weixing

doi:10.1029/2019ja026947

Cited by 11 publications

(12 citation statements)

References 40 publications

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“…Extreme value models evaluate the tail of a distribution and they have a wide range of applications in climate and atmospheric science to industrial risks, geosciences, finance, economics, and insurance [ 6 , 7 , 9 , 11 , 15 , 33 , 35 , 40 ].…”

Section: Methodsmentioning

confidence: 99%

How do mobility restrictions and social distancing during COVID-19 affect oil price?

Dey

Das

2022

J Stat Theory Pract

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This paper provides an analysis of the effect of the COVID-19 outbreak on the crude oil price. Using a newly developed air mobility index and Apple’s driving trends index, we assess the effect of human mobility restrictions and social distancing during the COVID-19 pandemic on the crude oil price. We apply a quantile regression model, which evaluates different quantiles of the crude oil price. We also conduct an extreme value modeling, which examines the lower tail of the crude oil price distribution. We find that both the air mobility index and driving trends index significantly influence lower and upper quantiles of the WTI crude oil price. The extreme value models suggest that there is a potential risk of a negative crude oil price for a sudden extreme fall of air mobility.

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Section: Methodsmentioning

confidence: 99%

How do mobility restrictions and social distancing during COVID-19 affect oil price?

Dey

Das

2022

J Stat Theory Pract

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“…Acero et al (2018) applied the EVT method to analyze the geomagnetic Dst index and obtained an upper bound of 850 nT. In applying EVT, the block maxima method (BMM) is usually chosen, and it has been successfully applied in estimating the occurrence probability of extreme geomagnetic storms (Chen et al, 2019). Below, the principle of the BMM will be introduced in detail.…”

Section: The Evt Methodsmentioning

confidence: 99%

Evaluation of the 900‐Year European Auroral Records With Extreme Value Theory

Chen

Wei

et al. 2021

JGR Space Physics

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The aurora usually occurs in high-latitude or polar regions, but sometimes can extend to the mid-and low-latitude regions due to strong solar eruptions (Siscoe, 1980;Usoskin, 2017). The modern observations during the space age help us to understand the physics of aurora and solar activity. However, the 60-year database is not enough to study the solar-terrestrial relation over time scales of centuries or even longer. Nowadays, the long-term solar activity before the telescope age can be reconstructed by tree rings (e.g., Solanki et al., 2004), ice-core data (Beer et al., 2003;Usoskin et al., 2003) or by studying Sun-like stars (Wood et al., 2002), but its resultant disturbances in the space environment can only be inferred from the auroral records in historical books. These books are mainly from Europe, China, Korea and Japan, and cover the past two thousand years (e.g.,

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“…The GEV distribution has been applied in a diversity of scientific projects (e.g., Ghil et al., 2011), in statistical analyses of space‐weather phenomena (e.g., O'Brien et al., 2007; Tsiftsi & De la Luz, 2018), and, in particular, in statistical analyses of magnetic storm intensities (e.g., Chen et al., 2019; Elvidge, 2020; Love, 2020; Nikitina et al., 2016; Woodroffe et al., 2016). The GEV distribution is certainly useful for parameter exploration.…”

Section: Models For Block‐maximum Samplesmentioning

confidence: 99%

Extreme‐Event Magnetic Storm Probabilities Derived From Rank Statistics of Historical Dst Intensities for Solar Cycles 14–24

Love

2021

Space Weather

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A compilation is made of the largest and second‐largest magnetic‐storm‐maximum intensities, −Dst1 and −Dst2, for solar cycles 14–24 (1902–2016) by sampling Oulu Dcx for cycles 19–24, using published −Dstm values for 4 intense storms in cycles 14, 15, and 18 (1903, 1909, 1921, 1946), and calculating 15 new storm‐maximum −Dstm values (reported here) for cycles 14–18. Three different models are fitted to the cycle‐ranked −Dst1 and −Dst2 values using a maximum‐likelihood algorithm: A Gumbel model, an unconstrained Generalized‐Extreme‐Value model, and a Weibull model constrained to have a physically justified maximum storm intensity of −Dstm = 2500 nT. All three models are good descriptions of the data. Since the best model is not clearly revealed with standard statistical tests, inference is precluded of the source process giving rise to storm‐maximum −Dstm values. Of the three candidate models, the constrained Weibull gives the lowest superstorm occurrence probabilities. Using the compiled data and the constrained Weibull model, a once‐per‐century storm intensity is estimated to be −Dst1 = 663 nT, with a bootstrap 68% confidence interval of [497, 694] nT. Similarly, the probability that a future storm will have an intensity exceeding that of the March 1989 superstorm, −Dstm > 565 nT, is 0.246 per cycle with a 68% confidence interval of [0.140, 0.311] per cycle. Noting (possibly slight) ambiguity in the rankings of storm intensities, using the same methods, but storms more intense than those identified for cycles 14–16, would yield a higher once‐per‐century intensity and a higher probability for a −Dstm > 565 nT storm.

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Estimation of the Occurrence Probability of Extreme Geomagnetic Storms by Applying Extreme Value Theory to Aa Index

Cited by 11 publications

References 40 publications

How do mobility restrictions and social distancing during COVID-19 affect oil price?

How do mobility restrictions and social distancing during COVID-19 affect oil price?

Evaluation of the 900‐Year European Auroral Records With Extreme Value Theory

Extreme‐Event Magnetic Storm Probabilities Derived From Rank Statistics of Historical Dst Intensities for Solar Cycles 14–24

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