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Cited by 26Ionosphere and magnetosphere dynamicsSolar and Space Plasma DynamicsGeomagnetism and Paleomagnetism Studies

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

Probability estimation of a Carrington-like geomagnetic storm

Open access
Moriña, David, Serra, Isabel, Puig, Pedro, Corral, Álvaro·2019·Scientific Reports
DOI 10.1038/s41598-019-38918-8PMC PMC6382914
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Abstract

Intense geomagnetic storms can cause severe damage to electrical systems and communications. This work proposes a counting process with Weibull inter-occurrence times in order to estimate the probability of extreme geomagnetic events. It is found that the scale parameter of the inter-occurrence time distribution grows exponentially with the absolute value of the intensity threshold defining the storm, whereas the shape parameter keeps rather constant. The model is able to forecast the probability of occurrence of an event for a given intensity threshold; in particular, the probability of occurrence on the next decade of an extreme event of a magnitude comparable or larger than the well-known Carrington event of 1859 is explored, and estimated to be between 0.46% and 1.88% (with a 95% confidence), a much lower value than those reported in the existing literature.

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Figures

Figure 1 Evolution of the Dst index in March 1989 (a) and values of the SYM-H index for the extreme geomagnetic storm of that month (b). Horizontal lines mark different thresholds. Time origins are 1989-03-01 (a) and 1989-03-13 (b).
Figure 1 Evolution of the Dst index in March 1989 (a) and values of the SYM-H index for the extreme geomagnetic storm of that month (b). Horizontal lines mark different thresholds. Time origins are 1989-03-01 (a) and 1989-03-13 (b).CC BY · PubMed Central
Figure 2 Empirical and fitted Weibull distributions for the time between consecutive geomagnetic storms, defined using two different thresholds, T = −150 nT (a) and T = −250 nT (b). The distributions are represented in terms of their probability densities, f(t). The empirical ones have been estimated with the so-called “log-binning” method56. Exponential fits, shown for comparison, underestimate the probability of both short and long times. Times below two days (disregarded in our approach and also in these fits) are grossly underestimated by any of the models. Weibull fits use the parameters coming from Eq. (2). Plotted using Gnuplot (version 3.7), see ref.57.
Figure 2 Empirical and fitted Weibull distributions for the time between consecutive geomagnetic storms, defined using two different thresholds, T = −150 nT (a) and T = −250 nT (b). The distributions are represented in terms of their probability densities, f(t). The empirical ones have been estimated with the so-called “log-binning” method56. Exponential fits, shown for comparison, underestimate the probability of both short and long times. Times below two days (disregarded in our approach and also in these fits) are grossly underestimated by any of the models. Weibull fits use the parameters coming from Eq. (2). Plotted using Gnuplot (version 3.7), see ref.57.CC BY · PubMed Central
Figure 3 Autocorrelation function of the inter-occurrence times for moderate (a), intense (b), Dst < −150 nT (c), Dst < −200 nT (d), super-storms (e) and Dst < −300 nT (f). The horizontal axis (lag) counts storm separation in terms of number of storms. Horizontal lines denote the limits of 95% confidence intervals.
Figure 3 Autocorrelation function of the inter-occurrence times for moderate (a), intense (b), Dst < −150 nT (c), Dst < −200 nT (d), super-storms (e) and Dst < −300 nT (f). The horizontal axis (lag) counts storm separation in terms of number of storms. Horizontal lines denote the limits of 95% confidence intervals.CC BY · PubMed Central
Figure 4 Relationship between Dst threshold (in nT) and Weibull shape (a) and scale (b) parameters, in log-scale, with scale parameter in days. Intensity thresholds range from −400 nT to −150 nT. The points correspond to maximum-likelihood estimates of the shape and scale parameters for fixed threshold values. The lines are the result of a Weibull regression model which is fitted directly to the whole data set of inter-occurrence times and thresholds (not to the shape and scale parameters).
Figure 4 Relationship between Dst threshold (in nT) and Weibull shape (a) and scale (b) parameters, in log-scale, with scale parameter in days. Intensity thresholds range from −400 nT to −150 nT. The points correspond to maximum-likelihood estimates of the shape and scale parameters for fixed threshold values. The lines are the result of a Weibull regression model which is fitted directly to the whole data set of inter-occurrence times and thresholds (not to the shape and scale parameters).CC BY · PubMed Central

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Citations

Cites

Show all 53 references

Research works printed but not linked: 2

  1. Chatfield, C. The Analysis of Time Series: An Introduction (Sixth Edition), https://www.crcpress.com/The-Analysis-of-Time-Series-An-Introduction-Sixth-Edition/Chatfield/p/book/9781584883173 (CRC Press, 2003).
  2. Dasgupta, R. Characterization theorems for Weibull distribution with applications. Journal of Environmental Statistics 6 (2014).

Cited by

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Metadata

Authors
Moriña, D., Serra, I., Puig, P., & Corral, Á.
Affiliations
Universitat Autònoma de Barcelona (ES)
Corresponding author
Moriña, David
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2019-02-20
Volume
9
Issue
1
Pages
2393-2393
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-019-38918-8
PMC
PMC6382914
OpenAlex
W2917727114
Cited by
26
References
53
Citations by year
2026: 2 · 2025: 2 · 2024: 2 · 2023: 3 · 2022: 4 · 2021: 7 · 2020: 2 · 2019: 4
FWCI
2.27
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, cc-by-nc-nd)
  • Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
  • Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, cc-by)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-nd)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion)
Funders
  • Ministerio de Econom&amp;#x00ED;a y Competitividad (MTM2015-69493-R)
  • Generalitat de Catalunya (2014SGR 1307)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Space and Planetary Science › Ionosphere and magnetosphere dynamics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Solar and Space Plasma Dynamics
  • Physical Sciences › Earth and Planetary Sciences › Geophysics › Geomagnetism and Paleomagnetism Studies
Keywords
geomagnetic storms, Carrington event, probability of occurrence, counting processes, Weibull distribution, scale parameter, shape parameter, space weather, solar activity forecasting

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CoChat. (2026). Citation check: Probability estimation of a Carrington-like geomagnetic storm. https://app.cochat.ai/papers/10.1038/s41598-019-38918-8
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Moriña, D., Serra, I., Puig, P., & Corral, Á. (2019). Probability estimation of a Carrington-like geomagnetic storm. Scientific Reports. https://doi.org/10.1038/s41598-019-38918-8

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