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A Solar‐Centric Approach to Improving Estimates of Exposure Processes for Coronal Mass Ejections

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  • Kathrin Kirchen
  • William Harbert
  • Jay Apt
  • M. Granger Morgan

Abstract

We present a solar‐centric approach to estimating the probability of extreme coronal mass ejections (CME) using the Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph Experiment (LASCO) CME Catalog observations updated through May 2018 and an updated list of near‐Earth interplanetary coronal mass ejections (ICME). We examine robust statistical approaches to the estimation of extreme events. We then assume a variety of time‐independent distributions fitting, and then comparing, the different probability distributions to the relevant regions of the cumulative distributions of the observed CME speeds. Using these results, we then obtain the probability that the velocity of a CME exceeds a particular threshold by extrapolation. We conclude that about 1.72% of the CMEs recorded with SOHO LASCO arrive at the Earth over the time both data sets overlap (November 1996 to September 2017). Then, assuming that 1.72% of all CMEs pass the Earth, we can obtain a first‐order estimate of the probability of an extreme space weather event on Earth. To estimate the probability over the next decade of a CME, we fit a Poisson distribution to the complementary cumulative distribution function. We inferred a decadal probability of between 0.01 and 0.09 for an event of at least the size of the large 2012 event, and a probability between 0.0002 and 0.016 for the size of the 1859 Carrington event.

Suggested Citation

  • Kathrin Kirchen & William Harbert & Jay Apt & M. Granger Morgan, 2020. "A Solar‐Centric Approach to Improving Estimates of Exposure Processes for Coronal Mass Ejections," Risk Analysis, John Wiley & Sons, vol. 40(5), pages 1020-1039, May.
  • Handle: RePEc:wly:riskan:v:40:y:2020:i:5:p:1020-1039
    DOI: 10.1111/risa.13461
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    1. J. P. Eastwood & E. Biffis & M. A. Hapgood & L. Green & M. M. Bisi & R. D. Bentley & R. Wicks & L.‐A. McKinnell & M. Gibbs & C. Burnett, 2017. "The Economic Impact of Space Weather: Where Do We Stand?," Risk Analysis, John Wiley & Sons, vol. 37(2), pages 206-218, February.
    2. Aban, Inmaculada B. & Meerschaert, Mark M. & Panorska, Anna K., 2006. "Parameter Estimation for the Truncated Pareto Distribution," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 270-277, March.
    3. M. Ivette Gomes & Armelle Guillou, 2015. "Extreme Value Theory and Statistics of Univariate Extremes: A Review," International Statistical Review, International Statistical Institute, vol. 83(2), pages 263-292, August.
    4. Seth Jonas & Kassandra Fronczyk & Lucas M. Pratt, 2018. "A Framework to Understand Extreme Space Weather Event Probability," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1534-1540, August.
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