The development of a space climatology: 3. Models of the evolution of distributions of space weather variables with timescale

Lockwood, Mike; Bentley, Sarah N.; Owens, Mathew J.; Barnard, Luke A.; Scott, Chris J.; Watt, Clare E.; Allanson, Oliver; Freeman, Mervyn P. ORCID: https://orcid.org/0000-0002-8653-8279. 2019 The development of a space climatology: 3. Models of the evolution of distributions of space weather variables with timescale. Space Weather, 17 (1). 180-209. 10.1029/2018SW002017

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Official URL: http://dx.doi.org/10.1029/2018SW002017

Abstract/Summary

We study how the probability distribution functions of power input to the magnetosphere Pα and of the geomagnetic ap and Dst indices vary with averaging timescale, τ, between 3 hours and 1 year. From this we develop and present algorithms to empirically model the distributions for a given τ and a given annual mean value. We show that lognormal distributions work well for ap, but because of the spread of Dst for low activity conditions, the optimum formulation for Dst leads to distributions better described by something like the Weibull formulation. Annual means can be estimated using telescope observations of sunspots and modelling, and so this allows the distributions to be estimated at any given τ between 3 hour and 1 year for any of the past 400 years, which is another important step towards a useful space weather climatology. The algorithms apply to the core of the distributions and can be used to predict the occurrence rate of “large” events (in the top 5% of activity levels): they may contain some, albeit limited, information relevant to characterizing the much rarer “superstorm” events with extreme value statistics. The algorithm for the Dst index is the more complex one because, unlike ap, Dst can take on either sign and future improvements to it are suggested.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1029/2018SW002017
ISSN:	1542-7390
Additional Keywords:	climatology, long-term variations, geomagnetic indices, probability distribution functions, model algorithms, large and extreme events
Date made live:	13 Dec 2018 11:02 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/521868

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1029/2018SW002017

ISSN:

1542-7390

Additional Keywords:

climatology, long-term variations, geomagnetic indices, probability distribution functions, model algorithms, large and extreme events

Date made live:

13 Dec 2018 11:02 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/521868