The University of Southampton
University of Southampton Institutional Repository

Solar energetic particle events: phenomenology and prediction

Solar energetic particle events: phenomenology and prediction
Solar energetic particle events: phenomenology and prediction
Solar energetic particle events can cause major disruptions to the operation of spacecraft in earth orbit and outside the earth's magnetosphere and have to be considered for EVA and other manned activities. They may also have an effect on radiation doses received by the crew flying in high altitude aircraft over the polar regions. The occurrence of these events has been assumed to be random, but there would appear to be some solar cycle dependency with a higher annual fluence occurring during a 7 year period, 2 years before and 4 years after the year of solar maximum. Little has been done to try to predict these events in real-time with nearly all of the work concentrating on statistical modelling. Currently our understanding of the causes of these events is not good. But what are the prospects for prediction? Can artificial intelligence techniques be used to predict them in the absence of a more complete understanding of the physics involved? The paper examines the phenomenology of the events, briefly reviews the results of neural network prediction techniques and discusses the conjecture that the underlying physical processes might be related to self-organised criticality and turbulent MHD flows.
intermittancy, neural networks, prediction, protons, soc, solar energetic particles
0038-6308
55-62
Gabriel, S.B.
ac76976d-74fd-40a0-808d-c9f68a38f259
Patrick, G.J.
232ed7b4-dfba-4f92-b86a-2be74fb4808e
Gabriel, S.B.
ac76976d-74fd-40a0-808d-c9f68a38f259
Patrick, G.J.
232ed7b4-dfba-4f92-b86a-2be74fb4808e

Gabriel, S.B. and Patrick, G.J. (2003) Solar energetic particle events: phenomenology and prediction. Space Science Reviews, 107 (1-2), 55-62. (doi:10.1023/A:1025599000778).

Record type: Article

Abstract

Solar energetic particle events can cause major disruptions to the operation of spacecraft in earth orbit and outside the earth's magnetosphere and have to be considered for EVA and other manned activities. They may also have an effect on radiation doses received by the crew flying in high altitude aircraft over the polar regions. The occurrence of these events has been assumed to be random, but there would appear to be some solar cycle dependency with a higher annual fluence occurring during a 7 year period, 2 years before and 4 years after the year of solar maximum. Little has been done to try to predict these events in real-time with nearly all of the work concentrating on statistical modelling. Currently our understanding of the causes of these events is not good. But what are the prospects for prediction? Can artificial intelligence techniques be used to predict them in the absence of a more complete understanding of the physics involved? The paper examines the phenomenology of the events, briefly reviews the results of neural network prediction techniques and discusses the conjecture that the underlying physical processes might be related to self-organised criticality and turbulent MHD flows.

Text
SPE phenomenology and prediction.pdf - Other
Download (46kB)

More information

Published date: 2003
Keywords: intermittancy, neural networks, prediction, protons, soc, solar energetic particles

Identifiers

Local EPrints ID: 22691
URI: http://eprints.soton.ac.uk/id/eprint/22691
ISSN: 0038-6308
PURE UUID: a1446ce4-25e3-4bad-a0d0-bc30bf773d0b

Catalogue record

Date deposited: 16 Mar 2006
Last modified: 15 Mar 2024 06:40

Export record

Altmetrics

Contributors

Author: S.B. Gabriel
Author: G.J. Patrick

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×