Predicting Extreme Solar Flare Events Using Lu and Hamilton Avalanche Model

Solar flares are the most powerful events in the solar atmosphere, releasing a huge amount of energy in a few minutes. Any progress in predicting when a flare of a big magnitude will occur is extremely important to evaluate the risk related to space weather. The Lu and Hamilton ( Astrophys. J. Lett....

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Bibliographic Details
Published in:Solar physics 2020-11, Vol.295 (11), Article 155
Main Authors: Morales, L. F., Santos, N. A.
Format: Article
Language:English
Subjects:
Astrophysics and Astroparticles
Atmospheric models
Atmospheric Sciences
Avalanche models
Avalanches
Physics
Physics and Astronomy
Probability density function
Probability density functions
Solar atmosphere
Solar flares
Solar physics
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Space weather
Stability analysis
Towards Future Research on Space Weather Drivers
Two dimensional models
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Summary:Solar flares are the most powerful events in the solar atmosphere, releasing a huge amount of energy in a few minutes. Any progress in predicting when a flare of a big magnitude will occur is extremely important to evaluate the risk related to space weather. The Lu and Hamilton ( Astrophys. J. Lett. 380 , L89, 1991 ) self-organized criticality (SOC) model for solar flares is the one most conspicuous amongst the several avalanche models for flares that have been developed in the last 30 years. It has been very successful in reproducing some of the characteristic features of observed flares (e.g. probability density function of flare energy) and in the last years has been explored as a way of predicting extreme flaring events. In this work, we study the predicting capabilities of Lu and Hamilton model by assessing the proximity to stability of the 2D lattice and studying the influence of the lattice structure in the generation of large avalanches. We find that the mean value of the lattice nodes bears enough information to predict large avalanches in more than half of the cases, making it a reliable precursor for forecasting purposes.
ISSN:0038-0938
1573-093X
DOI:10.1007/s11207-020-01713-0