scholarly journals Predicting Solar Flares by Data Assimilation in Avalanche Models

Solar Physics ◽  
2007 ◽  
Vol 245 (1) ◽  
pp. 141-165 ◽  
Author(s):  
Eric Bélanger ◽  
Alain Vincent ◽  
Paul Charbonneau
2014 ◽  
pp. 371-393 ◽  
Author(s):  
Antoine Strugarek ◽  
Paul Charbonneau ◽  
Richard Joseph ◽  
Dorian Pirot

2019 ◽  
Vol 883 (1) ◽  
pp. L20 ◽  
Author(s):  
Nastaran Farhang ◽  
Michael S. Wheatland ◽  
Hossein Safari

2001 ◽  
Vol 563 (2) ◽  
pp. L165-L168 ◽  
Author(s):  
S. W. McIntosh ◽  
P. Charbonneau

1996 ◽  
Vol 471 (2) ◽  
pp. 1044-1048 ◽  
Author(s):  
M. S. Wheatland ◽  
P. A. Sturrock

Solar Physics ◽  
2014 ◽  
Vol 289 (8) ◽  
pp. 2993-3015 ◽  
Author(s):  
Antoine Strugarek ◽  
Paul Charbonneau ◽  
Richard Joseph ◽  
Dorian Pirot

2015 ◽  
Vol 11 (A29B) ◽  
pp. 734-734
Author(s):  
Antoine Strugarek ◽  
Paul Charbonneau

AbstractWe propose to use a deterministically-driven class of self-organized criticality sandpile models to carry out predictions of the largest, most dangerous, and hardest to predict solar flares.


Solar Physics ◽  
2014 ◽  
Vol 289 (11) ◽  
pp. 4137-4150 ◽  
Author(s):  
A. Strugarek ◽  
P. Charbonneau

2017 ◽  
Vol 13 (S335) ◽  
pp. 250-253
Author(s):  
Antoine Strugarek ◽  
Allan S. Brun ◽  
Paul Charbonneau ◽  
Nicole Vilmer

AbstractThe largest solar flares, of class X and above, are often associated with strong energetic particle acceleration. Based on the self-similar distribution of solar flares, self-organized criticality models such as sandpiles can be used to successfully reproduce their statistics. However, predicting strong (and rare) solar flares turns out to be a significant challenge. We build here on an original idea based on the combination of minimalistic flare models (sandpiles) and modern data assimilation techniques (4DVar) to predict large solar flares. We discuss how to represent a sandpile model over a reduced set of eigenfunctions to improve the efficiency of the data assimilation technique. This improvement is model-independent and continues to pave the way towards efficient near real-time solutions for predicting solar flares.


Author(s):  
V. D. Tereshchenko ◽  
E. B. Vasil'ev ◽  
O. F. Ogloblina ◽  
V. A. Tereshchenko ◽  
S. M. Chernyakov

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