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Predicting Solar Flares by Data Assimilation in Avalanche Models. I. Model Design and Validation  

Eric Belanger   Submitted: 2007-08-14 13:02

Data assimilation techniques, developed in the last two decades mainly for weather prediction, produce better forecasts by taking advantage of both theoretical/numerical models and real-time observations. In this paper, we explore the possibility of applying the data-assimilation techniques known as 4D-VAR to the prediction of solar flares. We do so in the context of a continuous version of the classical cellular-automaton-based self-organized critical avalanche models of solar flares introduced by Lu and Hamilton (Astrophys. J., 380, L89, 1991). Such models, although a priori far removed from the physics of magnetic reconnection and magneto-hydrodynamical evolution of coronal structures, nonetheless reproduce quite well the observed statistical distribution of flare characteristics. We report here on a large set of data assimilation runs on synthetic energy release time series. Our results indicate that, despite the unpredictable (and unobservable) stochastic nature of the driving/triggering mechanism within the avalanche model, 4D-VAR succeeds in producing optimal initial conditions that reproduce adequately the time series of energy released by avalanches/flares. This is an essential first step towards forecasting real flares.

Authors: Eric Belanger, Alain Vincent, Paul Charbonneau
Projects: None

Publication Status: Accepted in Solar Physics.
Last Modified: 2007-08-16 08:37
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Predicting Solar Flares by Data Assimilation in Avalanche Models. I. Model Design and Validation

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