  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.
