  The prediction of solar flares is still a significant challenge in space
weather research, with no techniques currently capable of producing reliable
forecasts performing significantly above climatology. In this paper, we present
a flare forecasting technique using data assimilation coupled with
computationally inexpensive cellular automata called sandpile models. Our data
assimilation algorithm uses the simulated annealing method to find an optimal
initial condition that reproduces well an energy-release time series. We
present and empirically analyze the predictive capabilities of three sandpile
models, namely the Lu and Hamilton model (LH) and two deterministically-driven
models (D). Despite their stochastic elements, we show that
deterministically-driven models display temporal correlations between simulated
events, a needed condition for data assimilation. We present our new data
assimilation algorithm and demonstrate its success in assimilating synthetic
observations produced by the avalanche models themselves. We then apply our
method to GOES X-Ray time series for 11 active regions having generated
multiple X-class flares in the course of their lifetime. We demonstrate that
for such large flares, our data assimilation scheme substantially increases the
success of ``All-Clear'' forecasts, as compared to model climatology.

Solar and Stellar Astrophysics;Instrumentation and Methods for Astrophysics;Space Physics