Forecasting Solar Flares by Data Assimilation in Sandpile Models

TL;DR

This paper introduces a novel data assimilation approach using sandpile models to improve solar flare forecasting, demonstrating increased success in predicting large flares from real X-ray data.

Contribution

It presents a new data assimilation algorithm for sandpile models and applies it to solar flare prediction, showing improved forecast accuracy over climatology.

Findings

Deterministically-driven sandpile models show temporal correlations.

Data assimilation improves flare forecast success.

Method performs well on real X-ray data for large flares.

Abstract

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.