Progress in solar flare forecasting

Solar flare is an abrupt and scale of energy release process in solar surface local region. It is a main disturbance resource of space environment and has tremendous influence. Existed solar flare forecasting models include obtaining predictor from observed data, constructing relation model of predictors and flare occurrence by using statistic or data mining method, and predicting future flare occurrence with this model. In solar flare forecasting research, the predictor, the forecasting method and model are three main sides. As an important part, predictor and data process are preprocessing work. Predictors usually select solar sunspot parameter, magnetic parameters and fractal dimension and so on. Forecasting methods include statistic method, machine learning method and data assimilation method. Statistic method is mainly used in early model. With the development of data mining technique, more and more machine learning methods are concerned and receive satisfied result. Data assimilation method has good model correction ability. In forecasting model, most of them are static model. Recently, the time revolution dynamic model is developed and has better performance. Besides, self-organized model developed recently gives a physical description about burst mechanism of solar flare. This paper summarizes the research progress in these three sides of flare forecasting. Connected with the work in solar activity prediction center of National Astronomical Observatories, important research progresses are reviewed. Finally, future research trend is prospected.