Probabilistic solar flare forecasting using historical magnetogram data
Kiera van der Sande Andrés Muñoz-Jaramillo Subhamoy Chatterjee
Solar flare forecasting research using machine learning (ML) has focused on
high resolution magnetogram data from the SDO/HMI era covering Solar Cycle 24
and the start of Solar Cycle 25, with some efforts looking back to SOHO/MDI for
data from Solar Cycle 23. In this paper, we consider over 4 solar cycles of
daily historical magnetogram data from multiple instruments. This is the first
attempt to take advantage of this historical data for ML-based flare
forecasting. We apply a convolutional neural network (CNN) to extract features
from full-disk magnetograms together with a logistic regression model to
incorporate scalar features based on magnetograms and flaring history. We use
an ensemble approach to generate calibrated probabilistic forecasts of M-class
or larger flares in the next 24 hours. Overall, we find that including
historical data improves forecasting skill and reliability. We show that single
frame magnetograms do not contain significantly more relevant information than
can be summarized in a small number of scalar features, and that flaring
history has greater predictive power than our CNN-extracted features. This
indicates the importance of including temporal information in flare forecasting
models.