Probabilistic solar flare forecasting using historical magnetogram data

TL;DR

This study leverages over four solar cycles of historical magnetogram data with machine learning to improve probabilistic solar flare forecasts, emphasizing the importance of temporal information and flaring history.

Contribution

It introduces the first ML-based flare forecasting model utilizing extensive historical data and highlights the significance of flaring history over magnetogram features.

Findings

Including historical data enhances forecast skill and reliability.

Scalar features and flaring history outperform CNN-extracted features.

Temporal information is crucial for accurate flare prediction.

Abstract

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.