Addressing Known Challenges in Solar Flare Forecasting I: Limb-Flare Prediction with a 4-pi Full-Heliosphere Framework

A demonstrated failure mode for operational solar flare forecasting is the inability to forecast flares that occur near, or just beyond, the solar limb. To address this shortcoming, we develop a "4pi" full-heliosphere event forecasting framework and evaluate its statistical classification ability against this specific challenge. A magnetic surface flux transport model is used to generate full-sun maps of the photospheric radial magnetic field from which active regions (ARs) are identified and tracked using a new labeling scheme that is observer-location agnostic and allows for post-facto modifications. Flare-relevant magnetic parameters couple to a "visibility" index that specifies AR location relative to the visible solar limb and expected flare detection. Flare labels are assigned according to peak Soft X-ray flux, and a statistical classification is performed using nonparametric discriminant analysis. A version where new or emerging ARs on the far ("invisible" side of the Sun are incorporated into the model by way of far-side helioseismology, is also tested. We evaluate the new framework by its performance specifically including the limb areas using Brier Skill Score and ROC Skill Score, finding improvement at the 2-sigma level or less. However, we do find that the number of False Negatives, or "missed" forecasts decreases, and find strong evidence that the additional information provided by the far-side helioseismology can help predict near- and just-beyond-limb flares, particularly for East-limb events. While individual components of this framework could be improved, we demonstrate that a known failure mode for solar flare forecasting can be mitigated with available resources.