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This paper presents a post hoc analysis of a deep learning-based full-disksolar flare prediction model. We used hourly full-disk line-of-sightmagnetogram images and selected binary prediction mode to predict theoccurrence of $\geq$M1.0-class flares within 24 hours. We leveraged custom dataaugmentation and sample weighting to counter the inherent class-imbalanceproblem and used true skill statistic and Heidke skill score as evaluationmetrics. Recent advancements in gradient-based attention methods allow us tointerpret models by sending gradient signals to assign the burden of thedecision on the input features. We interpret our model using three post hocattention methods: (i) Guided Gradient-weighted Class Activation Mapping, (ii)Deep Shapley Additive Explanations, and (iii) Integrated Gradients. Ouranalysis shows that full-disk predictions of solar flares align withcharacteristics related to the active regions. The key findings of this studyare: (1) We demonstrate that our full disk model can tangibly locate andpredict near-limb solar flares, which is a critical feature for operationalflare forecasting, (2) Our candidate model achieves an averageTSS=0.51$\pm$0.05 and HSS=0.38$\pm$0.08, and (3) Our evaluation suggests thatthese models can learn conspicuous features corresponding to active regionsfrom full-disk magnetograms.

Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting