Towards coupling full-disk and active region-based flare prediction for operational space weather forecasting
Towards coupling full-disk and active region-based flare prediction for operational space weather forecasting
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DOI：
10.3389/fspas.2022.897301
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发表时间：
2022-08
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通讯作者：
Chetraj Pandey;Anli Ji;R. Angryk;M. Georgoulis;Berkay Aydin
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作者：
Chetraj Pandey;Anli Ji;R. Angryk;M. Georgoulis;Berkay Aydin
太阳耀斑预测是空间天气预报的一个核心问题，由于遥感以及机器学习和深度学习方法的最新进展，引起了广泛研究人员的关注。基于机器和深度学习模型的实验结果揭示了任务特定数据集的显着性能改进。除了构建模型之外，在操作环境下将此类模型部署到生产环境的实践是一个更加复杂且通常耗时的过程，通常不会在研究环境中直接解决。我们提出了一套新的启发式方法来训练和部署可操作的太阳耀斑预测系统，用于≥M1.0级耀斑，具有两种预测模式：全盘和基于活动区域。在全盘模式下，使用深度学习模型对全盘视线磁图执行预测，而在基于活动区域的模型中，使用多元时间序列数据实例单独为每个活动区域发出预测。各个活动区域预测和全盘预测器的输出通过元模型组合成最终的全盘预测结果。我们利用两个基础学习器耀斑概率的等权平均集成作为我们的基线元学习器，并通过训练逻辑回归模型来提高两个基础学习器的能力。这项研究的主要发现是：1）我们成功地将两个使用不同数据集和模型架构训练的异构耀斑预测模型结合在一起，以预测未来 24 小时的全盘耀斑概率，2）我们提出的集成模型，即逻辑回归，改进了两个基础学习器和基线元学习器的预测性能，根据两个广泛使用的指标真实技能统计（TSS）和海德克技能得分（HSS）进行测量，3）我们的结果分析表明逻辑回归基于回归的集成（Meta-FP）在 TSS 方面比全盘模型（基础学习器）提高了约 9%，在 HSS 方面提高了约 10%。同样，它在 TSS 和 HSS 方面分别比基于 AR 的模型（基础学习器）提高了约 17% 和约 20%。最后，与基线元模型相比，它的 TSS 提高了约 10%，HSS 提高了约 15%。
Solar flare prediction is a central problem in space weather forecasting and has captivated the attention of a wide spectrum of researchers due to recent advances in both remote sensing as well as machine learning and deep learning approaches. The experimental findings based on both machine and deep learning models reveal significant performance improvements for task specific datasets. Along with building models, the practice of deploying such models to production environments under operational settings is a more complex and often time-consuming process which is often not addressed directly in research settings. We present a set of new heuristic approaches to train and deploy an operational solar flare prediction system for ≥M1.0-class flares with two prediction modes: full-disk and active region-based. In full-disk mode, predictions are performed on full-disk line-of-sight magnetograms using deep learning models whereas in active region-based models, predictions are issued for each active region individually using multivariate time series data instances. The outputs from individual active region forecasts and full-disk predictors are combined to a final full-disk prediction result with a meta-model. We utilized an equal weighted average ensemble of two base learners’ flare probabilities as our baseline meta learner and improved the capabilities of our two base learners by training a logistic regression model. The major findings of this study are: 1) We successfully coupled two heterogeneous flare prediction models trained with different datasets and model architecture to predict a full-disk flare probability for next 24 h, 2) Our proposed ensembling model, i.e., logistic regression, improves on the predictive performance of two base learners and the baseline meta learner measured in terms of two widely used metrics True Skill Statistic (TSS) and Heidke Skill Score (HSS), and 3) Our result analysis suggests that the logistic regression-based ensemble (Meta-FP) improves on the full-disk model (base learner) by ∼9% in terms TSS and ∼10% in terms of HSS. Similarly, it improves on the AR-based model (base learner) by ∼17% and ∼20% in terms of TSS and HSS respectively. Finally, when compared to the baseline meta model, it improves on TSS by ∼10% and HSS by ∼15%.