AI 中文总结
本研究提出RMN策略，结合实时数据、注意力机制LSTM模型与NOAA耀斑标准，对1997-2024年GOES观测的C、M、X级耀斑进行峰值流量临近预报，揭示了不同强度耀斑的预测性能差异及不确定性来源。
AI 中文摘要
我们提出RMN策略（Real-time data, machine learning, and NOAA flare detection criteria，即实时数据、机器学习与NOAA耀斑探测标准），用于在业务运行的真实条件下对正在发生的太阳耀斑的软X射线峰值流量进行临近预报。该策略将实时GOES 0.1-0.8 nm X射线观测数据与基于注意力机制的序列到序列长短期记忆（Long Short-Term Memory，LSTM）模型相结合。根据NOAA耀斑探测标准，我们从目录记录的耀斑起始时间后3分钟开始，以1分钟为间隔进行预测，使用起始时间前60分钟的X射线观测数据，直至观测到的峰值时间。我们将RMN策略应用于1997年至2024年期间GOES-8至GOES-18观测到的C级、M级和X级耀斑，采用四重交叉验证进行评估。本研究的主要结果如下：第一，该模型对≥C级耀斑组的软X射线峰值流量预报的均方根误差（RMSE）和百分比误差（PE）分别为0.26和3.11%，对≥M级耀斑组分别为0.45和5.59%，对X级耀斑组分别为0.87和12.76%。向更强耀斑组的偏差增大，表明高强度耀斑的峰值流量预测更具挑战性。第二，模型性能取决于耀斑上升时间和预测时间，上升时间更长的事件误差更大，且随着预测时间接近耀斑峰值，性能会提升。上升时间更短的事件更快达到最终峰值，能更清晰地指示最终峰值，而上升时间更长的事件的更大差异可能部分反映了更复杂的时间演化过程。第三，基于总不确定性的经验覆盖率保持较高水平，但对于更强的耀斑会有所下降，其中噪声不确定性的贡献大于模型不确定性。
英文摘要
We present the RMN strategy (Real-time data, machine learning, and NOAA flare detection criteria) for nowcasting the peak soft X-ray flux of ongoing solar flares under operationally realistic conditions. The strategy combines real-time GOES 0.1-0.8 nm X-ray observations with an attention-based sequence-to-sequence Long Short-Term Memory model. Under the NOAA flare detection criteria, predictions are evaluated at one-minute intervals from three minutes after the cataloged onset to the observed peak using the preceding 60 minutes of X-ray observations. We apply the RMN strategy to C-, M-, and X-class flares observed by GOES-8-18 from 1997 to 2024 using four-fold cross-validation. The major results of this study are as follows. First, the model nowcasts peak soft X-ray flux with RMSE and PE values of 0.26 and 3.11\% for the $\geq$C-class group, 0.45 and 5.59\% for the $\geq$M-class group, and 0.87 and 12.76\% for the X-class group. The higher discrepancy toward stronger flare groups indicates that peak-flux prediction is more challenging for higher-intensity flares. Second, the model performance depends on flare rise time and prediction time, with larger errors for longer rise time events and improved performance as the prediction time approaches the flare peak. Shorter rise time events approach their final peak more rapidly, providing a clearer indication of the eventual peak, whereas the larger difference for longer rise time events may partly reflect more complex temporal evolution. Third, empirical coverage based on total uncertainty remains high but decreases for stronger flares, with noise uncertainty contributing more than model uncertainty.
Comments7 tables, 5 figures
