发表机构
Indian Institute of Geomagnetism; School of Environmental Science, Jawaharlal Nehru University（印度地磁研究所; 贾瓦哈拉尔·尼赫鲁大学环境科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种不依赖磁情指数的神经网络模型，利用太阳风参数和F10.7通量预测印度区域TEC，在磁暴期间保持良好精度，适用于区域预报和临近预报。
AI 中文摘要
在地磁扰动条件下准确预测电离层总电子含量（TEC）仍然具有挑战性，尤其是当经验模型在磁暴期间表现不佳，而神经网络（NN）方法又明确依赖诸如Kp/Ap或Dst/SYM-H等磁情指数时。在本研究中，我们开发了一个基于神经网络（NN）的模型，在不将磁情指数作为输入的情况下，预测印度经度扇区的TEC变化。该模型使用2024年全年、时间分辨率为15分钟的全球电离层地图（GIM）TEC数据集，以及太阳风参数——行星际磁场（IMF）B_z、速度（V_sw）和密度（N_p）——和F10.7厘米太阳射电流量进行训练。季节性和昼夜变化通过年积日（DOY）和一天中的小时（HOD）的正弦变换来表示。一个具有96个隐藏神经元的优化架构产生了良好的训练性能（R^2约0.96，RMSE约5.4，MAE约3.8 TECU）。对2025年的独立预测表明，在平静和地磁活动条件下均具有稳健的泛化能力。平静时期的残差通常限制在±10 TECU以内，而在中度和强磁暴条件下，尽管未使用磁情指数进行训练，该模型在印度大部分地区仍保持可比的误差范围。结果表明，太阳风参数结合F10.7太阳通量和周期性时间编码，包含了足够的信息来重现磁暴期间的TEC变化。因此，本研究证明了所提方法在区域TEC预测和电离层临近预报中的适用性，并在GNSS定位、卫星导航、无线电通信系统以及未来业务化电离层预报工具的开发中具有潜在应用价值。
英文摘要
Accurate prediction of ionospheric Total Electron Content (TEC) during geomagnetically disturbed conditions remains challenging, particularly when empirical models perform poorly during storms and neural network (NN) approaches rely explicitly on geomagnetic indices such as Kp/Ap or Dst/SYM-H. In this study, we develop an NN-based model to predict TEC variations over the Indian longitude sector without incorporating geomagnetic indices as inputs. The model is trained on the full-year (2024) Global Ionospheric Map (GIM) TEC dataset at a 15-minute cadence, together with solar wind parameters: Interplanetary Magnetic Field (IMF) $B_z$, velocity ($V_{sw}$), and density ($N_p$), along with the F10.7 cm solar radio flux. Seasonal and diurnal variability are represented using sinusoidal transformations of Day of Year (DOY) and hour of day (HOD). An optimized architecture with 96 hidden neurons yields strong training performance ($R^2$ $\sim$ 0.96, RMSE $\sim$ 5.4, and MAE $\sim$ 3.8 TECU). Independent predictions for the year 2025 demonstrate robust generalization under both quiet and geomagnetically active conditions. Quiet-time residuals are generally confined within $\pm$10 TECU, while during moderate and strong storm-time conditions, the model maintains comparable error bounds across most of the Indian region, despite not being trained on geomagnetic indices. The results indicate that solar wind parameters, combined with the F10.7 solar flux and cyclical temporal encoding, contain sufficient information to reproduce geomagnetic storm-time TEC variability. This study therefore demonstrates the applicability of the proposed approach for regional TEC prediction and ionospheric nowcasting, with potential applications in GNSS positioning, satellite navigation, radio communication systems, and the future development of operational ionospheric forecasting tools.
Comments16 pages, 9 figures, 1 table, manuscript is accepted for publication in Advances in Space Research (ASR)
Journal refAdvances in Space Research (2026)
