用Transformer预测电离层参数，支持不确定性估计和跨区域泛化。
Forecasting Local Ionospheric Parameters Using Transformers
- 基于Transformer构建本地电离层预报模型，融合太阳通量等外部变量。
- 24小时预报精度优于国际参考电离层模型，且提供非参数不确定性区间。
- 可推广至未训练过的地区和时段，适合空间天气预警与导航系统应用。
本文提出一种基于Transformer的新型方法，用于预测关键电离层参数。模型针对特定地理区域，准确预报F2层峰值等离子体频率（foF2）、F2层峰值密度高度（hmF2）以及总电子含量（TEC），并包含日地10.7厘米太阳通量（F10.7cm）和地磁扰动指数（Dst）等外生变量。通过类似数据同化的训练方式，结合气候学的朴素预测，生成24小时预报及非参数不确定性边界。该方法称为局部电离层预报Transformer（LIFT）。实验表明，训练后的模型能有效泛化到未见的地理区域和时间周期，并在使用CCIR系数的国际参考电离层（IRI）模型对比中表现更优。
原文摘要 · Abstract (English)
We present a novel method for forecasting key ionospheric parameters using transformer-based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2-layer peak plasma frequency (foF2), the F2-layer peak density height (hmF2), and total electron content (TEC) for a given geographic location. It includes a number of exogenous variables, including F10.7cm solar flux and disturbance storm time (Dst). We demonstrate how transformers can be trained in a data assimilation-like fashion that uses these exogenous variables along with naive predictions from climatology to generate 24-hour forecasts with nonparametric uncertainty bounds. We call this method the Local Ionospheric Forecast Transformer (LIFT). We demonstrate that the trained model can generalize to new geographic locations and time periods not seen during training, and we compare its performance to that of the International Reference Ionosphere (IRI) using CCIR coefficients.
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