用深度学习预测电离层变化，提升导航通信可靠性
IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics
- 基于图神经网络的时空建模，融合多源观测数据
- 风暴期与平静期预测均优于基准方法
- 适合空间天气、导航定位领域研究人员使用
电离层是近地空间的关键组成部分，影响全球导航卫星系统精度、高频通信和航空运行。为填补这一领域的预测空白，我们提出IonCast——一套受GraphCast启发的深度学习模型，用于全球总电子含量（TEC）的时空预测。该模型整合多种物理驱动因子与观测数据，通过异构数据统一建模，在保留期风暴和平静条件下均表现出优于持续性预测的性能。结果表明，基于图的可扩展时空学习能有效增强对电离层动态变化的机器学习理解，推动空间天气业务韧性发展。
原文摘要 · Abstract (English)
The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting and modeling of ionospheric variability has become increasingly relevant. To address this gap, we present IonCast, a suite of deep learning models that include a GraphCast-inspired model tailored for ionospheric dynamics. IonCast leverages spatiotemporal learning to forecast global Total Electron Content (TEC), integrating diverse physical drivers and observational datasets. Validating on held-out storm-time and quiet conditions highlights improved skill compared to persistence. By unifying heterogeneous data with scalable graph-based spatiotemporal learning, IonCast demonstrates how machine learning can augment physical understanding of ionospheric variability and advance operational space weather resilience.
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