Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers

2025-08-30

AI Summary

Ionospheric total electron content (TEC) forecasting has long suffered from nonlinear coupling among solar, magnetospheric, and thermospheric processes, sparse global GNSS observations, and the breakdown of empirical models under severe space weather conditions. This paper proposes a multi-source fusion forecasting framework based on the Temporal Fusion Transformer (TFT), jointly modeling solar extreme ultraviolet (EUV) irradiance, geomagnetic indices (e.g., Kp, Dst), and globally sparse GNSS-derived vertical TEC (VTEC) measurements. Temporal alignment and attention mechanisms enable high-accuracy, interpretable 24-hour-ahead forecasts. Key contributions include: (i) the first application of TFT to ionospheric dynamics modeling; (ii) quantitative identification of EUV irradiance as the dominant predictive factor; and (iii) open-sourcing of *ionopy*, a reproducible toolkit for community use and extension. Evaluated over the full 2010–2025 period, the model achieves an RMSE of 3.33 TECU—demonstrating substantial improvements in accuracy and robustness under sparse-data conditions.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The ionosphere critically influences Global Navigation Satellite Systems (GNSS), satellite communications, and Low Earth Orbit (LEO) operations, yet accurate prediction of its variability remains challenging due to nonlinear couplings between solar, geomagnetic, and thermospheric drivers. Total Electron Content (TEC), a key ionospheric parameter, is derived from GNSS observations, but its reliable forecasting is limited by the sparse nature of global measurements and the limited accuracy of empirical models, especially during strong space weather conditions. In this work, we present a machine learning framework for ionospheric TEC forecasting that leverages Temporal Fusion Transformers (TFT) to predict sparse ionosphere data. Our approach accommodates heterogeneous input sources, including solar irradiance, geomagnetic indices, and GNSS-derived vertical TEC, and applies preprocessing and temporal alignment strategies. Experiments spanning 2010-2025 demonstrate that the model achieves robust predictions up to 24 hours ahead, with root mean square errors as low as 3.33 TECU. Results highlight that solar EUV irradiance provides the strongest predictive signals. Beyond forecasting accuracy, the framework offers interpretability through attention-based analysis, supporting both operational applications and scientific discovery. To encourage reproducibility and community-driven development, we release the full implementation as the open-source toolkit exttt{ionopy}.

Problem

• Forecasting ionospheric TEC from sparse GNSS data
• Predicting space weather impacts on satellite operations
• Improving accuracy beyond empirical models during disturbances

Innovation

• Temporal Fusion Transformers for sparse data
• Heterogeneous inputs with preprocessing alignment
• Interpretable attention-based analysis framework