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

• URL: http://arxiv.org/abs/2509.00631v2
• Date: Thu, 02 Oct 2025 06:16:57 GMT
• Title: Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers
• Authors: Giacomo Acciarini, Simone Mestici, Halil Kelebek, Linnea Wolniewicz, Michael Vergalla, Madhulika Guhathakurta, Umaa Rebbapragada, Bala Poduval, Atılım Güneş Baydin, Frank Soboczenski,
• Abstract summary: Total Electron Content (TEC) is a key ionospheric parameter.<n>TEC is derived from observations, but its reliable forecasting is limited by the sparse nature of global measurements.<n>We present a machine learning framework for ionospheric TEC forecasting that leverages Temporal Fusion Transformers (TFT) to predict sparse ionosphere data.
• Score: 0.28112829609955153
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \texttt{ionopy}.

Related papers

• Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models [0.27300286905606946]
  We present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure.<n>Our workflow integrates a large selection of data sources comprising Solar Dynamic Observatory data, solar irradiance indices (F10.7), solar wind parameters (velocity and interplanetary magnetic field), geomagnetic activity indices (Kp, AE, SYM-H), and NASA JPL's Global Ionospheric Maps of Total Electron Content (GIM-TEC)
  arXiv  Detail & Related papers  (2025-11-18T20:13:25Z)
• Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management [41.99844472131922]
  This work presents a transformer-based model that forecasts densities up to three days ahead.<n>It avoids spatial reduction and complex input pipelines, operating directly on a compact input set.<n>It is validated on real-world data and shows potential to support mission planning.
  arXiv  Detail & Related papers  (2025-11-08T19:02:14Z)
• DAWP: A framework for global observation forecasting via Data Assimilation and Weather Prediction in satellite observation space [60.729377189859]
  We propose our DAWP framework to enable AIWPs to operate in a complete observation space.<n>AIDA module applies a mask multi-modality autoencoder for assimilating irregular satellite observation tokens.<n>We show that AIDA significantly improves the roll out and efficiency of AIWP and holds promising potential to be applied in global precipitationresolution forecasting.
  arXiv  Detail & Related papers  (2025-10-13T03:13:35Z)
• Ultra-short-term solar power forecasting by deep learning and data reconstruction [60.200987006598524]
  We propose a deep-learning based ultra-short-term solar power prediction with data reconstruction.<n>We employ deep-learning models to capture long- and short-term dependencies towards the target prediction period.
  arXiv  Detail & Related papers  (2025-09-21T14:22:35Z)
• Communications to Circulations: Real-Time 3D Wind Field Prediction Using 5G GNSS Signals and Deep Learning [12.797495578809608]
  This paper introduces GCast-Wind, a novel deep learning framework that leverages signal strength from 5 Global Navigation System Satellite (GNSS) signals to forecast atmospheric wind fields.<n>Preliminary results demonstrate promising accuracy and robustness in real-time wind forecasts (up to 30 minutes lead time)<n>This interdisciplinary approach underscores the transformative potential of exploiting non-traditional data sources and deep learning for advanced environmental monitoring and real-time atmospheric applications.
  arXiv  Detail & Related papers  (2025-09-19T15:17:08Z)
• S$^2$Transformer: Scalable Structured Transformers for Global Station Weather Forecasting [67.93713728260646]
  Existing time series forecasting methods often ignore or unidirectionally model spatial correlation when conducting large-scale global station forecasting.<n>This contradicts the nature underlying observations of the global weather system limiting forecast performance.<n>We propose a novel Structured Spatial Attention in this paper.<n>It partitions the spatial graph into a set of subgraphs and instantiates Intra-subgraph Attention to learn local spatial correlation within each subgraph.<n>It aggregates nodes into subgraph representations for message passing among the subgraphs via Inter-subgraph Attention -- considering both spatial proximity and global correlation.
  arXiv  Detail & Related papers  (2025-09-10T05:33:28Z)
• Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators [4.852378895360775]
  We evaluate the efficiency of three surrogate models in speeding up experimental research by simulating land surface processes. Our findings indicate that while all models on average demonstrate high accuracy over the forecast period, the LSTM network excels in continental long-range predictions when carefully tuned.
  arXiv  Detail & Related papers  (2024-07-23T13:26:05Z)
• TriQXNet: Forecasting Dst Index from Solar Wind Data Using an Interpretable Parallel Classical-Quantum Framework with Uncertainty Quantification [2.1940162009107382]
  Geomagnetic storms can disrupt critical infrastructure like GPS, satellite communications, and power grids. This research introduces TriQXNet, a novel hybrid classical-quantum neural network for Dst forecasting. Our model integrates classical and quantum computing, conformal prediction, and explainable AI (XAI) within a hybrid architecture.
  arXiv  Detail & Related papers  (2024-07-09T08:30:42Z)
• Global 4D Ionospheric STEC Prediction based on DeepONet for GNSS Rays [14.934920001287962]
  We propose a high-precision STEC model named DeepONet-STEC, which learns nonlinear operators to predict the 4D temporal-spatial integrated parameter for specified ground station - satellite ray path globally. As a demonstration, we validate the performance of the model based on observation data for global and US-CORS regimes under ionospheric quiet and storm conditions. The DeepONet-STEC model results show that the three-day 72 hour prediction in quiet periods could achieve high accuracy using observation data by the Precise Point Positioning (PPP) with temporal resolution 30s.
  arXiv  Detail & Related papers  (2024-03-12T10:51:38Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• High-Cadence Thermospheric Density Estimation enabled by Machine Learning on Solar Imagery [0.14061979259370275]
  We incorporate NASA's Solar Dynamics Observatory (SDO) extreme ultraviolet (EUV) spectral images into a neural thermospheric density model. We demonstrate that EUV imagery can enable predictions with much higher temporal resolution and replace ground-based proxies.
  arXiv  Detail & Related papers  (2023-11-12T23:39:21Z)
• Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context [46.72071291175356]
  Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_2$ emissions. However, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. In this paper, we put forth a deep learning architecture designed to harnesstemporal context using satellite data.
  arXiv  Detail & Related papers  (2023-06-01T19:54:39Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.