Scholarlink

<p>Ionospheric total electron content (TEC) serves as a fundamental parameter for characterizing ionospheric morphology. Ionospheric TEC exhibits irregular disturbances driven by solar and geomagnetic activities. TEC forecasting products enhance GNSS positioning precision through error correction and space weather assessment, consequently improving satellite navigation system reliability and space weather warning systems. This paper proposes a modified spatiotemporal 3D convolutional U-Net architecture incorporating fused index features for forecasting 1-day global ionospheric TEC maps (FIST-TECNet). The input includes the previous day’s TEC maps and corresponding solar and geomagnetic indices, such as F10.7, SSN, Vsw, IMF Bz, Dst, and Kp indices. The FIST-TECNet model comprises an MLP-based fused feature generation module for solar and geomagnetic indices and a 3D convolutional spatiotemporal forecasting module. The primary contribution of this work involves the dimensional expansion of 1D solar and geomagnetic indices to achieve spatiotemporal alignment with 2D TEC grid maps. We benchmark the model against the C1PG, evaluating prediction performance under different geomagnetic storm intensities. The results demonstrate that the fused index generation module significantly enhances 1-day TEC prediction accuracy during storm periods, particularly in low-latitude regions where the model better captures large-scale TEC anomalies. Compared to the C1PG, the FIST-TECNet model reduces forecast errors by 35–40% at low latitudes. The feature fusion approach provides new insights into the spatiotemporal TEC modeling field.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Forecasting one-day global ionospheric TEC maps based on a modified 3D convolution U-Net incorporating fused index features

  • 期刊论文

  • 发表日期：

    2025年12月23日

查看全文
  • Xin Gao,
  • Fang Cheng,
  • Xiaochun Lu,
  • Yibin Yao,
  • Liang Zhang,
  • Yang Wang

摘要

Ionospheric total electron content (TEC) serves as a fundamental parameter for characterizing ionospheric morphology. Ionospheric TEC exhibits irregular disturbances driven by solar and geomagnetic activities. TEC forecasting products enhance GNSS positioning precision through error correction and space weather assessment, consequently improving satellite navigation system reliability and space weather warning systems. This paper proposes a modified spatiotemporal 3D convolutional U-Net architecture incorporating fused index features for forecasting 1-day global ionospheric TEC maps (FIST-TECNet). The input includes the previous day’s TEC maps and corresponding solar and geomagnetic indices, such as F10.7, SSN, Vsw, IMF Bz, Dst, and Kp indices. The FIST-TECNet model comprises an MLP-based fused feature generation module for solar and geomagnetic indices and a 3D convolutional spatiotemporal forecasting module. The primary contribution of this work involves the dimensional expansion of 1D solar and geomagnetic indices to achieve spatiotemporal alignment with 2D TEC grid maps. We benchmark the model against the C1PG, evaluating prediction performance under different geomagnetic storm intensities. The results demonstrate that the fused index generation module significantly enhances 1-day TEC prediction accuracy during storm periods, particularly in low-latitude regions where the model better captures large-scale TEC anomalies. Compared to the C1PG, the FIST-TECNet model reduces forecast errors by 35–40% at low latitudes. The feature fusion approach provides new insights into the spatiotemporal TEC modeling field.