Abstract Real‐time Global Ionospheric Map (RT‐GIM) products, provided by the International GNSS Service (IGS), are designed to support time‐sensitive applications by offering ionospheric information with only a few minutes of latency. However, their accuracy falls far short of the high‐precision Final GIM, particularly during periods of intense solar activity. Previous state‐of‐the‐art approaches, such as the Convolutional Neural Network‐Enhance (CNN‐Enhance) method, have attempted to narrow this gap, but they processed RT‐GIM data and solar parameters jointly without distinguishing their heterogeneous features, thereby limiting the ability to capture underlying physical relationships. To overcome these limitations, this paper proposes GSI‐UNet (Geomagnetic and Solar Indices‐guided U‐Net), a deep learning method that introduces dedicated encoders for RT‐GIM data and geomagnetic/solar indices, followed by feature fusion to generate an enhanced GIM. In the final GIM evaluation, GSI‐UNet further reduced the mean absolute error of RT‐GIM by 7.8% compared to the CNN‐Enhance method, improved accuracy across all latitude ranges, and achieved 10%–20% higher precision during geomagnetic storms. In single‐point positioning tests, the method maintained polar station accuracy within 4.8 m and further improved ocean‐based station accuracy by 1–2 m, confirming its effectiveness for real‐time ionospheric modeling under severe space weather conditions.
Zhuang et al. (2026) studied this question.