AI-Enhanced GNSS Meteorology for Real-Time Correction of Microwave Remote Sensing Data

Dear Colleagues,

Tropospheric delay is a major error source for space geodetic techniques such as GNSS, VLBI, satellite altimetry, and InSAR, and its accurate estimation is critical for high-precision geodesy and atmospheric research. Traditional Zenith Tropospheric Delay (ZTD) estimation methods, largely based on empirical models and ground meteorological data, suffer from limited resolution, accuracy, and adaptability, particularly under rapidly changing weather conditions. Artificial intelligence (AI) offers innovative solutions by learning the complex spatiotemporal variability of the tropospheric delay from GNSS observations and reanalysis data. By directly retrieving ZTD/ZWD with high precision or optimizing key atmospheric parameters, AI methods significantly enhance modeling accuracy and reliability. AI-enhanced atmospheric products can support precise GNSS positioning and provide real-time, high-accuracy ZTD corrections for Earth observation systems, improving the consistency of multi-source data, and advancing high-precision space geodesy.

This Special Issue highlights recent advances in AI-enhanced tropospheric delay modeling to improve the accuracy of space geodetic techniques such as GNSS, InSAR, and satellite altimetry. We particularly welcome studies that explore multi-source observations and integrate artificial intelligence-based ZTD/ZWD estimation and correction into high-precision positioning and real-time remote sensing applications. By overcoming the limitations of traditional models, this Special Issue aims to promote innovation across geodesy, meteorology, and climate research.

Potential topics include, but are not limited to, the following:

• AI and machine learning methods for GNSS-based tropospheric delay estimation;
• Validation and intercomparison of AI-derived ZTD products against conventional techniques;
• GNSS tomography and 4D water vapor field reconstruction using AI methods;
• Uncertainty quantification and interpretability of AI-based tropospheric models;
• Assimilation of AI-enhanced GNSS and microwave remote sensing data into numerical weather prediction models;
• Transfer learning and physics-informed AI approaches in tropospheric delay prediction;
• Case studies on extreme weather events and climate applications.

We particularly encourage comprehensive review articles and cross-disciplinary studies that bridge GNSS, AI, and remote sensing communities.

Dr. Jungang Wang
Dr. Longjiang Tang
Guest Editors

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