11 citations as recorded by crossref.

1. Machine Learning-Based Wet Refractivity Prediction Through GNSS Troposphere Tomography for Ensemble Troposphere Conditions Forecasting S. Haji-Aghajany et al. https://doi.org/10.1109/TGRS.2024.3417487
2. ZWDX: a global zenith wet delay forecasting model using XGBoost L. Crocetti et al. https://doi.org/10.1186/s40623-024-02104-6
3. Detecting Signatures of Convective Storm Events in GNSS‐SNR: Two Case Studies From Summer 2021 in Switzerland M. Aichinger‐Rosenberger et al. https://doi.org/10.1029/2023GL104916
4. A Novel Multilayer Perceptron-Based Nonmeteorological Parameters PWV Retrieval Model H. Zhang et al. https://doi.org/10.1109/TGRS.2024.3423471
5. Combining Kriging and Neural Networks for Station‐Based Precipitation Forecasting in Radar‐Sparse Alpine Regions E. Freudiger & V. Lurkin https://doi.org/10.1002/met.70252
6. Global Navigation Satellite System (GNSS) radio occultation climatologies mapped by machine learning and Bayesian interpolation E. Shehaj et al. https://doi.org/10.5194/amt-18-57-2025
7. Exploring the troposphere with GNSS: Advances in modeling, monitoring, and applications C. Lu et al. https://doi.org/10.1016/j.earscirev.2026.105610
8. Enhanced Troposphere Tomography: Integration of GNSS and Remote Sensing Data With Optimal Vertical Constraints S. Izanlou et al. https://doi.org/10.1109/JSTARS.2024.3354884
9. Use of Tropospheric Delay in GNSS-Based Climate Monitoring—A Review A. Maciejewska https://doi.org/10.3390/rs17091501
10. Machine-learning-based identification of influencing factors and synoptic patterns of foehn on the eastern foothills of the Taihang Mountains, China X. Xu et al. https://doi.org/10.5194/acp-26-6507-2026
11. Research using GNSS (Global Navigation Satellite System) products – a comprehensive literature review A. Maciejewska & K. Maciuk https://doi.org/10.1515/jag-2024-0110