13 citations as recorded by crossref.

1. GNSS-Based Machine Learning Storm Nowcasting M. Łoś et al. 10.3390/rs12162536
2. Applying principal components to analyze the distribution of model biases in GNSS tropospheric tomography for a case study in Northwestern Iran M. Mashhadi Hossainali & H. Tabatabaei 10.1007/s10291-022-01315-2
3. Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece C. Giannaros et al. 10.3390/rs12030383
4. Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution E. Trzcina et al. 10.1007/s00190-022-01691-0
5. An Improved Tropospheric Tomographic Model Based on Artificial Neural Network M. Zhang et al. 10.1109/JSTARS.2023.3278302
6. GNSS ground-based tomography: state-of-the-art and technological challenges S. Saxena & R. Dwivedi 10.1080/01431161.2023.2247526
7. Fast Observation Operator for Global Navigation Satellite System Tropospheric Gradients F. Zus et al. 10.3390/rs15215114
8. A new method for tropospheric tomography using GNSS and Fengyun-4A data M. Zhang et al. 10.1016/j.atmosres.2022.106460
9. Precipitable Water Content Climatology over Poland H. Ojrzyńska et al. 10.3390/atmos13060988
10. Tropospheric and range biases in Satellite Laser Ranging M. Drożdżewski & K. Sośnica 10.1007/s00190-021-01554-0
11. An Investigation of Near Real-Time Water Vapor Tomography Modeling Using Multi-Source Data L. Tong et al. 10.3390/atmos13050752
12. Pre-analysis of GNSS tomography solution using the concept of spread of model resolution matrix Z. Adavi et al. 10.1007/s00190-022-01620-1
13. On the Potential of Improving WRF Model Forecasts by Assimilation of High-Resolution GPS-Derived Water-Vapor Maps Augmented with METEOSAT-11 Data A. Leontiev et al. 10.3390/rs13010096