59 citations as recorded by crossref.

1. Detecting Water Vapor Variability during Heavy Precipitation Events in Hong Kong Using the GPS Tomographic Technique B. Chen et al. https://doi.org/10.1175/JTECH-D-16-0115.1
2. Reconstruction of Wet Refractivity Field Using an Improved Parameterized Tropospheric Tomographic Technique B. Chen et al. https://doi.org/10.3390/rs12183034
3. A GPS water vapour tomography method based on a genetic algorithm F. Yang et al. https://doi.org/10.5194/amt-13-355-2020
4. Assessment of tomographic window and sampling rate effects on GNSS water vapor tomography F. Yang et al. https://doi.org/10.1186/s43020-023-00096-4
5. Spatiotemporal Analysis of GNSS-Derived Water Vapor Variations and Moisture Convergence Mechanism During Extreme Rainfall Events: A Hong Kong Case Study H. Qiu et al. https://doi.org/10.1109/JSTARS.2025.3605166
6. Enhancing GNSS water vapor tomography using machine learning-generated virtual tropospheric delays N. Huang et al. https://doi.org/10.1016/j.asr.2026.04.084
7. Statistical significance of trends in Zenith Wet Delay from re-processed GPS solutions A. Klos et al. https://doi.org/10.1007/s10291-018-0717-y
8. An optimal method for GNSS water vapor tomography based on parameter adaptation F. Yang et al. https://doi.org/10.1007/s10291-025-02006-4
9. Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe G. Guerova et al. https://doi.org/10.5194/amt-9-5385-2016
10. Modeling the Slant Wet Delays From One GPS Receiver as a Series Expansion With Respect to Time and Space: Theory and an Example of Application for the Tahiti Island F. Zhang et al. https://doi.org/10.1109/TGRS.2020.2975458
11. Pre-analysis of GNSS tomography solution using the concept of spread of model resolution matrix Z. Adavi et al. https://doi.org/10.1007/s00190-022-01620-1
12. Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication: Progress, Insights and Trends W. Song et al. https://doi.org/10.1109/JAS.2023.123588
13. An Improved Tomography Approach Based on Adaptive Smoothing and Ground Meteorological Observations B. Zhang et al. https://doi.org/10.3390/rs9090886
14. Cross-Comparison and Methodological Improvement in GPS Tomography H. Brenot et al. https://doi.org/10.3390/rs12010030
15. Retrieval of refractivity fields from GNSS tropospheric delays: theoretical and data-based evaluation of collocation methods and comparisons with GNSS tomography E. Shehaj et al. https://doi.org/10.1007/s00190-024-01903-9
16. Estimation of 3D wet refractivity by tomography, combining GNSS and NWP data: First results from assimilation of wet refractivity into NWP E. Trzcina & W. Rohm https://doi.org/10.1002/qj.3475
17. B-spline function-based approach for GPS tropospheric tomography S. Haji-Aghajany et al. https://doi.org/10.1007/s10291-020-01005-x
18. An overview of GNSS remote sensing K. Yu et al. https://doi.org/10.1186/1687-6180-2014-134
19. Application of the GPS reflected signals in tomographic reconstruction of the wet refractivity in Italy M. Shafei & M. Hossainali https://doi.org/10.1016/j.jastp.2020.105348
20. An Optimal Troposphere Tomography Technique Using the WRF Model Outputs and Topography of the Area S. Haji-Aghajany et al. https://doi.org/10.3390/rs12091442
21. TOMOREF Operator for Assimilation of GNSS Tomography Wet Refractivity Fields in WRF DA System E. Trzcina et al. https://doi.org/10.1029/2020JD032451
22. Assimilation of GNSS tomography products into the Weather Research and Forecasting model using radio occultation data assimilation operator N. Hanna et al. https://doi.org/10.5194/amt-12-4829-2019
23. Development of an adaptive 4-D water vapour density model for the vertical constraints in GNSS tropospheric tomography M. Zhang et al. https://doi.org/10.1007/s10291-024-01700-z
24. Tomographic Reconstruction of Water Vapor Density Fields From the Integration of GNSS Observations and Fengyun-4A Products B. Chen et al. https://doi.org/10.1109/TGRS.2023.3239392
25. A novel, optimized approach of voxel division for water vapor tomography Y. Yao & Q. Zhao https://doi.org/10.1007/s00703-016-0450-4
26. Application of integrated GNSS tomography in observation study over the area of southern Poland A. Cegla et al. https://doi.org/10.1016/j.asr.2024.07.059
27. Detecting Signatures of Giant Hail Events With Integrated GNSS Tomography: A Case Study Over Southern Poland A. Cegla et al. https://doi.org/10.1109/TGRS.2026.3659018
28. LEO satellites simulation for GNSS tomography S. Makuch & W. Rohm https://doi.org/10.1016/j.asr.2025.11.008
29. Adaptive Voxel-Based Model for the Dynamic Determination of Tomographic Region N. Ding et al. https://doi.org/10.3390/rs15020492
30. Real-Time Precise Point Positioning Using Tomographic Wet Refractivity Fields W. Yu et al. https://doi.org/10.3390/rs10060928
31. INTOMO operator for GNSS multi-source tomography based on 3D ray tracing technique A. Cegla et al. https://doi.org/10.1007/s00190-024-01915-5
32. An improved pixel-based water vapor tomography model Y. Yao et al. https://doi.org/10.5194/angeo-37-89-2019
33. An Investigation of Near Real-Time Water Vapor Tomography Modeling Using Multi-Source Data L. Tong et al. https://doi.org/10.3390/atmos13050752
34. A troposphere tomography method considering the weighting of input information Q. Zhao et al. https://doi.org/10.5194/angeo-35-1327-2017
35. Zenith total delay short-term statistical forecasts for GNSS Precise Point Positioning K. Wilgan https://doi.org/10.13168/AGG.2015.0035
36. 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
37. GNSS water vapor tomography based on Kalman filter with optimized noise covariance F. Yang et al. https://doi.org/10.1007/s10291-023-01517-2
38. A virtual-signal method for enhancing the efficacy of GNSS tropospheric tomography using artificial neural network technique M. Zhang et al. https://doi.org/10.1007/s10291-025-01935-4
39. Retrieving the 3-D Tropospheric Wet Refractivity Field From a Standalone Ground-Based GNSS Station With A Priori Information: Theory and Simulation X. Li et al. https://doi.org/10.1109/TGRS.2024.3412689
40. Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013 B. Chen & Z. Liu https://doi.org/10.5194/amt-9-5249-2016
41. A novel CNN-based method using GNSS tomography and WRF data for regional rainfall prediction L. Li et al. https://doi.org/10.1016/j.asr.2025.12.067
42. An optimal tropospheric tomography approach with the support of an auxiliary area Q. Zhao et al. https://doi.org/10.5194/angeo-36-1037-2018
43. Approach to leveraging real-time GNSS tomography usage A. Sá et al. https://doi.org/10.1007/s00190-020-01464-7
44. Use of Tropospheric Delay in GNSS-Based Climate Monitoring—A Review A. Maciejewska https://doi.org/10.3390/rs17091501
45. A New Method of GPS Water Vapor Tomography for Maximizing the Use of Signal Rays F. Yang et al. https://doi.org/10.3390/app9071446
46. A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network Y. Wang et al. https://doi.org/10.3390/rs12040617
47. Wide-Area Retrieval of Water Vapor Field Using an Improved Node Parameterization Tomography B. Chen et al. https://doi.org/10.1109/LGRS.2023.3293824
48. Enhancing InSAR accuracy: Unveiling more accurate displacement fields through 3-D troposphere tomography S. Maddahi et al. https://doi.org/10.1016/j.jastp.2024.106207
49. A new approach for GNSS tomography from a few GNSS stations N. Ding et al. https://doi.org/10.5194/amt-11-3511-2018
50. High-resolution GNSS troposphere tomography through explainable deep learning-based downscaling framework S. Haji-Aghajany et al. https://doi.org/10.1186/s43020-025-00177-6
51. Optimal Placement of Positioning Service for Multi-Robot System using Relative Angle Measurement K. Lin et al. https://doi.org/10.1016/j.ifacol.2025.11.278
52. A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network Y. Yao et al. https://doi.org/10.3390/rs12142306
53. Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing M. Heublein et al. https://doi.org/10.5194/angeo-38-179-2020
54. Capturing the Signature of Severe Weather Events in Australia Using GPS Measurements K. Zhang et al. https://doi.org/10.1109/JSTARS.2015.2406313
55. New parameterized model for GPS water vapor tomography N. Ding et al. https://doi.org/10.5194/angeo-35-311-2017
56. The effect of function-based and voxel-based tropospheric tomography techniques on the GNSS positioning accuracy S. Haji-Aghajany et al. https://doi.org/10.1007/s00190-021-01528-2
57. GNSS ground-based tomography: state-of-the-art and technological challenges S. Saxena & R. Dwivedi https://doi.org/10.1080/01431161.2023.2247526
58. Analyzing Different Parameterization Methods in GNSS Tomography Using the COST Benchmark Dataset Z. Adavi et al. https://doi.org/10.1109/JSTARS.2020.3027909
59. Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm N. Ding et al. https://doi.org/10.3390/rs12172744