14 citations as recorded by crossref.

1. EUREC<sup>4</sup>A's <i>Maria S. Merian</i> ship-based cloud and micro rain radar observations of clouds and precipitation C. Acquistapace et al. 10.5194/essd-14-33-2022
2. GNSS-based water vapor estimation and validation during the MOSAiC expedition B. Männel et al. 10.5194/amt-14-5127-2021
3. Diurnal variability of atmospheric water vapour, precipitation and cloud top temperature across the global tropics derived from satellite observations and GNSS technique Z. Baldysz et al. 10.1007/s00382-023-07005-0
4. Routine Measurement of Water Vapour Using GNSS in the Framework of the Map-Io Project P. Bosser et al. 10.3390/atmos13060903
5. Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC<sup>4</sup>A O. Bock et al. 10.5194/essd-13-2407-2021
6. Ocean cross-validated observations from R/Vs L'Atalante, Maria S. Merian, and Meteor and related platforms as part of the EUREC4A-OA/ATOMIC campaign P. L'Hégaret et al. 10.5194/essd-15-1801-2023
7. Evaluation of Shipborne GNSS Precipitable Water Vapor Over Global Oceans From 2014 to 2018 Z. Wu et al. 10.1109/TGRS.2022.3142745
8. Retrieving Accurate Precipitable Water Vapor Based on GNSS Multi‐Antenna PPP With an Ocean‐Based Dynamic Experiment J. Wei et al. 10.1029/2023GL102982
9. The Novel Copernicus Global Dataset of Atmospheric Total Water Vapour Content with Related Uncertainties from GNSS Observations K. Rannat et al. 10.3390/rs15215150
10. Ground- and ship-based microwave radiometer measurements during EUREC4A S. Schnitt et al. 10.5194/essd-16-681-2024
11. Assimilation of shipborne precipitable water vapour by Global Navigation Satellite Systems for extreme precipitation events Y. Ikuta et al. 10.1002/qj.4192
12. Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics Z. Baldysz et al. 10.3390/atmos12121698
13. Real-time shipborne multi-GNSS atmospheric water vapor retrieval over the South China Sea Z. Wu et al. 10.1007/s10291-023-01519-0
14. A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project G. Kłopotek et al. 10.1016/j.asr.2024.02.055