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Atmospheric water vapor plays an essential role in climate change and weather forecasting. However, monitoring water vapor with high spatial and temporal resolutions remains a challenge, especially over ocean regions where observations are insufficient. Shipborne global navigation satellite systems (GNSSs) contribute to enriching water vapor measurements over oceans and also can help validate satellite observations. Due to the lack of long-time serial observations, the performance of shipborne GNSS-derived precipitable water vapor (PWV) is inadequately evaluated on the global ocean scale. In this study, an overall assessment of shipborne GNSS PWV over global oceans is performed based on six voyages from 2014 to 2018. In coastal areas, the PWV differences of shipborne GNSS with respect to (w.r.t.) ground-based GNSS and ground-launched radiosonde data are 2.64 and 2.85 mm in the root mean square (rms), respectively. In open oceans, compared to ship-launched radiosonde profiles and satellite measurements, shipborne GNSS PWV shows the rms of differences of 2.54 and 2.53 mm, respectively. In addition, the rms of PWV differences between the whole track of shipborne GNSS PWV and National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) products is 2.96 mm. The intertechnique validations demonstrate that the accuracy of shipborne GNSS PWV is superior to 3 mm, which meets the requirements of climate research and numerical weather prediction (NWP). [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) provide a promising opportunity for real‐time precipitable water vapor (PWV) sensing. However, relying on meteorological information restrains the implementation of real‐time inversion from tropospheric zenith total delays (ZTD) into PWV. In this study, a stacked machine learning model for mapping ZTD into PWV in the absence of meteorological parameters is developed. The model performance is assessed and validated with information offered by fifth generation European Centre for Medium‐Range Weather Forecasts reanalysis (ERA5) and radiosondes. An accuracy of better than 2.5 mm is achievable for the PWV values. Compared to the physical model which applies GPT3‐derived meteorological parameters, the proposed model reveals an enhanced performance, especially in the high‐latitude regions, with improvements of 28.1% and 22.2% when validated with ERA5 and radiosondes. This model is capable of fulfilling the demands of time‐critical meteorological applications and is also promising for real‐time PWV retrieval of other techniques that own the capability to sense ZTD. Plain Language Summary: The measurement of precipitable water vapor (PWV) in the atmosphere is very important to weather monitoring and forecasting. Global Navigation Satellite System observations provide a good opportunity for such measurement but are kept from practical due to a lack of timely meteorological observations. We developed a machine learning method to solve this problem by extracting weather patterns from the existing data sets and use it to create maps of the PWV without using meteorological information. The developed model reveals good performance and shows promising prospects for other techniques involved in atmospheric water vapor sensing. Our method also has the potential for further development of a real‐time water vapor product. Key Points: A machine learning model for mapping the zenith total delays (ZTD) to precipitable water vapor in the absence of meteorological quantities is developedAccuracy better than 2.5 mm is achievable for model predictions, which fulfills the demands of time‐critical meteorological applicationsThis model is promising for real‐time water vapor inversion of other techniques that own the capability to sense the ZTD [ABSTRACT FROM AUTHOR]

The rapid development of the European Galileo system brings a great opportunity for the real-time retrieval of atmospheric parameters. In this contribution, Galileo observations are employed to retrieve real-time water vapor based on the precise point positioning (PPP) technique, where the benefit of ambiguity resolution on water vapor sensing is also investigated. The obtained atmospheric parameters, including zenith tropospheric delay (ZTD) and precipitable water vapor (PWV), are validated with respect to the postprocessing Global Positioning System (GPS) ZTD products and the PWV products derived from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results show that the real-time ZTDs, derived from the Galileo PPP solutions, agree well with the postprocessing GPS ZTDs. An averaged root-mean-square (rms) value of 8.5 mm for the ZTD differences is achieved for the float solution after an averaged initialization process of about 27.4 min. In terms of the fixed solution, the averaged rms value is decreased to 7 mm and the initialization time is shortened to 20.8 min, showing an improvement of 17.6% and 24.1%, respectively, when compared to the float solution. Furthermore, the derived Galileo-PWVs display good agreement with the ECMWF PWVs, with an accuracy of 1.9 and 1.7 mm for the float and the fixed solution, respectively. [ABSTRACT FROM AUTHOR]

The rapid development of the Chinese BeiDou Navigation Satellite System (BDS) brings a promising prospect for the real-time retrieval of zenith tropospheric delays (ZTD) and precipitable water vapor (PWV), which is of great benefit for supporting the time-critical meteorological applications such as nowcasting or severe weather event monitoring. In this study, we develop a real-time ZTD/PWV processing method based on Global Positioning System (GPS) and BDS observations. The performance of ZTD and PWV derived from BDS observations using real-time precise point positioning (PPP) technique is carefully investigated. The contribution of combining BDS and GPS for ZTD/PWV retrieving is evaluated as well. GPS and BDS observations of a half-year period for 40 globally distributed stations from the International GNSS Service Multi-GNSS Experiment and BeiDou Experiment Tracking Network are processed. The results show that the real-time BDS-only ZTD series agree well with the GPS-only ZTD series in general: the RMS values are about 11-16 mm (about 2-3 mm in PWV). Furthermore, the real-time ZTD derived from GPS-only, BDS-only, and GPS/BDS combined solutions are compared with those derived from the Very Long Baseline Interferometry. The comparisons show that the BDS can contribute to real-time meteorological applications, slightly less accurately than GPS. More accurate and reliable water vapor estimates, about 1.3-1.8 mm in PWV, can be obtained if the BDS observations are combined with the GPS observations in the real-time PPP data processing. The PWV comparisons with radiosondes further confirm the performance of BDS-derived real-time PWV and the benefit of adding BDS to standard GPS processing. [ABSTRACT FROM AUTHOR]