Real-time retrieval of precipitable water vapor from GPS precise point positioning

2014 ◽  
Vol 119 (16) ◽  
pp. 10044-10057 ◽  
Author(s):  
Yubin Yuan ◽  
Kefei Zhang ◽  
Witold Rohm ◽  
Suelynn Choy ◽  
Robert Norman ◽  
...  
Keyword(s):  
Water Vapor ◽  
Real Time ◽  
Precise Point Positioning ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Point Positioning

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

scholarly journals PROCESSING STRATEGY FOR NEAR REAL TIME GPS PRECIPITABLE WATER VAPOR RETRIEVAL

2007 ◽  
Vol 24 (4) ◽  
pp. 275-284
Author(s):  
Jeong-Ho Baek ◽  
Jae-Won Lee ◽  
Byung-Kyu Choi ◽  
Jung-Ho Cho
Keyword(s):  
Water Vapor ◽  
Real Time ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Processing Strategy

Real-time retrieval of precipitable water vapor from GPS and BeiDou observations

2015 ◽  
Vol 89 (9) ◽  
pp. 843-856 ◽  
Author(s):  
Cuixian Lu ◽  
Xingxing Li ◽  
Tobias Nilsson ◽  
Tong Ning ◽  
Robert Heinkelmann ◽  
...  
Keyword(s):  
Water Vapor ◽  
Real Time ◽  
Precipitable Water ◽  
Precipitable Water Vapor
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