Retrieving Precipitable Water Vapor from Real-Time Precise Point Positioning Using VMF1/VMF3 Forecasting Products

Real-time precise point positioning (RT-PPP) has become a powerful technique for the determination of the zenith tropospheric delay (ZTD) over a GPS (global positioning system) or GNSS (global navigation satellite systems) station of interest, and the follow-on high-precision retrieval of precipitable water vapor (PWV). The a priori zenith hydrostatic delay (ZHD) and the mapping function used in the PPP approach are the two factors that could affect the accuracy of the PPP-based ZTD significantly. If the in situ atmospheric pressure is available, the Saastamoinen model can be used to determine ZHD values, and the model-predicted ZHD results are of high accuracy. However, not all GPS/GNSS are equipped with an in situ meteorological sensor. In this research, the daily forecasting ZHD and mapping function values from VMF1 forecasting (VMF1_FC) and VMF3 forecasting (VMF3_FC) products were used for the determination of the GPS-derived PWV. The a priori ZHDs derived from VMF1_FC and VMF3_FC were first evaluated by comparing against the reference ZHDs from globally distributed radiosonde stations. GPS observations from 41 IGS stations that have co-located radiosonde stations during the period of the first half of 2020 were used to test the quality of GPS-ZTD and GPS-PWV. Three sets of ZTDs estimated from RT-PPP solutions using the a priori ZHD and mapping function from the following three VMF products were evaluated: (1) VMF1_FC; (2) VMF3_FC (resolution 5° × 5°); (3) VMF3_FC (resolution 1° × 1°). The results showed that, when the ZHDs from 443 globally distributed radiosonde stations from 1 July 2018 to 30 June 2021 were used as the reference, the mean RMSEs of the ZHDs from the three VMF products were 5.9, 5.4, and 4.3 mm, respectively. The ZTDs estimated from RT-PPP at 41 selected IGS stations were compared with those from IGS, and the results showed that the mean RMSEs of the ZTDs of the 41 stations from the three PPP solutions were 8.6, 9.0, and 8.6 mm, respectively, and the mean RMSEs of the PWV converted from their corresponding ZWDs were 1.9, 2.4, and 1.7 mm, respectively, in comparison with the reference PWV from co-located radiosonde stations. The results suggest that the a priori ZHD and mapping function from VMF1_FC and VMF3_FC can be used for the precise determination of real-time GPS/GNSS-PWV in most regions, especially the VMF3_FC (resolution 1° × 1°) product.

[1]  Harald Schuh,et al.  Estimation and evaluation of real-time precipitable water vapor from GLONASS and GPS , 2016, GPS Solutions.

[2]  T. Nilsson,et al.  GPT2: Empirical slant delay model for radio space geodetic techniques , 2013, Geophysical research letters.

[3]  H. Schuh,et al.  Short Note: A global model of pressure and temperature for geodetic applications , 2007 .

[4]  Kefei Zhang,et al.  A new global grid-based weighted mean temperature model considering vertical nonlinear variation , 2021 .

[5]  Yibin Yao,et al.  GGOS tropospheric delay forecast product performance evaluation and its application in real-time PPP , 2018, Journal of Atmospheric and Solar-Terrestrial Physics.

[6]  Jan Douša,et al.  Real-time zenith tropospheric delays in support of numerical weather prediction applications , 2014 .

[7]  Yingyan Cheng,et al.  Determination of zenith hydrostatic delay and its impact on GNSS-derived integrated water vapor , 2017 .

[8]  Eric Pottiaux,et al.  Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe , 2016 .

[9]  Johannes Böhm,et al.  Refined discrete and empirical horizontal gradients in VLBI analysis , 2018, Journal of Geodesy.

[10]  Xingxing Li,et al.  Real-time retrieval of precipitable water vapor from GPS and BeiDou observations , 2015, Journal of Geodesy.

[11]  Thomas Hobiger,et al.  Considering different recent advancements in GNSS on real-time zenith troposphere estimates , 2020, GPS Solutions.

[12]  Harald Schuh,et al.  Real-Time Tropospheric Delays Retrieved from Multi-GNSS Observations and IGS Real-Time Product Streams , 2017, Remote. Sens..

[13]  Guangxing Wang,et al.  The impact of second-order ionospheric delays on the ZWD estimation with GPS and BDS measurements , 2020, GPS Solutions.

[14]  Johannes Böhm,et al.  VMF3/GPT3: refined discrete and empirical troposphere mapping functions , 2017, Journal of Geodesy.

[15]  Witold Rohm,et al.  Ground-based GNSS ZTD/IWV estimation system for numerical weather prediction in challenging weather conditions , 2014 .

[16]  J. Kouba.  Implementation and testing of the gridded Vienna Mapping Function 1 (VMF1) , 2008 .

[17]  Harald Schuh,et al.  Real-Time Tropospheric Delay Retrieval from Multi-GNSS PPP Ambiguity Resolution: Validation with Final Troposphere Products and a Numerical Weather Model , 2018, Remote. Sens..

[18]  Yunbin Yuan,et al.  An analysis of multisource tropospheric hydrostatic delays and their implications for GPS/GLONASS PPP-based zenith tropospheric delay and height estimations , 2021, Journal of Geodesy.

[19]  D. Laurichesse,et al.  Comparative analysis of real-time precise point positioning zenith total delay estimates , 2016, GPS Solutions.

[20]  Máté Mile,et al.  The use of GNSS zenith total delays in operational AROME/Hungary 3D-Var over a central European domain , 2019, Atmospheric Measurement Techniques.

[21]  Allison Kealy,et al.  Assessment of forecast Vienna Mapping Function 1 for real-time tropospheric delay modeling in GNSS , 2019, Journal of Geodesy.

[22]  J. Saastamoinen.  Atmospheric Correction for the Troposphere and Stratosphere in Radio Ranging Satellites , 2013 .

[23]  Lewen Zhao,et al.  Performance Evaluation of Troposphere Estimated from Galileo-Only Multi-Frequency Observations , 2020, Remote. Sens..

[24]  Fei Guo,et al.  Real-time tropospheric delay retrieval with GPS, GLONASS, Galileo and BDS data , 2018, Scientific Reports.

[25]  Barry E. Schwartz,et al.  Rapid retrieval and assimilation of ground based GPS precipitable water observations at the NOAA Forecast Systems Laboratory: Impact on weather forecasts , 2004 .

[26]  Kamil Kazmierski,et al.  Optimum stochastic modeling for GNSS tropospheric delay estimation in real-time , 2017, GPS Solutions.

[27]  Xiaohong Zhang,et al.  The Effects of Higher-Order Ionospheric Terms on GPS Tropospheric Delay and Gradient Estimates , 2018, Remote. Sens..

[28]  Yang Gao,et al.  Impacts of real-time satellite clock errors on GPS precise point positioning-based troposphere zenith delay estimation , 2015, Journal of Geodesy.

[29]  Robert Weber,et al.  Development of an improved empirical model for slant delays in the troposphere (GPT2w) , 2015, GPS Solutions.