Water vapor monitoring with IGS RTS and GPT3/VMF3 functions over Turkey

Abstract

Precise Point Positioning (PPP) has been proven as a cost-effective and efficient technique for monitoring the precipitable water vapor (PWV), which plays a critical role in weather forecasting. The recent developments of the International Global Navigation Satellite Systems Service (IGS) Real-Time Pilot Project (RTPP) ease the difficulties of real-time determination of PWV. In this study, the quality of the real-time PPP-derived PWVs and their correlation with precipitation estimates from different global products are investigated. The accuracies of the orbits and clocks derived from the IGS02 streams are evaluated and their performance outperforms the predicted part of IGS Ultra-rapid (IGU) orbits and clocks. A year of GNSS observations from 9 stations located in Turkey are processed to estimate the PWV in the real-time. Since there are no stations to collect meteorological data near GNSS stations, the real-time solutions employ empirical Global Pressure and Temperature 3 (GPT3) and its fully consistent discrete Vienna Mapping Function 3 (VMF3) to model the troposphere. The performance of the PWV estimates is assessed by comparing with Radiosonde-derived PWV values and post-processed PPP-derived PWV estimates employing the Center for Orbit Determination in Europe (CODE) final orbits and clocks. At most sites, the root-mean-square (RMS) differences of the estimated PWVs in comparison with the radiosonde-derived PWVs are less than 2.4 mm with correlation coefficients greater than 0.90. The RMS differences between RT and post-processed PPP PWV estimates are found to be less than 1.2 mm. The estimated RT zenith total delays (ZTDs) at two IGS sites agree well with the ZTDs provided by the IGS final products. A method of precipitation monitoring is proposed based on the daily standard deviation (SD) of the PWV estimates. Experimental analyses of the proposal are undertaken by comparing the daily SDs of the PWV estimates and the daily accumulated precipitation estimates from the global precipitation grids produced from rain gauges, satellite images, and reanalyses product sets. The results show that the daily variation of the PWVs and the high precipitation estimates present a good agreement for some days but not always. The PWVs overestimate and underestimate a significant number of events. Spatial refinement of GPT3 grids and gridded precipitation products with higher sampling over Turkey may offer better results.