Improving GNSS PPP Performance in the South China Under Different Weather Conditions by Using the Weather Research and Forecasting (WRF) Model-Derived Wet Delay Corrections

Atmospheric wet delay caused by Precipitable Water Vapor (PWV) significantly impacts the performance of many geodetic surveying systems such as Global Navigation Satellite System (GNSS). In this study, we use wet delay corrections forecast by the Weather Research and Forecasting (WRF) model to enhance GNSS Precise Point Positioning (PPP) during two observation periods with two different weather conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m2) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m2), over the South China. PWV data from 277 to 263 GNSS stations are assimilated into WRF model to enhance the WRF water vapor forecasting capability for period 1 and period 2, respectively. Wet delay corrections from two different WRF configurations, that is, WRF no data assimilation and WRF with assimilation of GNSS PWV, are used to augment the PPP. Totally, eight WRF-enhanced PPP schemes are tested. The results show that WRF-enhanced PPP schemes generally have a better positioning performance in the up component than traditional PPP. After using WRF wet delay corrections, for static mode, the vertical positioning accuracy is improved by 14.6% and 33.7% for period 1 and period 2, respectively. The corresponding convergence time are reduced by 41.8% and 25.0% for period 1 and period 2, respectively. For kinematic mode, the positioning accuracy improvements in the up component reach 13.8% and 19.0% for period 1 and period 2, respectively. The kinematic PPP convergence time is reduced by up to 8.2% for period 1. Global Navigation Satellite System (GNSS) signal is significantly delayed by the water vapor in the Earth's troposphere. How to mitigate this so-called wet delay has long been a challenging problem in GNSS Precise Point Positioning (PPP) community. In this study, we use the Weather Research and Forecasting (WRF) model to simulate the distribution and variation of water vapor over the South China during two periods with different water vapor conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m2) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m2). In addition, water vapor data from 277 and 263 GNSS stations have been assimilated into the WRF model for period 1 and period 2, respectively, to further improve WRF water vapor forecasting accuracy. The high-quality water vapor forecasts are then used to correct the wet delay for GNSS PPP. Our results show that, with use of the wet delay corrections forecast by the WRF model, GNSS PPP can achieve a better performance in positioning accuracy and convergence speed during both two periods. Weather Research and Forecasting (WRF)-enhanced Precise Point Positioning (PPP) strategies improve positioning accuracy in the up component for both static and kinematic modes WRF-enhanced PPP strategies accelerate static positioning convergence speed in the up component WRF-enhanced PPP strategies show a larger positioning accuracy improvement in wet period