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.

中文翻译：

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利用天气研究和预报 (WRF) 模型导出的湿延迟修正提高华南地区不同天气条件下的 GNSS PPP 性能

可降水水蒸气 (PWV) 引起的大气湿延迟会严重影响全球导航卫星系统 (GNSS) 等许多大地测量系统的性能。在本研究中，我们使用天气研究和预报（WRF）模型的湿延迟修正预报来增强 GNSS 精确单点定位（PPP）在具有两种不同天气条件的两个观测周期（即周期 1：3 月 1 日至 14 日）期间的效果。 2020年（平均PWV：23.5 kg/m ²）和第二阶段：2020年6月2日至15日（平均PWV：55.6 kg/m ²的洪水天气），华南地区。来自277至263个GNSS站的PWV数据被同化到WRF模型中，以分别增强WRF第一阶段和第二阶段的水汽预报能力。来自两种不同 WRF 配置（即无数据同化的 WRF 和具有 GNSS PWV 同化的 WRF）的湿延迟校正用于增强 PPP。总共测试了八种 WRF 增强型 PPP 方案。结果表明，WRF增强型PPP方案在上行部分总体上比传统PPP具有更好的定位性能。使用WRF湿延迟校正后，对于静态模式，周期1和周期2的垂直定位精度分别提高了14.6%和33.7%。第 1 期和第 2 期相应的收敛时间分别减少了 41.8% 和 25.0%。对于运动模式，上组件的定位精度在周期 1 和周期 2 中分别提高了 13.8% 和 19.0%。第 1 期的运动学 PPP 收敛时间最多减少 8.2%。