A High-Precision Real-Time PWV Grid Model for the China Region and Its Preliminary Performance in WRF Assimilation

Precipitable water vapor (PWV) is a key parameter in studying water vapor variations during severe weather phenomena. The high-quality PWV maps are also of significant value for monitoring and early warning of geological disasters, such as landslides and debris flows. This study presents a high-precision real-time PWV grid model for the China region, utilizing global navigation satellite system (GNSS) observations and surface meteorological data. The model addresses the limitations of existing PWV retrieval methods by incorporating an improved altitude correction model for pressure and temperature using ERA5 reanalysis data. The model achieves a spatial resolution of 0.5° × 0.5° and incorporates real-time updates for accurate monitoring of atmospheric moisture variations. The model's performance was evaluated using surface meteorological observations and compared with the HGPT2 model. Results showed that the new model outperforms HGPT2 in terms of accuracy, particularly in low-latitude regions. In addition, the model was successfully assimilated into the weather research and forecasting (WRF) model, significantly improving the accuracy of the initial atmospheric field for numerical weather prediction. This study demonstrates the potential of GNSS and surface meteorological data in constructing high-resolution, real-time PWV models. The developed model provides valuable insights into atmospheric moisture variations and enhances the accuracy of weather forecasting and climate research in the China region.