Bo Shi

Tropospheric delay is recognized as one of the main error sources affecting Global Navigation Satellite System (GNSS) positioning accuracy. Previous studies have only employed artificial intelligence-based weather models with low temporal resolution for comprehensive assessments. Therefore, this study proposes an ensemble forecasting approach based on multiple initial conditions from the Pangu-Weather model to obtain hourly resolution tropospheric delays. The ZTD data from 250 Crustal Movement Observation Network of China (CMONOC) GNSS stations across China in 2020 are used to validate the accuracy of the Pangu-Weather model. The findings show that the Pangu-Weather model exhibits strong performance under both forecast lead times compared to the traditional Global Forecast System (GFS) product, particularly in southern China. However, the Pangu-Weather model provides slightly inferior forecast accuracy compared to the GFS product in dry, low-humidity regions at stations located between 2 and 4 km in altitude, and for forecast lead times of less than 9 h. Nevertheless, a lower error accumulation trend is exhibited by the Pangu-Weather model, as its RMSE is larger than that of the Global Pressure and Temperature 3 (GPT3) empirical model after 240 h (10 days), demonstrating more stable accuracy over longer forecast periods. In summary, the Pangu-Weather model shows significant advantages in Chinese regions with complex climates and terrains, and it is of great potential in GNSS real-time positioning and meteorological monitoring.