A refined zenith tropospheric delay model for Mainland China based on the global pressure and temperature 3 (GPT3) model and random forest

Zenith Tropospheric Delay (ZTD) plays a vital role in Global Navigation Satellite System (GNSS) navigation, positioning, and meteorology. The generally accepted empirical models that are now in use can only reflect the periodic changes in ZTD. Nevertheless, capturing its subtle nonlinear changes, like rapid ZTD variations, is challenging as its accuracy needs further improvement. To overcome these drawbacks, the relationship between the residuals of GPT3 ZTD minus GNSS ZTD and the spatiotemporal information was fitted by using random forests (RF). Consequently, a refined model of GPT3 ZTD was established in Mainland China (named RGPT3), and the performance of the proposed model was compared to two accepted empirical models and another model based on a popular algorithm of machine learning (backpropagation neural network algorithm). Based on the results of the study, the RMSE of RGPT3 is 1.83 cm, which has an improvement of 28.0, 16.8, and 34.4% over the three compared models. The RGPT3 performs better in capturing the instantaneous ZTD changes than the empirical models. The result of RGPT3 ZTD constraint GNSS precise point positioning (PPP) is also superior to that of GPT3 ZTD, with the U direction convergence time reduction of 12.3% and accuracy improvement of 7.9%. The new model can offer higher-precision ZTD predictions in the study area.