Machine Learning-Aided Tropospheric Delay Modeling over China

Real-time precise tropospheric corrections are critical for global navigation satellite system (GNSS) data processing. This chapter aims to develop a new tropospheric delay model over China with advanced machine learning method. Compared with previous models, the new model has features such as high accuracy, a small number of coefficients and good continuity of service, showing a good performance in severe weather conditions. The new model utilizes the complementary advantages of numerical weather prediction (NWP) forecasts and real-time GNSS observations with the aid of machine learning, which alleviates the high-dependency on the dense GNSS network and allows for the ease of generating tropospheric corrections. The results can provide a new insight into augmenting tropospheric delays for BeiDou Satellite-Based PPP service across China.