GNSS water vapor tomography algorithm with regional water vapor evolution characteristics

This paper addresses the limitations of traditional methods for selecting prior information in water vapor tomography by proposing a dynamic prior water vapor information construction method based on regional water vapor evolution characteristics. By enhancing the consistency between the prior field and the meteorological background on the observation day, the method provides more reasonable initial constraints for GNSS water vapor tomography inversion, thereby improving inversion accuracy. Inversion experiments using GNSS observation data from the Continuous Operation Reference Station (CORS) network in the Hong Kong region of China in July 2023 demonstrate that this method significantly improves the prior water vapor density and overall inversion accuracy, while exhibiting good stability under varying weather conditions. Furthermore, the method shows strong scalability and the ability to integrate multi-source data, providing more robust and efficient prior support for GNSS water vapor tomography inversion.