An Investigation of Near Real-Time Water Vapor Tomography Modeling Using Multi-Source Data
Laga Tong,
Kefei Zhang,
Haobo Li,
Xiaoming Wang,
Nan Ding,
Jiaqi Shi,
Dantong Zhu,
Suqin Wu
Affiliations
Laga Tong
Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou 221116, China
Kefei Zhang
Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou 221116, China
Haobo Li
School of Science (Geospatial), RMIT University, Melbourne, VIC 3001, Australia
Xiaoming Wang
Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Nan Ding
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
Jiaqi Shi
Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou 221116, China
Dantong Zhu
Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou 221116, China
Suqin Wu
Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, No. 1 Daxue Road, Xuzhou 221116, China
Global Navigation Satellite Systems (GNSS) tomography is a well-recognized modeling technique for reconstruction, which can be used to investigate the spatial structure of water vapor with a high spatiotemporal resolution. In this study, a refined near real-time tomographic model is developed based on multi-source data including GNSS observations, Global Forecast System (GFS) products and surface meteorological data. The refined tomographic model is studied using data from Hong Kong from 2 to 11 October 2021. The result is compared with the traditional model with physical constraints and is validated by the radiosonde data. It is shown that the root mean square error (RMSE) values of the proposed model and traditional model are 0.950 and 1.763 g/m3, respectively. The refined model can decrease the RMSE by about 46%, indicating a better performance than the traditional one. In addition, the accuracy of the refined tomographic model is assessed under both rainy and non-rainy conditions. The assessment shows that the RMSE in the rainy period is 0.817 g/m3, which outperforms the non-rainy period with the RMSE of 1.007 g/m3.
