Adaptive Voxel-Division Method of GNSS Water Vapor Tomography and Its Application in Data Assimilation - Yongjie Ma; Qingzhi Zhao; Duoduo Jiang; Wanqiang Yao; Yibin Yao; Jinfang Yin; Hongwu Guo; Yuan Zhai; Ying Xu; Ruikun Wang; Qingfang Chen; Jingyu Zhang (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025)
The global navigation satellite system (GNSS) water vapor tomography technique can retrieve high-quality water vapor profiles and holds significant potential for improving the performance of the initial 3-D water vapor field in numerical weather prediction (NWP). However, the empirical voxel-division method of GNSS water vapor tomography has been predominantly used before, and existing tomographic results are rarely incorporated into the NWP models due to the lack of a direct assimilation interface, which becomes the focus of this study. An adaptive voxel-division method for GNSS water vapor tomography is first proposed. The optimal horizontal and vertical steps of water vapor tomography are determined by introducing the principles of maximum grid coverage and the equal amount of layered water vapor. In addition, a two-step variational assimilation method has been developed to address the limitation of existing NWP models without a direct interface for assimilating GNSS water vapor tomographic results. The proposed water vapor tomography method and its application in the weather research and forecasting (WRF) model are comprehensively analyzed and evaluated. Numerical results in Hong Kong show the superior performance of the proposed adaptive voxel-division method for GNSS water vapor tomography compared with empirical methods. The average improvement rate of the root-mean-square error (RMSE) in the water vapor profile ranges from 10.6% to 48.7%, while that in the integrated water vapor is 19.0–42.9% . Furthermore, the assimilated and forecasted results of assimilating GNSS tomographic results under different weather conditions are validated and proved the significantly positive contributions of water vapor profiles to the WRF model. Specifically, the RMSE reduction in precipitation is 43.5%, while that for relative humidity, temperature, and pressure are 50.7% /58.2%, 41.1% /48.3%, and 24.2% /33.7%, respectively, under no-rain and rain conditions compared with the traditional assimilation method. These results show the good performance of the proposed adaptive voxel-division method for water vapor tomography and the two-step method for assimilating the tomographic water vapor profile, which highlight the significant potential of the GNSS tomographic result for data assimilation.