A Case Study of the 3D Water Vapor Tomography Model Based on a Fast Voxel Traversal Algorithm for Ray Tracing

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 12; p. 2422
• Main Authors: Hu, Heng, Liu, Min, Zhong, Jiqin, Deng, Xin, Cao, Yunchang, Fang, Peng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2021
• Subjects: 3D water vapor tomography; Accuracy; Algorithms; Deviation; Efficiency; Floors; Forecasting; GFS; Global navigation satellite system; Global positioning systems; GNSS/MET; GPS; Horizontal distribution; Humidity; Mathematical models; Meteorology; Navigation systems; Precipitation; Ray tracing; the fast voxel traversal algorithm; Tomography; Universal time; Vapor density; Vertical distribution; Water vapor; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13122422

A fast voxel traversal algorithm for ray tracing was applied to build a 4 × 4 × 20 tomography model using the observation data of 11 ground-based Global Navigation Satellite System (GNSS) meteorology (GNSS/MET) stations in Hebei Province, China. The precipitation water vapor (PWV) observed at 05 a.m. (Universal Time Coordinated: UTC) on 10 December 2019, was used to reconstruct three-dimensional (3D) water vapor density fields over the test area. The tomographic results (GNSS_T) show that the water vapor density above this area is mainly below 25 g/m3 and is concentrated between the first to the fourth layers. The vertical distribution conforms to the exponential characteristics, while the horizontal distribution shows a decreasing trend from southwest to northeast. In addition, the results of the 0.25° grid dataset generated by the Global Forecast System (GFS) of the National Center for Environmental Forecasting (NCEP) (GFS_L) were interpolated to the height of the tomographic grid, which is in good agreement with the tomographic results. GFS_L is larger than GNSS_T on the first floor at the surface, with an average deviation of 0.19 g/m3. In contrast, GFS_L from the second floor to the top of the model is smaller than GNSS_T, with the average deviations distributed between −0.08 and −0.15 g/m3.