Abstract
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.
Highlights
Accepted: 18 June 2021Changes in water vapor over time and space have important indications for meteorological forecasting [1,2], especially for the monitoring and forecasting of smalland medium-scale severe weather with a horizontal scale of about 100 km and a life history of only a few hours [3,4,5]
In order to analyze the accuracy of the tomography model more clearly, we calibrated the GNSS_T and the GFS_L from the altitude and horizontal position respectively before the comparison
First of all,for thethe horizontal distribution stations is GNSSsatisfy water all vapor tomography model first time, which has of high feasibility
Summary
Accepted: 18 June 2021Changes in water vapor over time and space have important indications for meteorological forecasting [1,2], especially for the monitoring and forecasting of smalland medium-scale severe weather with a horizontal scale of about 100 km and a life history of only a few hours [3,4,5]. Accurate atmospheric water vapor monitoring and its assimilation in the numerical weather forecasting model will improve the prediction accuracy of precipitation and severe weather [6,7]. Satellite, and ground data have been used as supplementary observations to initialize mesoscale models in recent years, their applications are limited by the lower spatial resolution and retrieval accuracy [8,9]. Meteorological products such as precipitable water vapor (PWV), total zenith delay (ZTD), and zenith wet delay (ZWD) obtained by ground-based Global Navigation Satellite
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