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For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system (GNSS) tomography technique has become one of the most potential techniques for retrieving the three ‐dimensional (3D) distribution of water vapor with the advantages of high precision observations, low cost and all ‐weather monitoring. Methods: High horizontal resolution water vapor information provided by remote sensing satellites is introduced. We propose a GNSS water vapor tomography algorithm constrained with high horizontal resolution precipitable water vapor (PWV) data for the first time, which supplements and improves the constraints of existing water vapor tomography algorithms. In the proposed algorithm, firstly, the high horizontal resolution PWV observations are calibrated, and then the PWV constraint equations are constructed based on the densified tomographic voxels. Finally, the PWV constraint equations are included into the GNSS tomography model, which optimizes the constraint conditions and improves the tomographic results. Results: GNSS data and remote sensing water vapor data from FY‐3A satellite over Xuzhou area, China in August 2017 are used to assess the feasibility and accuracy of the proposed algorithm. Taking the high precision radiosonde water vapor profile and 3D water vapor density field from ERA5 as reference values, it can be observed that the proposed algorithm is superior to traditional method in retrieving water vapor profile and 3D water vapor distribution. Three kinds of accuracy indexes of the tomographic results have been significantly improved using the proposed method, with the mean root mean square error (RMSE) decreasing from 2.73 g/m3 to 1.78 g/m³, showing an improvement of 34.80%.Conclusions: This highlights that the improved tomography algorithm has significant potential to reconstruct the accurate and reliable 3D atmospheric water vapor distribution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]