| Atmospheric water vapor plays a very important role in atmospheric energy transport,weather system evolution,earth-atmosphere system radiation budget balance,global climate change,etc.The spatial and temporal variability of water vapor inherits crucial information for the regional meteorology and global climate system.Studying the spatial and temporal distribution of water vapor,modeling the vertical distribution of water vapor,and analyzing the correlation between water vapor and weather phenomena can not only enhance the in-depth understanding of atmospheric issues related to water vapor,but also strengthen our in-depth understanding of the analysis of climate change,i.e.,the spatial and temporal distribution characteristics of water vapor,atmospheric process research,climate model parameterization,and weather forecasting.This thesis focuses on the high-precision inversion of the spatial and temporal distribution of water vapor by ground-based Global Navigation Satellite Systems(GNSS)technology,and aims to improve or enhance the key techniques in the inversion,with the following main contributions and conclusions:(1)First,a new water vapor parameter,IRPWV,is proposed,and then based on the IRPWV and its vertical distribution,the time series of water vapor vertical structure from 2000 to 2019 at 9 radiosonde stations in China were investigated and analyzed,and three types of water vapor vertical structures were summarized.For the first type,the IRPWV shows an increasing trend with decreasing pressure from 1000 to 750 h Pa,while it decreases with decreasing pressure from 750 to 250 h Pa.The second type,the IRPWV shows to be fairly constant or with small variations with decreasing pressure from 1000 to 750 h Pa and the IRPWV decreases with decreasing pressure from 750 to250 h Pa.The third type,the IRPWV decreases with decreasing pressure from 1000 to250 h Pa.As the surface IRPWV(IRPWVs)changes from small to large,the vertical structure of water vapor gradually transitions from the first type to the second type and then to the third type,and finally tends to be stable.The three vertical structure types show that the water vapor content(WVC)is mainly distributed in the range of 1000 to700 h Pa,while WVC of the first type in the range of 850 to 700 h Pa is more than that of in the range of 1000 to 925 h Pa,WVC of the second type is approximately uniformly distributed in the pressure range,and WVC of the third type decreases gradually with the pressure from 1000 to 700 h Pa.As a structural index to measure the vertical distribution of water vapor,the IRPWV and its vertical distribution unify the vertical distribution of water vapor of different spatio-temporal over different geographic regions.(2)Based on the correlation characteristics of the vertical structure of water vapor and its variation with the total precipitable water vapor(TPWV)and surface temperature(Ts),respectively,the results show that,first,there is a strong correlation between the vertical structure of water vapor and the relative magnitude of TPWV at the same time scale,the larger the relative magnitude of TPWV,the more stable the vertical structure of water vapor,and vice versa.Secondly,the trend of IRPWV in the middle-pressure layer(850-700 h Pa)is completely opposite to that in the lower layer(1000 and 925 h Pa),and more consistent with that in the upper layer(500-250 h Pa).Finally,under the same Ts conditions,the larger the TPWV the more stable the vertical structure of water vapor,and under the same TPWV conditions,the smaller the Ts the more stable the vertical structure of water vapor.The comprehensive analysis indicated that the vertical structure of water vapor is not determined by a single factor but is jointly correlated with multiple factors such as TPWV,Ts,and IRPWVs.(3)Hong Kong,China,is selected as the experimental region,and the time series of IRPWV and TPWV from 2008 to 2018(11 years)are used for the analyses.Based on the strong correlation between the vertical structure of water vapor and its corresponding relative magnitude of TPWV,firstly,the relative magnitude of TPWV is determined by the TPWV partitioning criteria,i.e.,6 TPWV time series ranges,then based on the periodic characteristics of the IRPWV time series in each of the 6 TPWV ranges,IRPWV spatio-temporal models with six set coefficients for 6 TPWV ranges were developed.An experiment was carried out by four GNSS tomographic schemes using GNSS observation data in 2019.The IRPWV spatio-temporal model is used in scheme 1(SCH1),and the traditional vertical constraint models are the exponential functions which with mean value,periodic function value,and real-time values of water vapor scale height,respectively were used on the other three schemes.Results from the four schemes were compared against the reference values derived from sounding data in 2019,first,the statistical results of the relative errors of daily water vapor density(WVD)less than 30%for SCH1 improved by at least 10%and 49%over that of the other three schemes in the lower troposphere(below 3 km,except for the surface)and the upper layer(about 5-10 km),respectively;secondly,the proportion of skill scores greater than 10 for monthly root mean square error(RMSE)of layered WVD resulted from SCH1 relative to the other three schemes is about 83%,87%,and 64%,respectively;finally,the annual bias of layered WVD of SCH1 is significantly reduced by 1.1-1.5 g/m~3 in tomographic height layer 2 and 3(about 1 km),and its annual RMSE is improved by 13%-42%and 5%-47%in the lower troposphere(about 0.6-3 km)and upper troposphere(about 5-10 km),respectively.Overall,the IRPWV spatio-temporal model performs better in the GNSS water vapor tomographic model and significantly improves the accuracy of its result.(4)The adaptive point cloud water vapor tomographic model is proposed to address the unsuitability of the traditional GNSS water vapor tomographic model.According to the spatial shape formed by the signal paths of the ground-based GNSS reference station network,the tomographic space is adaptively divided,the water vapor observation is described by the hybrid segmentation parameterization method,and the point clouds distributed in the tomographic space are adaptively generated according to the numbers of water vapor observation and the principles of point cloud selection,distribution and layout principles.Compared with the traditional water vapor tomographic model,firstly,the spatial distribution of the point cloud conforms to the shape of the spatial distribution of the signal paths,which solves the shortcomings of the traditional grid dividing,while increasing the utilization rate of the observation information to 100%.Secondly,the numbers of point clouds are less than or equal to that of observation,which solves the ill-posed problem of the traditional tomographic observation equations.The GNSS observation data of July 2019 in Hong Kong,China,are selected for the feasibility experiments of the adaptive point cloud water vapor tomographic model.Firstly,WVD resulted from the tomographic model were compared against the reference values of ERA5 and sounding data,respectively,which show a good agreement with that of the two.The maximum monthly bias and monthly RMSE both appear in the surface layer,which are about 1.2-1.3 g/m~3 and 1.8 g/m~3,respectively,with the sub-maximum monthly RMSE appearing at about 2 km,which is about 1.6g/m~3.Secondly,comparing TPWV and slant water vapor(SWV)derived from WVD of the tomographic model over GNSS stations with that of ERA5,the tomographic results are more stable throughout the tomographic period and have less loss of WVC in the inversion,with the bias of both TPWV and SWV fluctuating around the value of 0 mm,and the maximum RMSEs are 5.6 mm and 5.4 mm,respectively,which improved by53%and 35%,respectively,over the ERA5 results.Finally,the four-dimensional spatial distribution of tomographic WVD shows that the overall trend of WVD decreases with increasing height,which is consistent with the variation of water vapor with height in the troposphere;the distribution of WVD in the horizontal plane has the characteristics of continuity and correlation,which is consistent with the actual continuous and smooth distribution.The above results show that the adaptive point cloud water vapor tomographic model can truly invert the spatial distribution of water vapor,and it can be used as a new GNSS water vapor tomographic model.The main foci of this research are the key technical issues of the high-precision inversion of water vapor spatial and temporal distribution information using ground-based GNSS and gives meaningful exploration results and practical parametric models by analyzing the characteristics of water vapor spatial and temporal distribution and its correlation factors,constructing a refined parametric model of water vapor vertical structure and water vapor tomographic model,which provides a theoretical basis and reference significance for the subsequent inversion of water vapor spatial distribution information from multi-source data and its application expansion.There are 30 figures,9 tables and 193 references in this thesis. |
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