Research On Inversion Method And Its Application Of GNSS-Based Tropospheric Water Vapor

Tropospheric water vapor is the main driving source of the formation and change of extreme weather.Understanding and mastering the spatiotemporal changes of tropospheric water vapor is of great significance for disaster warning.The traditional water vapor detection methods mainly include radiosonde,microwave radiometer,laser radar and satellite infrared radiometer,etc.,but the traditional water vapor detection methods have their own limitations in time and space.With the rapid development of Global Navigation Satellite Systems(GNSS),Many countries build Continuously Operating Reference Stations(CORS)networks,which provide rich data sources for GNSS tropospheric water vapor remote sensing.Compared with traditional observation methods,GNSS tropospheric water vapor remote sensing technology has the advantages of all-weather,all-time,high precision and low cost,and is one of the important means of water vapor monitoring.GNSS water vapor remote sensing technology is mainly divided into atmospheric precipitable water inversion and water vapor chromatography.At present,the accuracy of two-dimensional tropospheric water vapor inversion and threedimensional tropospheric water vapor chromatography is generally not high,which cannot meet the accuracy requirements of water vapor application.In order to improve the accuracy of GNSS moisture inversion and tomographic results,the paper researches on Tropospheric Weight mean temperature(Tm),measured meteorological parameters and improvement of tomographic algorithm.The main research achievements of this paper are as follows:(1)Based on the sonde data from 2013 to 2019 in China,this paper analyzes the influence of temporal and spatial parameters on Tm,and constructs a high-precision Tm model in China with consideration of temporal and spatial parameters with the advantage of BP neural network in nonlinear fitting.The model has both the range of use and the accuracy.The sonde data test shows that the Mean deviation(Bias)and Root Mean Square Error(RMSE)of the newly constructed model are 0.24 K and 2.60 K,which are better than the traditional model.(2)In view of the low accuracy of tropospheric Water Vapor inversion,the atmospheric Precipitable Water Vapor(PWV)was calculated using the meteorological parameters provided by GPT2 w model and the measured meteorological parameters,and the sonde data were used to test the accuracy of the calculated results.The test results show that the accuracy of PWV calculated based on measured meteorological parameters is better than that calculated using GPT2 w meteorological parameters.(3)An iterative algorithm based on robust estimation is proposed in this paper.The new algorithm can optimize the observed data of water vapor chromatography and improve the accuracy of tropospheric water vapor chromatography inversion.The experimental results show that:(a)Adaptive Simultaneous Iterative Reconstruction Technique for the same set of data,The results of ASIRT are better than those of the Adaptive Algebraic Reconstruction Technique(AART).The main reason is that AART algorithm will be affected by the sequence of data,so the quality of the results is not good.(b)In the iterative algorithm of fusion robust estimation,the results of the adaptive iterative algorithm of robust estimation are superior to those of the adaptive iterative algorithm due to its screening of the tomographic data.Because the tomographic results are susceptible to the influence of the number of rays,the spatial distribution of rays and the tomographic algorithm,the enhancement rates of each algorithm in each system are different.