| Water vapor plays a very important role in the global energy balance and water cycle.It is also closely related to various meteorological and climatic disasters.Precipitable Water Vapor(PWV)reflects the change of water vapor content in the atmosphere,and has become an important monitoring object in the field of meteorology and climate.However,due to observing costs,technology and other reasons,it is difficult for traditional atmospheric detection methods to capture the temporal and spatial characteristics of water vapor.In recent years,with the development of Global Navigation Satellite System(GNSS)technology,ground-based GNSS has become the fastest growing and most promising new detection method in the field of meteorology.This paper focuses on scientific research on PWV,learns the theoretical knowledge of PWV inversion through GNSS technology,and studies the modeling of weighted mean temperature(),which is the key parameter in the PWV inversion process,and establishes the regional model in Shaanxi region and in China.Finally,based on the longsequence PWV products provided by ECMWF(European Centre for Medium-Range Weather Forecasts,ECMWF)combined with ground weather station data,the climate change characteristics of the Mu Us Sandy Land under the background of global warming are analyzed.This paper lays the foundation for the expansion of GNSS technology in the fields of weather forecasting and climate monitoring.The main research results and innovations of this paper are as follows:(1)This article first analyzes the correlation between common climate factors and in view of the existing problems of the model that requires measured meteorological parameters,using the reanalysis data provided by ECMWF and the data of three sounding stations,based on the principle of least squares A regression model of regionalization was established.And to solve the problem of the accuracy difference of the regression model in different climate types,a model that takes into account the climate difference is established,which solves the problem of the adaptability of the regression model in different climate regions.The results show that the area model established in this paper has an average improvement rate of 44.9% compared with the traditional Bevis model.(2)Taking into account the limitations of the model that requires measured meteorological parameters in practical applications,a large-scale,high-precision Chinese regional model(CTm-h)that does not require prior information is established.Aiming at the problem of low accuracy of linear vertical decline rate fitting profile,a method of combining linear function and Fourier function is proposed,which greatly improves the fitting accuracy of in the elevation direction.Therefore,it is more in line with the Characteristics of temperature variation with elevation changes.The experimental results show that the CTmh model can achieve higher accuracy in the entire Chinese region,and compared with the GTP2 w model with the highest nominal accuracy in the world,the overall accuracy of the CTmh model in the Chinese region has increased by 26.8%.The estimation accuracy of the profile can be significantly improved.(3)Using the meteorological data provided by the ground weather station and ECMWF in the Mu Us dune field and surrounding areas,the time series and spatial distribution characteristics of temperature,precipitation and PWV in the Mu Us dune field were analyzed and discussed.Through principal component analysis of temperature,precipitation,and PWV,the temporal and spatial characteristics of temperature,precipitation,and PWV changes in the Mu Us dune field were initially determined.By analyzing its time series,the cycle of each climate factor is determined,and their seasonal and interannual changes are described.Combining the DEM and river distribution information of the study area,the spatial distribution of temperature,precipitation,and PWV,as well as the spatial variation of temperature,precipitation,and PWV in different time periods,are analyzed.The analysis shows that the Mu Us dune field has become more sensitive to global warming in recent years,and there is a trend of "warming and dampness".This long-term trend is largely driven by changes in the regional ecological environment and global warming. |
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