Development Of Global Model For Atmospheric Weighted Mean Temperature Considering Spatiotemporal Factors

Water vapor is an important part of the lower atmosphere.More than 90%of the water vapor distribution in the bottom of the troposphere.Although it is a small proportion of the earth’s atmosphere,it is closely related to a variety of climate and weather phenomena.How to accurately detect the changes of water vapor in the atmosphere has attracted widespread attention.GNSS water vapor detection technology has become a research hotspot in recent years,which has the advantages of real-time,high-precision and high spatiotemporal resolution.However,the atmospheric weighted mean temperature(T_m)is the key parameter of GNSS tropospheric water vapor inversion,which can convert zenith wet delay(ZWD)into precipitable water vapor(PWV).In view of the shortcomings of existing atmospheric weighted mean temperature models,elevation,latitude and seasonal variation were not considered simultaneously,this paper combines the latest ERA5 atmospheric reanalysis data provided by the European Centre for Medium-Range Weather Forecasts(ECMWF)to construct a global high-precision and high spatiotemporal resolution model for atmospheric weighted mean temperature considering spatiotemporal factors.The main research content and work of this paper are as follows:1.An integral method is proposed to calculate the starting height from the height of the user such as the radiosonde station.At the same time,in order to evaluate the performance of T_m derived from MERRA-2 and ERA5 atmospheric reanalysis data using the T_m data of 545 radiosonde stations in 2017.The results showed that the annual bias of T_m calculated from MERRA-2 and ERA5 reanalysis data were-0.10 K and 0.13 K,respectively,and the annual RMSE were 1.23 K and 1.09 K,respectively.Therefore,the accuracy of T_m derived from the ERA5 reanalysis data slightly better than the MERRA-2 reanalysis data,which can be used as a data source for global T_m model construction.2.On the basis of analyzing the spatiotemporal variation characteristics of global T_m lapse rate,a global multi-dimensional T_m lapse rate model(NGGTm-H model)is constructed by introducing the sliding window algorithm.At the same time,the application accuracy of NGGTm-H model in vertical interpolation and spatial interpolation was tested and analyzed by combining the T_m data of 378 radiosonde stations in 2017 and the grid T_m data calculated from the ERA5 reanalysis data not involved into modeling in 2017.The results showed that NGGTm-H models with three resolutions exhibit high accuracy and stability in global vertical interpolation and spatial interpolation,especially in low and middle latitudes and ocean regions.3.Based on the construction of T_m lapse rate model,the diurnal variation characteristics of T_m and its relationship with altitude and latitude were explored,and finally a high-precision global T_m model considering spatiotemporal factors(NGGTm model)was constructed.At the same time,the accuracy of NGGTm model was tested by combining the T_m data of 378 radiosonde stations in 2017 and the surface grid T_m data calculated from the ERA5 reanalysis data,and compared with Bevis and GPT3 models.The results showed that:(1)Using the surface grid T_m data of ERA5 as reference value,the mean RMSE of NGGTm model was only 2.84 K,which was 0.50 K,0.18 K and 0.06 K higher than that of Bevis,GPT3-5 and GPT3-1 models,respectively.(2)The T_m data of radiosonde station are taken as reference value,the mean bias and RMSE of NGGTm model were 0.10 K and 3.30 K,respectively,which exhibited the best accuracy and stability compared with other models.Therefore,NGGTm model can be applied to high-precision real-time GNSS water vapor monitoring.