Study On Tropospheric Wet Delay And Weighted Mean Temperature In GNSS

When the signal transmission of the global satellite navigation system(GNSS)travel through the Earth's atmosphere,zenith wet tropospheric delay(ZWD)is caused by the water vapor and should be removed or reduced.On the other hand,the estimated ZWD error from GNSS can be converted to precipitable water vapor(PWV),while the conversion process of ZWD-PWV requires a parameter,i.e.the weighted average temperature(T_m).ZWD and T_m are two parameters that are closely related to water vapor.Because of the characteristics of water vapor variability and randomness,studying the global ZWD and T_m are challenging in GNSS research compared to the regional ZWD and T_m.In this paper,ZWD and T_m on the global scale are studied.The main research contents and conclusions are as follows:(1)The ZWD model of the traditional neural network is established and its global accuracy is analyzed.Based on some previous research,a ZWD model(NN-ZWD model)is constructed by using the meteorological parameters,coordinate parameters and time parameters at the station.VMF1troposphere delay products at 130 sites on the global scale were used as the modeling data and the sounding data of 163 sites were used as the test data.But the results show that the NN-ZWD are not better than the traditional ZWD models(Saastamoinen model,the Hopfield model and the GPT2w model).(2)The NN-ZWD model was refined and the NN-ZWD-R model was proposed.The ZWD model based on neural network was refined.In order to improve the NN-ZWD model,the author takes into account the water vapor vertical variation characteristics and the introduction of moisture gradient factor(?)as model variables,will provide the moisture gradient factor GPT2w model(?-GPT2w)as approximation values.On the basis of this,the ZWD model(NN-ZWD-R model)based on neural network is proposed to discuss the accuracy of nn-zwd-r model worldwide.The results show that nn-zwd-r model is about 16.9%higher than NN-ZWD model.In addition,NN-ZWD-R model has a good applicability:it can maintain good accuracy at different latitudes,different heights and different seasons.(3)The relationship between T_m and the site meteorological parameters was also studied.T_m is related to the site meteorological parameters T_m is strongly related to the temperature(T_S)of the station.T_m is strongly related to the water vapor pressure(e_S)of the station,and the atmospheric pressure(P_S)of the station is almost irrelevant.The correlation characteristics of T_m-T_S and T_m-e_S show a certain geographical distribution pattern:their correlation increases roughly with the increase of latitude.(4)The variation characteristics of T_m time series in time domain were studied.Firstly,the periodic characteristics of spectrum analysis of T_m were studied by using the lomb-scargle periodogram.The annual cycle characteristics of T_m are found in 10 sample sites in different climatic regions around the world,which may be characterized by the semi-annual cycle,and the other cycle characteristics are not obvious.Again,the periodic terms,trend items and random items were used construct the Tm time series model.In tropical regions,the seasonal variation of T_m is often irregular;annul seasonal variation can be a major cyclical feature of T_m.In non-tropical regions,the seasonal variation of Tm shows certain rules:the change of seasons is mainly influenced by the characteristics of the year.In the southern hemisphere,the initial phase of the phase is about January and the annual phase of the northern hemisphere is generally in July.The maximum and minimum values of the T_m time series model are usually in summer and winter respectively.Globally,T_m has a long-term trend of around 0.22 K/decade growth,and the T_m trends are the most visible in high latitudes in the northern hemisphere.The high model error of T_m season model are found in mid-latitude and high latitudes,while the maximum errors of T_m time series model are in the polar region.(5)Two multi-parameter T_m models based on the neural network were firstly proposed.According to the research content(3)and(4),the author combined of the relationship between T_m and meteorological parameters of the station and T_m time series characteristics,and then the neural network model was used as a modeling tool for the first time.Thus,Two feedforward neural network(FFNN)models(the NN-1 and the NN-2)were established by a combination of the T_m seasonal variations and its relationship with surface meteorological elements.The NN-1 is used with measurements of both surface temperature T_S and surface vapor pressure e_S,while the NN-2 is only used with measurements of only T_S.Globally,the NN-1 model and NN-2 model have the same accuracy,and their accuracies are higher than that of 5 traditional models,such as GPT2w,BTm,GTm,GTm-I and PTm-I.Under the same conditions,the accuracy of the NN-1 model is about11.1%higher than that of the PTm-I model.The accuracy of NN-2 model is about 17.9%higher than that of GTm-I model.The NN-1 model and the NN-2 model have good applicability:in the long term,different seasons,different heights and different latitudes can maintain high precision.