Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China

Abstract

Atmospheric weighted mean temperature (Tm) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical Tm models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal Tm model for PWV retrieval in China, this paper analyzes and evaluates some typical Tm models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the Tm model for China (CTm), the Global Weighted Mean Temperature-H (GTm-H) and the Global Tropospheric (GTrop) models. The Tm values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding Tm values of 87 RS stations in China are also calculated using the layered meteorological data and regarded as the reference. Comparison results show that the accuracy of these five Tm models in China has an obvious geographical distribution and decreases along with increasing altitude and latitude, respectively. The average root mean square (RMS) and Bias for the Linear, GPT3, CTm, GTm-H and GTrop models are 4.2/3.7/3.4/3.6/3.3 K and 0.7/−1.0/0.7/−0.1/0.3 K, respectively. Among these models, Linear and GPT3 models have lower accuracy in high-altitude regions, whereas CTm, GTm-H and GTrop models show better accuracy and stability throughout the whole China. These models generally have higher accuracy in regions with low latitude and lower accuracy in regions with middle and high latitudes. In addition, Linear and GPT3 models have poor accuracy in general, whereas GTm-H and CTm models are obviously less accurate and stable than GTrop model in regions with high latitude. These models show different accuracies across the four geographical regions of China, with GTrop model demonstrating the relatively better accuracy and stability. Therefore, the GTrop model is recommended to obtain Tm for calculating PWV in China.

Machine Learning-Based Estimation of Hourly GNSS Precipitable Water Vapour

“…The presence of outliers in the measurements is inevitable and can negatively affect the performance and accuracy of a model if they are not addressed appropriately [30,[57][58][59]. In data pre-processing, the Interquartile Range (IQR) method is widely used to detect outliers.…”

“…As already stated, T m is a crucial parameter in PWV retrieval. In general, T m modelling methods can be categorized into surface meteorological factor models and nonmeteorological factor models based on the use of surface meteorological data [30,31]. In the first method, a linear or nonlinear model of T m is developed based on the relationships between this parameter and surface meteorological variables, such as surface temperature (T s ), pressure (P s ), and water vapour pressure (e s ) [31].…”

“…In addition, this technique has also been used in numerous studies to obtain a more accurate global or local model of T m [19,[37][38][39][40][41]. For the second method, instead of using in situ meteorological data, other variables such as geographical location and time are taken into account to model T m [30,31,42]. There have been a number of empirical models developed in recent years, including different models of Global Pressure and Temperature (GPT) [43][44][45], Global Weighted Mean Temperature [46,47], Global Tropospheric Model [48], Hourly Global Pressure and Temperature model [49], GTm_R [34] model, and the Global Weighted Mean Temperature model [50].…”

An Optimized Framework for Precipitable Water Vapor Mapping Using TS-InSAR and GNSS

“…Due to regional differences, applying a water vapor inversion based on data from global regions can introduce regional model system errors, leading to suboptimal atmospheric water vapor estimations. Therefore, many researchers use local meteorological data from radiosonde stations and statistical analysis methods to develop more region-specific weighted average temperature calculation models [30]. However, meteorological data from radiosonde stations are not spatially continuous, making it impossible to obtain spatially continuous weighted average temperatures.…”