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

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.

[1]  Qimin He,et al.  Weighted Mean Temperature Modelling Using Regional Radiosonde Observations for the Yangtze River Delta Region in China , 2022, Remote. Sens..

[2]  A. Bărbulescu,et al.  Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series , 2022, Atmosphere.

[3]  Guanwen Huang,et al.  A Weighted Mean Temperature Model with Nonlinear Elevation Correction Using China as an Example , 2021, Remote. Sens..

[4]  Wenliang Gao,et al.  A Novel Modeling Strategy of Weighted Mean Temperature in China Using RNN and LSTM , 2021, Remote. Sens..

[5]  Yuxiang Yan,et al.  Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature , 2021, Remote. Sens..

[6]  Weiping Jiang,et al.  A global grid model for the correction of the vertical zenith total delay based on a sliding window algorithm , 2021, GPS Solutions.

[7]  O. Reitebuch,et al.  Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents , 2021 .

[8]  Ruizhi Chen,et al.  Refining the empirical global pressure and temperature model with the ERA5 reanalysis and radiosonde data , 2021, Journal of Geodesy.

[9]  H. Lim,et al.  Variability and Trend in Integrated Water Vapour from ERA-Interim and IGRA2 Observations over Peninsular Malaysia , 2020, Atmosphere.

[10]  Junping Chen,et al.  Assessment of Empirical Troposphere Model GPT3 Based on NGL’s Global Troposphere Products , 2020, Sensors.

[11]  Yibin Yao,et al.  A Refined Regional Model for Estimating Pressure, Temperature, and Water Vapor Pressure for Geodetic Applications in China , 2020, Remote. Sens..

[12]  A. Favre,et al.  Contrasting Floristic Diversity of the Hengduan Mountains, the Himalayas and the Qinghai-Tibet Plateau Sensu Stricto in China , 2020, Frontiers in Ecology and Evolution.

[13]  M. Ding A second generation of the neural network model for predicting weighted mean temperature , 2020, GPS Solutions.

[14]  Yibin Yao,et al.  Applicability of Bevis Formula at Different Height Levels and Global Weighted Mean Temperature Model Based on Near-earth Atmospheric Temperature , 2020 .

[15]  Xiaolin Meng,et al.  An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate , 2020, GPS Solutions.

[16]  Jakub Nowosad,et al.  Climate: An R Package to Access Free In-Situ Meteorological and Hydrological Datasets For Environmental Assessment , 2020, Sustainability.

[17]  Jong Chul Kim,et al.  Calibration of a radiosonde humidity sensor at low temperature and low pressure , 2019, Metrologia.

[18]  Yibin Yao,et al.  A Global Model for Estimating Tropospheric Delay and Weighted Mean Temperature Developed with Atmospheric Reanalysis Data from 1979 to 2017 , 2019, Remote. Sens..

[19]  Weiping Jiang,et al.  An improved atmospheric weighted mean temperature model and its impact on GNSS precipitable water vapor estimates for China , 2019, GPS Solutions.

[20]  Shirong Ye,et al.  A new global grid model for the determination of atmospheric weighted mean temperature in GPS precipitable water vapor , 2019, Journal of Geodesy.

[21]  Jong Chul Kim,et al.  Evaluation of radiosonde humidity sensors at low temperature using ultralow-temperature humidity chamber , 2018, Advances in Science and Research.

[22]  V M van Zoest,et al.  Outlier Detection in Urban Air Quality Sensor Networks , 2018, Water, Air, & Soil Pollution.

[23]  Maohua Ding,et al.  A neural network model for predicting weighted mean temperature , 2018, Journal of Geodesy.

[24]  M. Ding,et al.  A further contribution to the seasonal variation of weighted mean temperature , 2017 .

[25]  Johannes Böhm,et al.  VMF3/GPT3: refined discrete and empirical troposphere mapping functions , 2017, Journal of Geodesy.

[26]  Robert Weber,et al.  Development of an improved empirical model for slant delays in the troposphere (GPT2w) , 2015, GPS Solutions.

[27]  Yibin Yao,et al.  GTm-III: a new global empirical model for mapping zenith wet delays onto precipitable water vapour , 2014 .

[28]  F. Yan,et al.  Improved one/multi-parameter models that consider seasonal and geographic variations for estimating weighted mean temperature in ground-based GPS meteorology , 2014, Journal of Geodesy.

[29]  T. Nilsson,et al.  GPT2: Empirical slant delay model for radio space geodetic techniques , 2013, Geophysical research letters.

[30]  Yibin Yao,et al.  Global empirical model for mapping zenith wet delays onto precipitable water , 2013, Journal of Geodesy.

[31]  H. Schuh,et al.  Short Note: A global model of pressure and temperature for geodetic applications , 2007 .