Analysis of atmospheric weighted mean temperature models based on radiosonde observations over Iran

Document Type : Research Article

Authors

Department of Surveying Engineering, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran.

Abstract

The Global Navigation Satellite System (GNSS) has been in existence for several decades, serving the purpose of position determination and finding applications in navigation, military operations, mapping, and earth sciences. Nevertheless, for almost thirty years, this system has also been utilized in meteorology, leading to the establishment of a field known as GNSS metrology. Signals transmitted from GNSS satellites must pass through all layers of the atmosphere to reach receivers located on the ground or at higher altitudes. When passing through these layers, the signals interact with the components of the atmosphere, which changes the speed of propagation of the signals and, as a result, their arrival at the receiver is delayed. This phenomenon, known as total signal delay, is mainly caused by the troposphere and ionosphere layers. Meanwhile, the ionospheric delay, which is caused by the presence of ions and free electrons in this layer, can be eliminated by using observational techniques such as combining observations of the two frequencies L1 and L2.When processing GNSS observations, the tropospheric delay can be estimated along with the coordinate unknowns. This delay is divided into two main parts: the hydrostatic delay and the wet delay, the latter of which is related to the water vapor in the atmosphere. In the process of retrieving precipitable water vapor from the total tropospheric delay using GNSS meteorology, the atmospheric weighted mean temperature (Tm) plays an important role. Bevis et al. (1992) created a mapping model designed to transform wet delay (ZWD) into precipitable water vapor (PWV). This mapping model includes various physical coefficients and Tm to calculate ZWD based on PWV. There are several methods for calculating Tm, among which the use of actual observations of radiosonde atmospheric profiles is considered as the basic method. However, limitations such as very low temporal resolution and poor spatial resolution in most regions of the world, especially in a vast country like Iran, have necessitated the need for an alternative model in the absence of radiosonde observations. Since accurate estimation of Tm requires vertical profiles of atmospheric temperature and humidity, several empirical regional and global models have been developed to estimate it to date. The objective of this research is to assess the precision of several recent global and regional Tm models in Iran, as well as to present a model founded on the SVM algorithm aimed at enhancing the accuracy of Tm estimation within the study area. To develop the model, observations from 11 radiosonde stations collected between 2015 and 2023 were utilized. For evaluation, Tm values derived from data from these stations in 2024 were employed to assess the proposed model alongside other existing models. The results indicated that the proposed model achieved reductions in RMSE of 3.25, 2.43, 1.00, 1.02, 0.58, 0.61, and 0.61 °C compared with the seven selected models: Bevis, hgpt2, gpt2w, gpt3, GTrop, GGNTm, and Rahimi, respectively. Following the  model, the GTrop, GGNTm, and Rahimi models demonstrated, on average, better performance than the other models within the study area. Furthermore, the proposed model was evaluated against other models under rainy conditions, and statistical analyses confirmed that it outperformed the alternatives.

Keywords

Main Subjects

References

Alber, C., Ware, R., Rocken, C & Braun, J. (2000). Obtaining single path phase delays from GPS double differences. Geophysical Research Letters, 27, 2661–2664.
Allan, RP. (2012). The Role of Water Vapor in Earth’s Energy Flows. Surv Geophys, 33, 557–564.
Sharifi M. A., Sam Khaniani, A. & Joghataei, M. (2015). Comparison of GPS precipitable water vapor and meteorological parameters during rainfalls in Tehran. Meteorology and Atmospheric Physics, 127(6), 701-710.
Abdelfatah, M.A., Elhady, S. R., Mousa, A. & El‑Fiky, G.S. (2024). A new Egyptian Grid Weighted Mean Temperature (EGWMT) model using hourly ERA5 reanalysis data in GNSS PWV retrieval. Scientific Reports, 14(1), 14608.
Boudouris, G. (1963). On the index of refraction of air, the absorption and dispersion of centimeter waves by gases. Journal of Research of the National Bureau of Standards, 67D, 631-684.
Bolton, D. (1980). The computation of equivalent potential temperature. Monthly Weather Review, 108(7), 1046–1053.
Beria, H., Nanda, T., Singh Bisht, D & Chatterjee, C. (2017). Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale. Hydrol Earth Syst Sci, 21, 6117–6134.
Bevis, M., Businger, S., Herring, T.A., Rocken, C., Anthes, R.A & Ware, R.H. (1992). GPS meteorology: remote sensing of the atmospheric water vapor using the global positioning system. Journal of Geophysical Research, 97(D14), 15787–15801.
Bevis, M., Chiswell, S., Herring, TA., Anthes, RA., Rocken, C .& Ware, R.H. (1994). GPS meteorology: mapping zenith wet delays onto precipitable water. J Appl Meteor, 33(3), 379-386.
Braun, J., Rocken, C. & Ware, R. (2001). Validation of line-of-sight water vapor measurements with GPS. Radio Science, 36, 459–472.
Boehm, J., Moeller, G., Schindelegger, M., Pain, G & Weber, R. (2015). Development of an improved empirical model for slant delays in the troposphere (GPT2w). GPS Solutions, 19(3), 433-441.
Davis, J.L., Herring, T.A., Shapiro, I.I., Rogers, A.E.E & Elgered, G. (1985). Geodesy by Radio Interferometry: Effects of Atmospheric Modeling Errors on Estimates of Baseline Length. Radio Science, 20, 1593–1607.
Rahimi, H., Asgari, J & Nafisi, V. (2022). Local
modeling of weighted mean temperature in Iran
and its impact on GNSS meteorology. Acta Geophysica, 70(3), 1445-1454.
He, C., Wu, S., Wang, X., Hu, A., Wang, Q & Zhang, K. (2017). A new voxel-based model for the determination 669 of atmospheric weighted mean temperature in GPS atmospheric sounding. Atmospheric Measurement Techniques, 10(6), 2045-2060
Hollander, M. & Wolfe, D.A. (1973). Nonparametric statistical methods. John Wiley & Sons, New York-Sydney-Tokyo-Mexico City.
Hopfield, H.S. (1971). Tropospheric effect on electromagnetically measured range prediction from surface weather data. Radio Science, 6, 357–367.
Huang, L., Jiang, W., Liu, L., Chen, H & Ye, S. (2018). A new global grid model for the determination of atmospheric weighted mean temperature in GPS precipitable water vapor. Journal of Geodesy, 93, 159–176.
Huang, L., Liu, L., Chen, H & Jiang, W. (2019). An improved atmospheric weighted mean temperature model and its impact on GNSS precipitable water vapor estimates for China. GPS Solutions, 23(2), 51.
Huang, L., Li, H., Li, J., Liu, L., Zhao, Q & Zhou, L. (2022). Random Forest-Based Model for Estimating Weighted Mean Temperature in Mainland China. Atmosphere, 13, 1368.
Kouba, J. & Héroux, P. (2001). Precise point positioning using IGS orbit and clock products. GPS Solutions, 5, 12–28.
Landskron, D. & Boehm, J. (2017). VMF3/GPT3: Refined discrete and empirical troposphere mapping functions. Journal of Geodesy, 92(3), 349-360.
Li, XX., Dick, G., Lu, CX., Ge, MR., Nilsson, T., Ning, T., Wickert, J & Schuh, H. (2015). Multi-GNSS Meteorology: Real-Time Retrieving of Atmospheric Water Vapor from Beidou, Galileo, GLONASS and GPS Observations. IEEE Trans Geosci Remote Sens, 53, 6385–6393.
Li, L., Li, Y., He, Q. & Wang, X. (2022). Weighted Mean Temperature Modelling Using Regional Radiosonde Observations for the Yangtze River Delta Region in China. Remote Sens, 14, 1909.
Li, K., Li, L., Hu, A., Pan, J., Ma, Y & Zhang, M. (2023). Research on Modeling Weighted Average Temperature Based on the Machine Learning Algorithms. Atmosphere, 14, 1251.
Murray, FW. (1967). On the computation of saturation vapor pressure. J. Appl Meteor, 6, 203-204.
Mateus, P., B. Mendes, V., Catalao, J. & Nico, G. (2020). An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model. Remote Sens, 12(7), 1098.
Mateus, P., B. Mendes, V. & Plecha, S. (2021). HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity. Remote Sens, 13(11), 2179.
Ma, Y., Zhao, Q., Wu, K., Yao, W., Liu, Y., Li, Z. & Shi, Y. (2022). Comprehensive Analysis and Validation of the Atmospheric Weighted Mean Temperature Models in China. Remote Sens, 14, 3435.
Niell, A.E. (1996). Global mapping functions for the atmosphere delay at radio wavelengths. J Geophys Res 101, 3227–3246.
Smith, E.K & Weintraub, S. (1953). The Constants in the Equation for Atmospheric Refractive Index at Radio Frequencies. Proceedings of the IRE, 41, 1035-1037.
Saastamoinen, J. (1972). Atmospheric correction for the Troposphere and stratosphere in radio ranging of satellites in the use of artificial satellites for geodesy. Geophysics Monoger, 15, 247–251.
Snoek, J., Larochelle, H & Adams, R.P. (2012) Practical Bayesian Optimization of Machine Learning Algorithms, Advances in Neural Information Processing Systems.
Sun, Z., Zhang, B. & Yao, Y. (2019). A Global Model for Estimating Tropospheric Delay and Weighted Mean Temperature Developed with Atmospheric Reanalysis Data from 1979 to 2017. Remote Sens, 11, 1893.
Cortes, C. & Vapnik, V. (1995). Support-vector networks. Mach Learn, 20, 273–297.
Vapnik, V. (1999). The nature of statistical learning theory. Springer, Berlin.
Wang, X., Li, L., Li, Y & He, Q. (2022). Weighted Mean Temperature Modelling Using Regional Radiosonde Observations for the Yangtze River Delta Region in China. Remote Sens.
Zhang, B., Wang, Z., Li, W., Jiang, W., Shen, Y., Zhang, Y., Zhang, S & Tian, K. (2022). An Improved Spatiotemporal Weighted Mean Temperature Model over Europe Based on the Nonlinear Least Squares Estimation Method. Remote Sens, 14, 3609.
Zumberge, J.F., Heflin, M.B & Jefferson, D.C. (1997). Precise point positioning for the efficient and robust analysis of GPS data from large networks. Journal of Geophysical Research, 102(B3), 5005–5017.
Journal of the Earth and Space Physics
Volume 52, Issue 1
online publication date: 30 May 2026
May 2026
Pages 121-137

Files

Share

How to cite

Statistics